trieste.acquisition.function.multi_objective#

This module contains multi-objective acquisition function builders.

Module Contents#

class ExpectedHypervolumeImprovement(reference_point_spec: Sequence[float] | trieste.types.TensorType | Callable[Ellipsis, trieste.types.TensorType] = get_reference_point)[source]#

Bases: trieste.acquisition.interface.SingleModelAcquisitionBuilder[trieste.models.ProbabilisticModel]

Builder for the expected hypervolume improvement acquisition function. The implementation of the acquisition function largely follows [YEDBack19]

Parameters:

reference_point_spec – this method is used to determine how the reference point is calculated. If a Callable function specified, it is expected to take existing posterior mean-based observations (to screen out the observation noise) and return a reference point with shape [D] (D represents number of objectives). If the Pareto front location is known, this arg can be used to specify a fixed reference point in each bo iteration. A dynamic reference point updating strategy is used by default to set a reference point according to the datasets.

prepare_acquisition_function(model: trieste.models.ProbabilisticModel, dataset: trieste.data.Dataset | None = None) → trieste.acquisition.interface.AcquisitionFunction[source]#
Parameters:

  • model – The model.

  • dataset – The data from the observer. Must be populated.

Returns:

The expected hypervolume improvement acquisition function.

update_acquisition_function(function: trieste.acquisition.interface.AcquisitionFunction, model: trieste.models.ProbabilisticModel, dataset: trieste.data.Dataset | None = None) → trieste.acquisition.interface.AcquisitionFunction[source]#
Parameters:

  • function – The acquisition function to update.

  • model – The model.

  • dataset – The data from the observer. Must be populated.

class expected_hv_improvement(model: trieste.models.ProbabilisticModel, partition_bounds: tuple[trieste.types.TensorType, trieste.types.TensorType])[source]#

Bases: trieste.acquisition.interface.AcquisitionFunctionClass

An AcquisitionFunctionClass is an acquisition function represented using a class rather than as a standalone function. Using a class to represent an acquisition function makes it easier to update it, to avoid having to retrace the function on every call.

expected Hyper-volume (HV) calculating using Eq. 44 of [YEDBack19] paper. The expected hypervolume improvement calculation in the non-dominated region can be decomposed into sub-calculations based on each partitioned cell. For easier calculation, this sub-calculation can be reformulated as a combination of two generalized expected improvements, corresponding to Psi (Eq. 44) and Nu (Eq. 45) function calculations, respectively.

Note: 1. Since in Trieste we do not assume the use of a certain non-dominated region partition algorithm, we do not assume the last dimension of the partitioned cell has only one (lower) bound (i.e., minus infinity, which is used in the [YEDBack19] paper). This is not as efficient as the original paper, but is applicable to different non-dominated partition algorithm. 2. As the Psi and nu function in the original paper are defined for maximization problems, we inverse our minimisation problem (to also be a maximisation), allowing use of the original notation and equations.

Parameters:

  • model – The model of the objective function.

  • partition_bounds – with shape ([N, D], [N, D]), partitioned non-dominated hypercell bounds for hypervolume improvement calculation

Returns:

The expected_hv_improvement acquisition function modified for objective minimisation. This function will raise ValueError or InvalidArgumentError if used with a batch size greater than one.

update(partition_bounds: tuple[trieste.types.TensorType, trieste.types.TensorType]) → None[source]#

Update the acquisition function with new partition bounds.

__call__(x: trieste.types.TensorType) → trieste.types.TensorType[source]#

Call acquisition function.

class BatchMonteCarloExpectedHypervolumeImprovement(sample_size: int, reference_point_spec: Sequence[float] | trieste.types.TensorType | Callable[Ellipsis, trieste.types.TensorType] = get_reference_point, *, jitter: float = DEFAULTS.JITTER)[source]#

Bases: trieste.acquisition.interface.SingleModelAcquisitionBuilder[trieste.models.interfaces.HasReparamSampler]

Builder for the batch expected hypervolume improvement acquisition function. The implementation of the acquisition function largely follows [DBB20]

Parameters:

  • sample_size – The number of samples from model predicted distribution for each batch of points.

  • reference_point_spec – this method is used to determine how the reference point is calculated. If a Callable function specified, it is expected to take existing posterior mean-based observations (to screen out the observation noise) and return a reference point with shape [D] (D represents number of objectives). If the Pareto front location is known, this arg can be used to specify a fixed reference point in each bo iteration. A dynamic reference point updating strategy is used by default to set a reference point according to the datasets.

  • jitter – The size of the jitter to use when stabilising the Cholesky decomposition of the covariance matrix.

Raises:

ValueError (or InvalidArgumentError) – If sample_size is not positive, or jitter is negative.

prepare_acquisition_function(model: trieste.models.interfaces.HasReparamSampler, dataset: trieste.data.Dataset | None = None) → trieste.acquisition.interface.AcquisitionFunction[source]#
Parameters:

  • model – The model. Must have event shape [1].

  • dataset – The data from the observer. Must be populated.

Returns:

The batch expected hypervolume improvement acquisition function.

batch_ehvi(sampler: trieste.models.ReparametrizationSampler[trieste.models.interfaces.HasReparamSampler], sampler_jitter: float, partition_bounds: tuple[trieste.types.TensorType, trieste.types.TensorType]) → trieste.acquisition.interface.AcquisitionFunction[source]#
Parameters:

  • sampler – The posterior sampler, which given query points at, is able to sample the possible observations at ‘at’.

  • sampler_jitter – The size of the jitter to use in sampler when stabilising the Cholesky decomposition of the covariance matrix.

