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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:
pip install waggon
or
git clone https://github.com/hse-cs/waggon
cd waggon
pip install -e
(See more examples in the documentation.)
The following code snippet is an example of surrogate optimisation.
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()
FAQs
WAsserstein Global Gradient-free OptimisatioN (WAGGON) methods library.
We found that waggon demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 1 open source maintainer collaborating on the project.
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