qpots.acquisition

class qpots.acquisition.Acquisition(func: Function, gps: ModelListGP, cons: Callable | None = None, device: device = device(type='cpu'), q: int = 1, NUM_RESTARTS: int = 10, RAW_SAMPLES: int = 512)[source]

Bases: object

A class providing various acquisition functions and methods for multi-objective optimization.

__init__(func: Function, gps: ModelListGP, cons: Callable | None = None, device: device = device(type='cpu'), q: int = 1, NUM_RESTARTS: int = 10, RAW_SAMPLES: int = 512) → None[source]

Initialize the multi-objective acquisition class.

This class provides various acquisition functions for multi-objective Bayesian optimization. It supports Gaussian Process models and handles inequality constraints if provided.

Parameters:

  • func (Function) – The test function being optimized.

  • gps (ModelListGP) – A list of Gaussian Process models used for Bayesian optimization.

  • cons (Optional[Callable], optional) – A vector-valued function representing inequality constraints. If provided, the acquisition function will account for feasibility constraints.

  • device (torch.device, optional) – The computational device to use (CPU or GPU). Defaults to torch.device("cpu").

  • q (int, optional) – The number of candidate points to sample per iteration. Defaults to 1.

  • NUM_RESTARTS (int, optional) – The number of restarts for optimizing the acquisition function. A higher value can improve optimization quality. Defaults to 10.

  • RAW_SAMPLES (int, optional) – The number of raw samples used in acquisition optimization. Higher values increase exploration but may slow computation. Defaults to 512.

jesmo() → Tensor[source]

Perform Joint Entropy Search for Multi-Objective optimization (JESMO).

JESMO is an acquisition function that uses joint entropy search to efficiently explore the Pareto frontier in multi-objective Bayesian optimization.

Returns:

A tensor containing the candidate points generated by JESMO.

Return type:

Tensor

mesmo() → Tensor[source]

Perform Multi-Objective Max-Value Entropy Search (MESMO).

Returns:

A tensor containing the candidate points selected by MESMO.

Return type:

Tensor

parego() → Tensor[source]

Perform qParEGO optimization using random weights for scalarization.

Returns:

A tensor containing the new candidate points selected based on qParEGO optimization.

Return type:

Tensor

pesmo() → Tensor[source]

Perform Predictive Entropy Search for Multi-Objective optimization (PESMO).

Returns:

A tensor containing the candidate points selected by PESMO.

Return type:

Tensor

qlogei(ref_point: Tensor = tensor([0., 0.])) → Tensor[source]

Optimize the qLogEI acquisition function and return new candidate points.

Parameters:

ref_point (Tensor, optional) – The reference point for hypervolume calculation, typically representing a baseline for performance. Defaults to [0.0, 0.0].

Returns:

A tensor containing the new candidate points selected based on qLogEI optimization.

Return type:

Tensor

qlogparego() → Tensor[source]

Perform qParEGO optimization using qNParEGO from BoTorch.

Returns:

A tensor containing the candidate points selected based on qParEGO optimization.

Return type:

Tensor

qpots(bounds: Tensor, iteration: int, **kwargs) → Tensor[source]

Perform Pareto Optimal Thompson Sampling (qPOTS).

Parameters:

  • bounds (Tensor) – A tensor representing the lower and upper bounds for the optimization problem.

  • iteration (int) – The current iteration index, used for seeding randomness.

  • **kwargs (dict) –

    Additional arguments for customization, including:

    • nystrom (int): Whether to use the Nystrom approximation (1 for yes, 0 for no).

    • iters (int): Number of iterations for approximation.

    • nychoice (str): Column selection method for the Nystrom approximation.

    • dim (int): Dimensionality of the problem.

    • ngen (int): Number of generations for NSGA-II optimization.

    • q (int): Number of candidates to select.

Returns:

A tensor containing the selected candidate points after Pareto Optimal Thompson Sampling.

Return type:

Tensor

sobol() → Tensor[source]

Generate random Sobol sequence samples.

Returns:

A tensor of randomly generated candidate points using the Sobol sequence.

Return type:

Tensor

tsemo(save_dir: str, iters: int, ref_point: Tensor, train_shape: int, rep: int = 0)[source]

Perform Thompson Sampling Efficient Multiobjective Optimization (TS-EMO).

Parameters:

  • save_dir (str) – The directory to save the TS-EMO results.

  • iters (int) – How many iterations TS-EMO should run for.

  • ref_point (Tensor) – The reference point for the hypervolume calculation.

  • train_shape (int) – The shape for determining the size of bounds.

  • rep (int, optional) – The repetition of the experiment. Defaults to 0.

Returns:

  • x (np.ndarray) – The inputs of the function chosen by TS-EMO.

  • y (np.ndarray) – The outputs of the function for each input.

  • times (np.ndarray) – The time that each iteration takes.

  • hv (list) – The list of the hypervolume at each iteration.

  • pf (Tensor) – The Pareto frontier determined by TS-EMO.