# EGObox - Efficient Global Optimization toolbox
[](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
```
This installation also provides the `gpx` command-line interface:
```bash
gpx --help
```
### 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]
optim = egx.Egor([[0.0, 25.0]]).minimize(f_obj, max_iters=20, seed=42)
print(f"Optimization f={optim.result.y_opt} at {optim.result.x_opt}") # Optimization f=[-15.12510323] at [18.93525454]
print(f"Status {optim.status.exit} in {optim.status.elapsed_time}s") # ExitStatus.SOLVER_CONVERGED in 0.021s
```
### 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/notebooks/README.md) and [examples folder](https://github.com/relf/egobox/tree/master/python/examples) for more information on the usage of the optimizer and mixture of Gaussian processes surrogate model.