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Phoenics: A deep Bayesian optimizer

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Phoenics is an open source optimization algorithm combining ideas from Bayesian optimization with Bayesian Kernel Density estimation [1]. It performs global optimization on expensive to evaluate objectives, such as physical experiments or demanding computations.

Check out the examples folder for detailed descriptions and code examples for:

Example Link
Sequential optimization examples/optimization_sequential

Using Phoenics

Phoenics is designed to suggest new parameter points based on prior observations. The suggested parameters can then be passed on to objective evaluations (experiments or involved computation). As soon as the objective values have been determined for a set of parameters, these new observations can again be passed on to Phoenics to request new, more informative parameters.

from phoenics import Phoenics
# create an instance from a configuration file
config_file = 'config.json'
phoenics    = Phoenics(config_file)
# request new parameters from a set of observations
params      = phoenics.recommend(observations = observations)

Detailed examples for specific applications are presented in the examples folder.


Note: This repository is under construction! We hope to add further details on the method, instructions and more examples in the near future.

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[1] Häse, F., Roch, L. M., Kreisbeck, C., & Aspuru-Guzik, A. Phoenics: A Bayesian Optimizer for Chemistry. ACS central science 4.6 (2018): 1134-1145.

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