25 releases (breaking)
0.23.0 | Oct 1, 2024 |
---|---|
0.21.1 | Jul 30, 2024 |
0.16.0 | Mar 7, 2024 |
0.14.0 | Dec 13, 2023 |
0.4.0 | Jul 9, 2022 |
#108 in Machine learning
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EGObox - Efficient Global Optimization toolbox
Rust toolbox for Efficient Global Optimization inspired by the EGO implementation in the SMT Python library.
The egobox
package is twofold:
- for end-users: a Python module, the Python binding of the optimizer named
Egor
and the surrogate modelGpx
, mixture of Gaussian processes, written in Rust. - for developers: a set of Rust libraries useful to implement bayesian optimization (EGO-like) algorithms,
The Python module
Installation
pip install egobox
Egor optimizer
import numpy as np
import egobox as egx
# Objective function
def f_obj(x: np.ndarray) -> np.ndarray:
return (x - 3.5) * np.sin((x - 3.5) / (np.pi))
# Minimize f_opt in [0, 25]
res = egx.Egor(egx.to_specs([[0.0, 25.0]]), seed=42).minimize(f_obj, max_iters=20)
print(f"Optimization f={res.y_opt} at {res.x_opt}") # Optimization f=[-15.12510323] at [18.93525454]
Gpx surrogate model
import numpy as np
import egobox as egx
# Training
xtrain = np.array([[0.0, 1.0, 2.0, 3.0, 4.0]]).T
ytrain = np.array([[0.0, 1.0, 1.5, 0.9, 1.0]]).T
gpx = egx.Gpx.builder().fit(xtrain, ytrain)
# Prediction
xtest = np.linspace(0, 4, 20).reshape((-1, 1))
ytest = gpx.predict(xtest)
See the tutorial notebooks and examples folder for more information on the usage of the optimizer and mixture of Gaussian processes surrogate model.
The Rust libraries
egobox
Rust libraries consists of the following sub-packages.
Name | Version | Documentation | Description |
---|---|---|---|
doe | sampling methods; contains LHS, FullFactorial, Random methods | ||
gp | gaussian process regression; contains Kriging, PLS dimension reduction and sparse methods | ||
moe | mixture of experts using GP models | ||
ego | efficient global optimization with constraints and mixed integer handling |
Usage
Depending on the sub-packages you want to use, you have to add following declarations to your Cargo.toml
[dependencies]
egobox-doe = { version = "0.23" }
egobox-gp = { version = "0.23" }
egobox-moe = { version = "0.23" }
egobox-ego = { version = "0.23" }
Features
The table below presents the various features available depending on the subcrate
Name | doe | gp | moe | ego |
---|---|---|---|---|
serializable | ✔️ | ✔️ | ✔️ | |
persistent | ✔️ | ✔️(*) | ||
blas | ✔️ | ✔️ | ✔️ | |
nlopt | ✔️ | ✔️ |
(*) required for mixed-variable gaussian process
serializable
When selected, the serialization with serde crate is enabled.
persistent
When selected, the save and load as a json file with serde_json crate is enabled.
blas
When selected, the usage of BLAS/LAPACK backend is possible, see below for more information.
nlopt
When selected, the nlopt crate is used to provide optimizer implementations (ie Cobyla, Slsqp)
Examples
Examples (in examples/
sub-packages folder) are run as follows:
cd doe && cargo run --example samplings --release
cd gp && cargo run --example kriging --release
cd moe && cargo run --example clustering --release
cd ego && cargo run --example ackley --release
BLAS/LAPACK backend (optional)
egobox
relies on linfa project for methods like clustering and dimension reduction, but also try to adopt as far as possible the same coding structures.
As for linfa
, the linear algebra routines used in gp
, moe
ad ego
are provided by the pure-Rust linfa-linalg crate, the default linear algebra provider.
Otherwise, you can choose an external BLAS/LAPACK backend available through the ndarray-linalg crate. In this case, you have to specify the blas
feature and a linfa
BLAS/LAPACK backend feature (more information in linfa features).
Thus, for instance, to use gp
with the Intel MKL BLAS/LAPACK backend, you could specify in your Cargo.toml
the following features:
[dependencies]
egobox-gp = { version = "0.23", features = ["blas", "linfa/intel-mkl-static"] }
or you could run the gp
example as follows:
cd gp && cargo run --example kriging --release --features blas,linfa/intel-mkl-static
Citation
If you find this project useful for your research, you may cite it as follows:
@article{
Lafage2022,
author = {Rémi Lafage},
title = {egobox, a Rust toolbox for efficient global optimization},
journal = {Journal of Open Source Software}
year = {2022},
doi = {10.21105/joss.04737},
url = {https://doi.org/10.21105/joss.04737},
publisher = {The Open Journal},
volume = {7},
number = {78},
pages = {4737},
}
Additionally, you may consider adding a star to the repository. This positive feedback improves the visibility of the project.
References
Bartoli, N., Lefebvre, T., Dubreuil, S., Olivanti, R., Priem, R., Bons, N., Martins, J. R. R. A., & Morlier, J. (2019). Adaptive modeling strategy for constrained global optimization with application to aerodynamic wing design. Aerospace Science and Technology, 90, 85–102. https://doi.org/10.1016/j.ast.2019.03.041
Bouhlel, M. A., Bartoli, N., Otsmane, A., & Morlier, J. (2016). Improving kriging surrogates of high-dimensional design models by partial least squares dimension reduction. Structural and Multidisciplinary Optimization, 53(5), 935–952. https://doi.org/10.1007/s00158-015-1395-9
Bouhlel, M. A., Hwang, J. T., Bartoli, N., Lafage, R., Morlier, J., & Martins, J. R. R. A. (2019). A python surrogate modeling framework with derivatives. Advances in Engineering Software, 102662. https://doi.org/10.1016/j.advengsoft.2019.03.005
Dubreuil, S., Bartoli, N., Gogu, C., & Lefebvre, T. (2020). Towards an efficient global multi- disciplinary design optimization algorithm. Structural and Multidisciplinary Optimization, 62(4), 1739–1765. https://doi.org/10.1007/s00158-020-02514-6
Jones, D. R., Schonlau, M., & Welch, W. J. (1998). Efficient global optimization of expensive black-box functions. Journal of Global Optimization, 13(4), 455–492. https://www.researchgate.net/publication/235709802_Efficient_Global_Optimization_of_Expensive_Black-Box_Functions
Diouane, Youssef, et al. "TREGO: a trust-region framework for efficient global optimization." Journal of Global Optimization 86.1 (2023): 1-23. https://arxiv.org/pdf/2101.06808
smtorg. (2018). Surrogate modeling toolbox. In GitHub repository. GitHub. https://github.com/SMTOrg/smt
License
Licensed under the Apache License, Version 2.0 http://www.apache.org/licenses/LICENSE-2.0
Dependencies
~23–42MB
~670K SLoC