  • partition_bounds – with shape ([N, D], [N, D]), partitioned non-dominated hypercell bounds for hypervolume improvement calculation

Returns:

The batch expected hypervolume improvement acquisition function for objective minimisation.

class ExpectedConstrainedHypervolumeImprovement(objective_tag: trieste.types.Tag, constraint_builder: trieste.acquisition.interface.AcquisitionFunctionBuilder[trieste.acquisition.interface.ProbabilisticModelType], min_feasibility_probability: float | trieste.types.TensorType = 0.5, reference_point_spec: Sequence[float] | trieste.types.TensorType | Callable[Ellipsis, trieste.types.TensorType] = get_reference_point)[source]#

Bases: trieste.acquisition.function.function.ExpectedConstrainedImprovement[trieste.acquisition.interface.ProbabilisticModelType]

Builder for the constrained expected hypervolume improvement acquisition function. This function essentially combines ExpectedConstrainedImprovement and ExpectedHypervolumeImprovement.

Parameters:

  • objective_tag – The tag for the objective data and model.

  • constraint_builder – The builder for the constraint function.

  • min_feasibility_probability – The minimum probability of feasibility for a “best point” to be considered feasible.

  • reference_point_spec – this method is used to determine how the reference point is calculated. If a Callable function specified, it is expected to take existing posterior mean-based feasible observations (to screen out the observation noise) and return a reference point with shape [D] (D represents number of objectives). If the feasible Pareto front location is known, this arg can be used to specify a fixed reference point in each bo iteration. A dynamic reference point updating strategy is used by default to set a reference point according to the datasets.

_update_expected_improvement_fn(objective_model: trieste.acquisition.interface.ProbabilisticModelType, feasible_mean: trieste.types.TensorType) → None[source]#

Set or update the unconstrained expected improvement function.

Parameters:

  • objective_model – The objective model.

  • feasible_mean – The mean of the feasible query points.

class HIPPO(objective_tag: trieste.types.Tag = OBJECTIVE, base_acquisition_function_builder: trieste.acquisition.interface.AcquisitionFunctionBuilder[trieste.acquisition.interface.ProbabilisticModelType] | trieste.acquisition.interface.SingleModelAcquisitionBuilder[trieste.acquisition.interface.ProbabilisticModelType] | None = None)[source]#

Bases: trieste.acquisition.interface.GreedyAcquisitionFunctionBuilder[trieste.acquisition.interface.ProbabilisticModelType]

HIPPO: HIghly Parallelizable Pareto Optimization

Builder of the acquisition function for greedily collecting batches by HIPPO penalization in multi-objective optimization by penalizing batch points by their distance in the objective space. The resulting acquistion function takes in a set of pending points and returns a base multi-objective acquisition function penalized around those points.

Penalization is applied to the acquisition function multiplicatively. However, to improve numerical stability, we perform additive penalization in a log space.

Initializes the HIPPO acquisition function builder.

Parameters:

  • objective_tag – The tag for the objective data and model.

  • base_acquisition_function_builder – Base acquisition function to be penalized. Defaults to Expected Hypervolume Improvement, also supports its constrained version.

prepare_acquisition_function(models: Mapping[trieste.types.Tag, trieste.acquisition.interface.ProbabilisticModelType], datasets: Mapping[trieste.types.Tag, trieste.data.Dataset] | None = None, pending_points: trieste.types.TensorType | None = None) → trieste.acquisition.interface.AcquisitionFunction[source]#

Creates a new instance of the acquisition function.

Parameters:

  • models – The models.

  • datasets – The data from the observer. Must be populated.

  • pending_points – The points we penalize with respect to.

Returns:

The HIPPO acquisition function.

Raises:

tf.errors.InvalidArgumentError – If the dataset is empty.

update_acquisition_function(function: trieste.acquisition.interface.AcquisitionFunction, models: Mapping[trieste.types.Tag, trieste.acquisition.interface.ProbabilisticModelType], datasets: Mapping[trieste.types.Tag, trieste.data.Dataset] | None = None, pending_points: trieste.types.TensorType | None = None, new_optimization_step: bool = True) → trieste.acquisition.interface.AcquisitionFunction[source]#

Updates the acquisition function.

Parameters:

  • function – The acquisition function to update.

  • models – The models.

  • datasets – The data from the observer. Must be populated.

  • pending_points – Points already chosen to be in the current batch (of shape [M,D]), where M is the number of pending points and D is the search space dimension.

  • new_optimization_step – Indicates whether this call to update_acquisition_function is to start of a new optimization step, of to continue collecting batch of points for the current step. Defaults to True.

Returns:

The updated acquisition function.

class hippo_penalizer(model: trieste.models.ProbabilisticModel, pending_points: trieste.types.TensorType)[source]#

Returns the penalization function used for multi-objective greedy batch Bayesian optimization.

A candidate point \(x\) is penalized based on the Mahalanobis distance to a given pending point \(p_i\). Since we assume objectives to be independent, the Mahalanobis distance between these points becomes a Eucledian distance normalized by standard deviation. Penalties for multiple pending points are multiplied, and the resulting quantity is warped with the arctan function to \([0, 1]\) interval.

Parameters:

  • model – The model over the specified dataset.

  • pending_points – The points we penalize with respect to.

Returns:

The penalization function. This function will raise ValueError or InvalidArgumentError if used with a batch size greater than one.

Initialize the MO penalizer.

Parameters:

  • model – The model.

  • pending_points – The points we penalize with respect to.

Raises:

ValueError – If pending points are empty or None.

Returns:

The penalization function. This function will raise ValueError or InvalidArgumentError if used with a batch size greater than one.

update(pending_points: trieste.types.TensorType) → None[source]#

Update the penalizer with new pending points.