qpots.utils.utils

This module contains utility functions for multi-objective Bayesian optimization.

Functions

qpots.utils.utils.unstandardize(Y: Tensor, train_y: Tensor) → Tensor[source]

Reverse the standardization of output Y using the mean and standard deviation computed from the training data.

Parameters:

  • Y (torch.Tensor) – The standardized output tensor.

  • train_y (torch.Tensor) – The training output data used to compute the mean and standard deviation.

Returns:

The unstandardized output tensor.

Return type:

torch.Tensor

qpots.utils.utils.expected_hypervolume(gps: ModelObject, ref_point: Tensor = tensor([-300., -18.]), min: bool = False) → Tuple[float, Tensor][source]

Compute the expected hypervolume and Pareto front based on GP model predictions.

Parameters:

  • gps (ModelObject) – The multi-objective GP models.

  • ref_point (torch.Tensor, optional) – Reference point for hypervolume calculation.

  • min (bool, optional) – If True, minimizes the objectives instead of maximizing them.

Returns:

  • hypervolume_value (float): The computed hypervolume.

  • pareto_front (torch.Tensor): The Pareto front tensor.

Return type:

tuple

qpots.utils.utils.gen_filtered_cands(gps: ModelObject, cands: Tensor, ref_point: Tensor = tensor([0., 0.]), kernel_bandwidth: float = 0.05) → Tensor[source]

Generate filtered candidate points based on the current Pareto front using Kernel Density Estimation (KDE).

Parameters:

  • gps (ModelObject) – The multi-objective GP models.

  • cands (torch.Tensor) – Candidate points to filter.

  • ref_point (torch.Tensor, optional) – Reference point for the Pareto front.

  • kernel_bandwidth (float, optional) – Bandwidth for the KDE filter.

Returns:

Filtered candidate points.

Return type:

torch.Tensor

qpots.utils.utils.select_candidates(gps: ModelObject, pareto_set: ndarray, device: device, q: int = 1, seed: int | None = None) → Tensor[source]

Select candidates from the Pareto-optimal set.

Parameters:

  • gps (ModelObject) – Gaussian Process models.

  • pareto_set (numpy.ndarray) – Pareto-optimal set of solutions.

  • device (torch.device) – Device to store the selected candidates.

  • q (int, optional) – Number of candidates to select. Defaults to 1.

  • seed (int, optional) – Random seed for sampling.

Returns:

Selected candidate points.

Return type:

torch.Tensor

qpots.utils.utils.arg_parser()[source]

Parses command-line arguments for the multi-objective Bayesian optimization script.

This function provides default values for each argument, allowing customization for different optimization setups, including high-performance computing (HPC) environments.

Returns:

Parsed command-line arguments.

Return type:

argparse.Namespace