poli 🧪: a library of discrete objective functions#
poli is a library of discrete objective functions for benchmarking optimization algorithms. If offers
isolation of black box function calls inside conda environments. Don’t worry about clashes w. black box requirements, poli will create the relevant conda environments for you.
logging each black box call using observers.
A numpy interface. Inputs are
np.arrays of strings, outputs arenp.arrays of floats.
We also provide poli-baselines, a collection of optimizers of these discrete black box functions.
We are running a benchmark!
Using poli and poli-baselines, we are running a benchmark comparing high-dimensional Bayesian optimization algorithms for discrete sequence.
Getting started#
A good place to start is the next chapter! Go to Getting Started.
To install poli and poli-baselines, we recommend creating a fresh conda environment
conda create -n poli-base python=3.9
conda activate poli-base
pip install git+https://github.com/MachineLearningLifeScience/poli.git@dev
pip install git+https://github.com/MachineLearningLifeScience/poli-baselines.git@main
poli also runs on Google Colab. Here is a small example of how to run one of the objective functions..
Black-box objective functions#
Toy problems#
Small molecules#
Proteins#
Black-box optimization algorithms#
On top of poli, we provide poli-baselines, a collection of black-box optimization algorithms (focusing especially on discrete sequences). Examples include
Discrete#
Continuous#
Cite us and other relevant work#
If you use certain black boxes, we expect you to cite the relevant work. Check inside the documentation of each black box for the relevant references.
Contribute problems or solvers#
These are a couple of guides about how to contribute a new problem factory (i.e. black-box objective function), or a new optimization algorithm.