<p align="center">
<img
width="100"
src="./doc/LOGO_EGOBOX_v4_100x100.png"
alt="Efficient Global Optimization toolbox in Rust"
/>
</p>
# EGObox - Efficient Global Optimization toolbox
[](https://github.com/relf/egobox/actions?query=workflow%3Atests)
[](https://github.com/relf/egobox/actions?query=workflow%3Apytest)
[](https://github.com/relf/egobox/actions?query=workflow%3Alint)
[](https://doi.org/10.21105/joss.04737)
Rust toolbox for Efficient Global Optimization inspired by [the EGO implementation](https://smt.readthedocs.io/en/stable/_src_docs/applications/ego.html)
in the [SMT](https://github.com/SMTorg/smt) Python library.
The `egobox` package is twofold:
1. for end-users: [a Python module](#the-python-module), the Python binding of the optimizer named `Egor` and the surrogate model `Gpx`, mixture of Gaussian processes, written in Rust.
2. for developers: [a set of Rust libraries](#the-rust-libraries) useful to implement bayesian optimization (EGO-like) algorithms,
## The Python module
### Installation
```bash
pip install egobox
```
### Egor optimizer
```python
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
```python
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](https://github.com/relf/egobox/tree/master/doc/README.md) and [examples folder](https://github.com/relf/egobox/tree/d9db0248199558f23d966796737d7ffa8f5de589/python/egobox/examples) 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](https://github.com/relf/egobox/tree/master/doe) | [](https://crates.io/crates/egobox-doe) | [](https://docs.rs/egobox-doe) | sampling methods; contains LHS, FullFactorial, Random methods |
| [gp](https://github.com/relf/egobox/tree/master/gp) | [](https://crates.io/crates/egobox-gp) | [](https://docs.rs/egobox-gp) | gaussian process regression; contains Kriging, PLS dimension reduction and sparse methods |
| [moe](https://github.com/relf/egobox/tree/master/moe) | [](https://crates.io/crates/egobox-moe) | [](https://docs.rs/egobox-moe) | mixture of experts using GP models |
| [ego](https://github.com/relf/egobox/tree/master/ego) | [](https://crates.io/crates/egobox-ego) | [](https://docs.rs/egobox-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`
```text
[dependencies]
egobox-doe = { version = "0.24" }
egobox-gp = { version = "0.24" }
egobox-moe = { version = "0.24" }
egobox-ego = { version = "0.24" }
```
### 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](https://serde.rs/) is enabled.
#### persistent
When selected, the save and load as a json file with [serde_json crate](https://serde.rs/) is enabled.
#### blas
When selected, the usage of BLAS/LAPACK backend is possible, see [below](#blaslapack-backend-optional) for more information.
#### nlopt
When selected, the [nlopt crate](https://github.com/adwhit/rust-nlopt) is used to provide optimizer implementations (ie Cobyla, Slsqp)
### Examples
Examples (in `examples/` sub-packages folder) are run as follows:
```bash
cd doe && cargo run --example samplings --release
```
``` bash
cd gp && cargo run --example kriging --release
```
``` bash
cd moe && cargo run --example clustering --release
```
``` bash
cd ego && cargo run --example ackley --release
```
### BLAS/LAPACK backend (optional)
`egobox` relies on [linfa](https://github.com/rust-ml/linfa) project for methods like clustering and dimension reduction, but also try to adopt as far as possible the same [coding structures](https://github.com/rust-ml/linfa/blob/master/CONTRIBUTE.md).
As for `linfa`, the linear algebra routines used in `gp`, `moe` ad `ego` are provided by the pure-Rust [linfa-linalg](https://github.com/rust-ml/linfa-linalg) crate, the default linear algebra provider.
Otherwise, you can choose an external BLAS/LAPACK backend available through the [ndarray-linalg](https://github.com/rust-ndarray/ndarray-linalg) crate. In this case, you have to specify the `blas` feature and a `linfa` [BLAS/LAPACK backend feature](https://github.com/rust-ml/linfa#blaslapack-backend) (more information in [linfa features](https://github.com/rust-ml/linfa#blaslapack-backend)).
