# EGObox - Efficient Global Optimization toolbox
[](https://github.com/relf/egobox/actions?query=workflow%3Apytest)
[](https://doi.org/10.21105/joss.04737)
`egobox` package is the Python binding of the optimizer named `Egor` and the surrogate model `Gpx`, mixture of Gaussian processes, from the [EGObox libraries](https://github.com/relf/egobox?tab=readme-ov-file#egobox---efficient-global-optimization-toolbox) written in Rust.
## 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([[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 matplotlib.pyplot as plt
import egobox as egx
# Training
xtrain = np.array([0.0, 1.0, 2.0, 3.0, 4.0])
ytrain = np.array([0.0, 1.0, 1.5, 0.9, 1.0])
gpx = egx.Gpx.builder().fit(xtrain, ytrain)
# Prediction
xtest = np.linspace(0, 4, 100).reshape((-1, 1))
ytest = gpx.predict(xtest)
# Plot
plt.plot(xtest, ytest)
plt.plot(xtrain, ytrain, "o")
plt.show()
```
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.
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"description": "# EGObox - Efficient Global Optimization toolbox\n\n[](https://github.com/relf/egobox/actions?query=workflow%3Apytest)\n[](https://doi.org/10.21105/joss.04737)\n\n`egobox` package is the Python binding of the optimizer named `Egor` and the surrogate model `Gpx`, mixture of Gaussian processes, from the [EGObox libraries](https://github.com/relf/egobox?tab=readme-ov-file#egobox---efficient-global-optimization-toolbox) written in Rust.\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([[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 matplotlib.pyplot as plt\nimport egobox as egx\n\n# Training\nxtrain = np.array([0.0, 1.0, 2.0, 3.0, 4.0])\nytrain = np.array([0.0, 1.0, 1.5, 0.9, 1.0])\ngpx = egx.Gpx.builder().fit(xtrain, ytrain)\n\n# Prediction\nxtest = np.linspace(0, 4, 100).reshape((-1, 1))\nytest = gpx.predict(xtest)\n\n# Plot\nplt.plot(xtest, ytest)\nplt.plot(xtrain, ytrain, \"o\")\nplt.show()\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",
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