tsingqaq / pf2es

Information-Theoretic based Acquisition Function for Parallalble Multi-Objective Bayesian Optimization

{PF} $^2$ ES: A New Acquisition Function for Parallel Feasible Pareto Frontier Entropy Search

Installation

❗❗❗Caution: You are away from the main branch of Trieste, this branch contains certain other dependencies

install from sources, run

$ pip install -e.

in the repository root (tested with Python version 3.7.11).

Tutorial/Demonstration Notebook

There is a tutorial notebook multi_objective_entropy_search_via_PF2ES.py at (\docs\notebooks) demonstrating how to make use of the acquisition function for different multi-objective optimization tasks.

In order to run the notebook, install the following dependency:

$ pip install -r notebooks/requirements.txt

Then, run the notebooks with

$ jupyter-notebook notebooks

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Reproduce the paper's result

If you'd like to reproduce the paper's result exactly, the following directories contain relevant experiments:

• docs\exp\unconstraint_exp MOO Experiments
• docs\exp\constraint_exp Constraint MOO Experiments
• For other information-theoretic acquisitions utilized in experimental comparison, please refer their branch:
  • PESMO: https://github.com/HIPS/Spearmint/tree/PESM
  • MESMOC+: https://github.com/fernandezdaniel/Spearmint
  • PPESMOC: https://github.com/EduardoGarrido90/spearmint_ppesmoc