Thus, for instance, to use `gp` with the Intel MKL BLAS/LAPACK backend, you could specify in your `Cargo.toml` the following features:
```text
[dependencies]
egobox-gp = { version = "0.24", features = ["blas", "linfa/intel-mkl-static"] }
```
or you could run the `gp` example as follows:
``` bash
cd gp && cargo run --example kriging --release --features blas,linfa/intel-mkl-static
```
## Citation
[](https://doi.org/10.21105/joss.04737)
If you find this project useful for your research, you may cite it as follows:
```text
@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>
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"description": "<p align=\"center\">\n <img\n width=\"100\"\n src=\"./doc/LOGO_EGOBOX_v4_100x100.png\"\n alt=\"Efficient Global Optimization toolbox in Rust\"\n />\n</p>\n\n# EGObox - Efficient Global Optimization toolbox\n\n[](https://github.com/relf/egobox/actions?query=workflow%3Atests)\n[](https://github.com/relf/egobox/actions?query=workflow%3Apytest)\n[](https://github.com/relf/egobox/actions?query=workflow%3Alint)\n[](https://doi.org/10.21105/joss.04737)\n\nRust toolbox for Efficient Global Optimization inspired by [the EGO implementation](https://smt.readthedocs.io/en/stable/_src_docs/applications/ego.html)\nin the [SMT](https://github.com/SMTorg/smt) Python library.\n\nThe `egobox` package is twofold:\n\n1. for end-users: [a Python module](#the-python-module), the Python binding of the optimizer named `Egor` and the surrogate model `Gpx`, mixture of Gaussian processes, written in Rust.\n2. for developers: [a set of Rust libraries](#the-rust-libraries) useful to implement bayesian optimization (EGO-like) algorithms,\n\n## The Python module\n\n### Installation\n\n```bash\npip install egobox\n```\n\n### Egor optimizer\n\n```python\nimport numpy as np\nimport egobox as egx\n\n# Objective function\ndef f_obj(x: np.ndarray) -> np.ndarray:\n return (x - 3.5) * np.sin((x - 3.5) / (np.pi))\n\n# Minimize f_opt in [0, 25]\nres = egx.Egor(egx.to_specs([[0.0, 25.0]]), seed=42).minimize(f_obj, max_iters=20)\nprint(f\"Optimization f={res.y_opt} at {res.x_opt}\") # Optimization f=[-15.12510323] at [18.93525454]\n```\n\n### Gpx surrogate model\n\n```python\nimport numpy as np\nimport egobox as egx\n\n# Training\nxtrain = np.array([[0.0, 1.0, 2.0, 3.0, 4.0]]).T\nytrain = np.array([[0.0, 1.0, 1.5, 0.9, 1.0]]).T\ngpx = egx.Gpx.builder().fit(xtrain, ytrain)\n\n# Prediction\nxtest = np.linspace(0, 4, 20).reshape((-1, 1))\nytest = gpx.predict(xtest)\n```\n\nSee the [tutorial notebooks](https://github.com/relf/egobox/tree/master/doc/README.md) and [examples folder](https://github.com/relf/egobox/tree/d9db0248199558f23d966796737d7ffa8f5de589/python/egobox/examples) for more information on the usage of the optimizer and mixture of Gaussian processes surrogate model.\n\n## The Rust libraries\n\n`egobox` Rust libraries consists of the following sub-packages.\n\n| Name | Version | Documentation | Description |\n| :---------------------------------------------------- | :---------------------------------------------------------------------------------------------- | :-------------------------------------------------------------------------- | :---------------------------------------------------------------------------------------- |\n| [doe](https://github.com/relf/egobox/tree/master/doe) | [](https://crates.io/crates/egobox-doe) | [](https://docs.rs/egobox-doe) | sampling methods; contains LHS, FullFactorial, Random methods |\n| [gp](https://github.com/relf/egobox/tree/master/gp) | [](https://crates.io/crates/egobox-gp) | [](https://docs.rs/egobox-gp) | gaussian process regression; contains Kriging, PLS dimension reduction and sparse methods |\n| [moe](https://github.com/relf/egobox/tree/master/moe) | [](https://crates.io/crates/egobox-moe) | [](https://docs.rs/egobox-moe) | mixture of experts using GP models |\n| [ego](https://github.com/relf/egobox/tree/master/ego) | [](https://crates.io/crates/egobox-ego) | [](https://docs.rs/egobox-ego) | efficient global optimization with constraints and mixed integer handling |\n\n### Usage\n\nDepending on the sub-packages you want to use, you have to add following declarations to your `Cargo.toml`\n\n```text\n[dependencies]\negobox-doe = { version = \"0.24\" }\negobox-gp = { version = \"0.24\" }\negobox-moe = { version = \"0.24\" }\negobox-ego = { version = \"0.24\" }\n```\n\n### Features\n\nThe table below presents the various features available depending on the subcrate\n\n| Name | doe | gp | moe | ego |\n| :----------- | :--- | :--- | :--- | :--- |\n| serializable | \u2714\ufe0f | \u2714\ufe0f | \u2714\ufe0f | |\n| persistent | | | \u2714\ufe0f | \u2714\ufe0f(*) |\n| blas | | \u2714\ufe0f | \u2714\ufe0f | \u2714\ufe0f |\n| nlopt | | \u2714\ufe0f | | \u2714\ufe0f |\n\n(*) required for mixed-variable gaussian process\n\n#### serializable\n\nWhen selected, the serialization with [serde crate](https://serde.rs/) is enabled.