Welcome to WAGGON: WAssrestein Global Gradient-free OptimisatioN

WAGGON is a python library of black box gradient-free optimisation. Currently, the library contains implementations of optimisation methods based on Wasserstein uncertainty and baseline approaches from the following papers:

• Tigran Ramazyan, Mikhail Hushchyn and Denis Derkach. "Global Optimisation of Black-Box Functions with Generative Models in the Wasserstein Space." Arxiv abs/2407.1117 (2024). [arxiv]

Implemented methods

• Wasserstein Uncertainty Global Optimisation (WU-GO)
• Bayesian optimisation: via Expected Improvement (EI), Lower and Upper Confidence Bounds (LCB, UCB)

Installation

pip install waggon

or

git clone https://github.com/hse-cs/waggon
cd waggon
pip install -e

Basic usage

(See more examples in the documentation.)

The following code snippet (does this and that)

import waggon
from waggon.optim import SurrogateOptimiser

from waggon.acquisitions import WU
from waggon.surrogates.gan import WGAN_GP as GAN
from waggon.test_functions import three_hump_camel

# initialise the function to be optimised
func = three_hump_camel()
# initialise the surrogate to carry out optimisation
surr = GAN()
# initialise optimisation acquisition function
acqf = WU()

# initialise optimiser
opt = SurrogateOptimiser(func=func, surr=surr, acqf=acqf)

# run optimisation
opt.optimise()

# visualise
waggon.utils.display()

Support

• Home: https://github.com/hse-cs/waggon
• Documentation: https://hse-cs.github.io/waggon
• For any usage questions, suggestions and bugs please use the issue page.