Benchmarking framework for noisy optimization and experiment planning
Project description
Olympus: a benchmarking framework for noisy optimization and experiment planning
Olympus
provides a consistent and easy-to-use framework for benchmarking optimization algorithms. With olympus
you can:
- Access a suite of 18 experiment planning algortihms via a simple and consistent interface
- Easily integrate custom optimization algorithms
- Access 10 experimentally-derived benchmarks emulated with probabilistic models, and 23 analytical test functions for optimization
- Easily integrate custom datasets, which can be used to train models for custom benchmarks
You can find more details in the documentation.
Installation
Olympus
can be installed with pip
:
pip install olymp
Dependencies
The installation only requires:
python >= 3.6
numpy
pandas
Additional libraries are required to use specific modules and objects. Olympus
will alert you about these requirements as you try access the related functionality.
Citation
Olympus
is research software. If you make use of it in scientific publications, please cite the following article:
@misc{olympus,
title={Olympus: a benchmarking framework for noisy optimization and experiment planning},
author={Florian Häse and Matteo Aldeghi and Riley J. Hickman and Loïc M. Roch and Melodie Christensen and Elena Liles and Jason E. Hein and Alán Aspuru-Guzik},
year={2020},
eprint={2010.04153},
archivePrefix={arXiv},
primaryClass={stat.ML}
}
License
Olympus
is distributed under an MIT License.
Project details
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