MCBO

MCBO: Framework and Benchmarks for Combinatorial and Mixed-variable Bayesian Optimization

This library provides an easy way to mix-and-match Bayesian optimization components in order to run new and existing mixed-variable or combinatorial Bayesian optimization.

Installation of MCBO

Tested on Ubuntu 18.04 and python 3.8.

Create a conda environment and activate it.

conda create -n mcbo_env python=3.8
conda activate mcbo_env

# install MCBO to be able to run optimization on custom problems
pip install -e .

# You can specify the path where all results will be stored by default
echo './results/' > ./mcbo/tasks/eda_seq_opt/results_root_path.txt

# if you want to run optimization of built-in Black-box, run the following (may take a while to get everything installed)
chmod u+x ./bbox_setup.sh
./bbox_setup.sh

Implemented Tasks

Synthetic

• 21 SFU test functions
• ackley: Ackley 20 caterorical dimensions (11 categories each).
• ackley-53: Ackley 50 binary dimensions, 3 numerical.
• pest: Pest Control

Real-world

• antibody_design: Antibody Design
• rna_inverse_fold: RNA Inverse Folding
• aig_optimization: EDA Sequence Optimisation (AIG sequence optimization)
• aig_optimization_hyp: EDA Sequence and Parameter Optimisation (AIG sequence and parameter optimization)
• mig_optimization: MIG Sequence Optimisation
• svm_opt: SVM hyperparameter tuning and feature selection
• xgboost_opt: XG-Boost hyperparameter tuning

How to access a task

All the tasks are accessible via the task_factory function. Below we show how to obtain the task and search_space class for the RNA inverse fold task.

from mcbo.factory import task_factory
import torch

task_name = 'rna_inverse_fold'
task = task_factory(task_name=task_name)
search_space = task.get_search_space()

Implemented Primitives

Surrogate model

• gp_o: GP with overlap kernel.
• gp_to: GP with transformed-overlap kernel.
• gp_hed: GP with the Hamming embedding via dictionary kernel.
• gp_ssk: GP with string subsequence kernel.
• gp_diff: GP with diffusion kernel.
• lr_sparse_hs: Bayesian linear regression with Hoorseshoe prior.

Acquisition function optimizers

• ga: Genetic algorithm.
• sa: Simulated Annealing.
• ls: Exhaustive Local Search.
• is: Interleaved search with Hill-Climbing and Gradient-Descent.
• mab: Multi-Armed Bandit for categorical and Gradient-Descent for numerical.

Acquisition functions

• ei: Expected Improvement.
• lcb: Lower Confidence Bound.
• ts: Thompson Sampling.

Trust-region

• basic: Hamming distance for categorical variables, hyperectangle limit for numerical variables.

Non-BO baselines

• rs: Random Search.
• ls: Local Search.
• sa: Simulated Annealing.
• ga: Genetic Algorithm.
• mab: Multi-Armed Bandit.

Simple optimization example

• A simple script to build Casmopolitan optimizer and run it on RNA inverse fold.

import torch
from mcbo import task_factory
from mcbo.optimizers.bo_builder import BoBuilder

if __name__ == '__main__':
    task_kws = dict(target=65)
    task = task_factory(task_name='rna_inverse_fold', **task_kws)
    search_space = task.get_search_space()
    bo_builder = BoBuilder(
        model_id='gp_to', acq_opt_id='is', acq_func_id='ei', tr_id='basic'
    )

    optimizer = bo_builder.build_bo(search_space=search_space, n_init=20, device=torch.device("cuda"))

    for i in range(100):
        x = optimizer.suggest(1)
        y = task(x)
        optimizer.observe(x, y)
        print(f'Iteration {i + 1:3d}/{100:3d} - f(x) = {y[0, 0]:.3f} - f(x*) = {optimizer.best_y:.3f}')

• To run and save optimization results with several seeds and optimizers, we provide the ./experiments/run_task_exps.py script calling run_experiment function.

python ./experiments/run_task_exps.py --device_id 0 --task_id "rna_inverse_fold" --optimizers_ids gp_to__is__ei__tr --seeds 42 --verbose 2

Reproduce the results of the paper.

Running

It is possible to reproduce our optimization results by running the script ./all_runs.sh.

chmod u+x ./experiments/all_runs.sh
./experiments/all_runs.sh

Results will be saved in ./resutls/ or in user specified path.

Visualize

We provide notebooks to visualize rankings and regrets. Many plotting tools are available in general_plot_utils.py.

Extend MCBO

Adding a task

• The task should be a class inheriting TaskBase
• Create a folder containing the code of the new task in the tasks folder.
• The folder can also contain a README providing a description of the task, associated search_space, dependencies
• Add the new task to the factory script (prefer local import of the new task class if it depends on packages not listed in requirements.txt).
• Add reference to the task in the present README.md in the dedicated section.

Cite us

If you use this library, please cite MCBO paper:

Kamil Dreczkowski, Antoine Grosnit, and Haitham Bou Ammar. Framework and Benchmarks for Combinatorial and Mixed-variable Bayesian Optimization

@misc{dreczkowski2023mcbo,
      title={Framework and Benchmarks for Combinatorial and Mixed-variable Bayesian Optimization}, 
      author={Kamil Dreczkowski and Antoine Grosnit and Haitham Bou Ammar},
      year={2023},
      eprint={2306.09803},
      archivePrefix={arXiv},
      primaryClass={cs.LG}
}