# egobox
[](https://github.com/relf/egobox/actions?query=workflow%3Atests)
[](https://github.com/relf/egobox/actions?query=workflow%3Apytests)
[](https://github.com/relf/egobox/actions?query=workflow%3Alint)
[](https://doi.org/10.21105/joss.04737)
Rust toolbox for Efficient Global Optimization algorithms inspired from [SMT](https://github.com/SMTorg/smt).
`egobox` 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
Thanks to the [PyO3 project](https://pyo3.rs), which makes Rust well suited for building Python extensions.
You can install the Python package using:
```bash
pip install egobox
```
See the [tutorial notebooks](https://github.com/relf/egobox/tree/master/doc/README.md) for 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 = "0.19" }
egobox-gp = { version = "0.19" }
egobox-moe = { version = "0.19" }
egobox-ego = { version = "0.19" }
```
### 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 = "0.19", 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.
Raw data
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"description": "# egobox\n\n[](https://github.com/relf/egobox/actions?query=workflow%3Atests)\n[](https://github.com/relf/egobox/actions?query=workflow%3Apytests)\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 algorithms inspired from [SMT](https://github.com/SMTorg/smt).\n\n`egobox` 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\nThanks to the [PyO3 project](https://pyo3.rs), which makes Rust well suited for building Python extensions.\nYou can install the Python package using:\n\n```bash\npip install egobox\n```\n\nSee the [tutorial notebooks](https://github.com/relf/egobox/tree/master/doc/README.md) for usage of the optimizer\nand 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 = \"0.19\" }\negobox-gp = { version = \"0.19\" }\negobox-moe = { version = \"0.19\" }\negobox-ego = { version = \"0.19\" }\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 = \"0.19\", 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",
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