trieste.acquisition.multi_objective.pareto#

This module contains functions and classes for Pareto based multi-objective optimization.

Module Contents#

class Pareto(observations: trieste.types.TensorType, already_non_dominated: bool = False)[source]#

A Pareto constructs a Pareto set. Stores a Pareto set and calculates hypervolume of the Pareto set given a specified reference point

Parameters:

  • observations – The observations for all objectives, with shape [N, D].

  • already_non_dominated – Whether the observations are already non dominated

Raises:

ValueError (or InvalidArgumentError) – If observations has an invalid shape.

hypervolume_indicator(reference: trieste.types.TensorType) → trieste.types.TensorType[source]#

Calculate the hypervolume indicator based on self.front and a reference point The hypervolume indicator is the volume of the dominated region.

Parameters:

reference – a reference point to use, with shape [D]. Defines the upper bound of the hypervolume. Should be equal or bigger than the anti-ideal point of the Pareto set. For comparing results across runs, the same reference point must be used.

Returns:

hypervolume indicator, if reference point is less than all of the front in any dimension, the hypervolume indicator will be zero.

Raises:

  • ValueError (or tf.errors.InvalidArgumentError) – If reference has an invalid shape.

  • ValueError (or tf.errors.InvalidArgumentError) – If self.front is empty (which can happen if the concentration point is too strict so no frontier exists after the screening)

sample_diverse_subset(sample_size: int, allow_repeats: bool = True, bounds_delta_scale_factor: float = 0.2, bounds_min_delta: float = 1e-09) → tuple[trieste.types.TensorType, trieste.types.TensorType][source]#

Sample a set of diverse points from the Pareto set using Hypervolume Sharpe-Ratio Indicator

Parameters:

  • sample_size – The number of points to sample from the Pareto front

  • allow_repeats – Whether the sample may contain repeats

  • bounds_delta_scale_factor – The factor by which to grow the distance between extrema when calculating lower and upper bounds

  • bounds_min_delta – The minimum value of the distance between extrema

Returns:

sample: Tensor of query points selected in the sample and sample_ids: Tensor of indices of points selected from the Pareto set

get_reference_point(observations: trieste.types.TensorType) → trieste.types.TensorType[source]#

Default reference point calculation method that calculates the reference point according to a Pareto front extracted from set of observations.

Parameters:

observations – observations referred to calculate the reference point, with shape […, N, D]

Returns:

a reference point to use, with shape […, D].

Raises:

ValueError – If observations is empty