Simlation Based Bayesian Optimization

This repository contains the code needed to reproduce all the experiments in the "Simulation Based Bayesian Optimization" paper: https://arxiv.org/abs/2401.10811

This paper introduces Simulation Based Bayesian Optimization (SBBO) as a novel approach to optimizing acquisition functions in Bayesian Optimization that only requires sampling-based access to posterior predictive distributions. SBBO allows the use of surrogate probabilistic models tailored for combinatorial spaces with discrete variables. Any Bayesian model in which posterior inference is carried out through Markov chain Monte Carlo can be selected as the surrogate model in SBBO. In applications involving combinatorial optimization, we demonstrate empirically the effectiveness of SBBO method using various choices of surrogate models.

The code is written Python. A conda environment contains all necessary dependencies. It can be installed using

conda env create -f sbbo.yml

And activated through

conda activate sbbo

In addition, the sbbo package must be installed running the following in the root directory:

pip install -e .

Running experiments

To run SBBO, execute the following:

python -u run.py --problem {problem}
                 --learner {learner} --acqfun {acqfun}
                 --niters {niters} --search {search}
                 --seed_conf {seed_conf} --nexp {nexp} 

The --problem argument selects the optimization problem in which SBBO will be run. Currently implemented options are:

• BQP: Binary quadratic problem (Section 4.1 of the paper).
• CON: Contamination problem (Section 4.2 of the paper).
• pRNA: RNA design problem (Section 4.3 of the paper).

The --learner argument selects the surrogate probabilistic model. Available options are:

• BOCS: Sparse Bayesian linear regression with pairwise interactions.
• BNN: Bayesian Neural Network.
• GPr: Tanimoto Gaussian Process Regression.
• NGBdec: Natural gradient boosting with a shallow decision tree model as base learner.
• NGBlinCV: Natural gradient boosting with an sparse linear regression as base learner.

The --acqfun argument selects the acquisition function. Specify EI for expected improvement or PI for probability of improvement. --niters specifies the number of function evaluations. --search selects the algorithm used to select the next function evaluation location. Possible options are:

• MH: to use SBBO with Metropolis-Hastings sampling scheme.
• Gibbs: to use SBBO with Gibbs sampling scheme.
• RS: to use Random Local Search.
• SA: to use Simulated Annealing.

--seed_conf allows configuration of random seed. Set it to 23 in order to reproduce the experiments in the paper. Finally, use --nexp as a label for the experiment number.

After running the previous command with the selected options, results will be stored in the results folder.

In order to reproduce the convergence plot from the appendix, run the convergence.ipynb jupyter notebook located in the notebooks folder.

Tables and visualizations

The code to reproduce tables an plots has been written in R. tidyverse, kableExtra and latex2exp libraries need to be installed. First of all, results generated in the experiments of the previous section must be preprocessed running

Rscript preprocess_results.R

Then, the plots of the paper could be reproduced running

Rscript plots.R

Resulting images will be stored in the figs folder.

Finally, tables can be reproduced running

Rscript tables.R

Latex code for each table will be printed.