\n\n#### persistent\n\nWhen selected, the save and load as a json file with [serde_json crate](https://serde.rs/) is enabled.\n\n#### blas\n\nWhen selected, the usage of BLAS/LAPACK backend is possible, see [below](#blaslapack-backend-optional) for more information.\n\n#### nlopt\n\nWhen selected, the [nlopt crate](https://github.com/adwhit/rust-nlopt) is used to provide optimizer implementations (ie Cobyla, Slsqp)\n\n### Examples\n\nExamples (in `examples/` sub-packages folder) are run as follows:\n\n```bash\ncd doe && cargo run --example samplings --release\n```\n\n``` bash\ncd gp && cargo run --example kriging --release\n```\n\n``` bash\ncd moe && cargo run --example clustering --release\n```\n\n``` bash\ncd ego && cargo run --example ackley --release\n```\n\n### BLAS/LAPACK backend (optional)\n\n`egobox` relies on [linfa](https://github.com/rust-ml/linfa) project for methods like clustering and dimension reduction, but also try to adopt as far as possible the same [coding structures](https://github.com/rust-ml/linfa/blob/master/CONTRIBUTE.md).\n\nAs for `linfa`, the linear algebra routines used in `gp`, `moe` ad `ego` are provided by the pure-Rust [linfa-linalg](https://github.com/rust-ml/linfa-linalg) crate, the default linear algebra provider.\n\nOtherwise, you can choose an external BLAS/LAPACK backend available through the [ndarray-linalg](https://github.com/rust-ndarray/ndarray-linalg) crate. In this case, you have to specify the `blas` feature and a `linfa` [BLAS/LAPACK backend feature](https://github.com/rust-ml/linfa#blaslapack-backend) (more information in [linfa features](https://github.com/rust-ml/linfa#blaslapack-backend)).\n\nThus, for instance, to use `gp` with the Intel MKL BLAS/LAPACK backend, you could specify in your `Cargo.toml` the following features:\n\n```text\n[dependencies]\negobox-gp = { version = \"0.24\", features = [\"blas\", \"linfa/intel-mkl-static\"] }\n```\n\nor you could run the `gp` example as follows:\n\n``` bash\ncd gp && cargo run --example kriging --release --features blas,linfa/intel-mkl-static\n```\n\n## Citation\n\n[](https://doi.org/10.21105/joss.04737)\n\nIf you find this project useful for your research, you may cite it as follows:\n\n```text\n@article{\n Lafage2022, \n author = {R\u00e9mi Lafage}, \n title = {egobox, a Rust toolbox for efficient global optimization}, \n journal = {Journal of Open Source Software} \n year = {2022}, \n doi = {10.21105/joss.04737}, \n url = {https://doi.org/10.21105/joss.04737}, \n publisher = {The Open Journal}, \n volume = {7}, \n number = {78}, \n pages = {4737}, \n} \n```\n\nAdditionally, you may consider adding a star to the repository. This positive feedback improves the visibility of the project.\n\n## References\n\nBartoli, N., Lefebvre, T., Dubreuil, S., Olivanti, R., Priem, R., Bons, N., Martins, J. R. R. A.,\n& Morlier, J. (2019). Adaptive modeling strategy for constrained global optimization with\napplication to aerodynamic wing design. Aerospace Science and Technology, 90, 85\u2013102.\n<https://doi.org/10.1016/j.ast.2019.03.041>\n\nBouhlel, M. A., Bartoli, N., Otsmane, A., & Morlier, J. (2016). Improving kriging surrogates\nof high-dimensional design models by partial least squares dimension reduction.\nStructural and Multidisciplinary Optimization, 53(5), 935\u2013952. <https://doi.org/10.1007/s00158-015-1395-9>\n\nBouhlel, M. A., Hwang, J. T., Bartoli, N., Lafage, R., Morlier, J., & Martins, J. R. R. A.\n(2019). A python surrogate modeling framework with derivatives. Advances in Engineering\nSoftware, 102662. <https://doi.org/10.1016/j.advengsoft.2019.03.005>\n\nDubreuil, S., Bartoli, N., Gogu, C., & Lefebvre, T. (2020). Towards an efficient global multi-\ndisciplinary design optimization algorithm. Structural and Multidisciplinary Optimization,\n62(4), 1739\u20131765. <https://doi.org/10.1007/s00158-020-02514-6>\n\nJones, D. R., Schonlau, M., & Welch, W. J. (1998). Efficient global optimization of expensive\nblack-box functions. Journal of Global Optimization, 13(4), 455\u2013492. <https://www.researchgate.net/publication/235709802_Efficient_Global_Optimization_of_Expensive_Black-Box_Functions>\n\nDiouane, Youssef, et al. \"TREGO: a trust-region framework for efficient global optimization.\"\nJournal of Global Optimization 86.1 (2023): 1-23. <https://arxiv.org/pdf/2101.06808>\n\nsmtorg. (2018). Surrogate modeling toolbox. In GitHub repository. GitHub. <https://github.com/SMTOrg/smt>\n\n## License\n\nLicensed under the Apache License, Version 2.0 <http://www.apache.org/licenses/LICENSE-2.0>\n\n",
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