EGObox - Efficient Global Optimization toolbox

egobox package is the Python binding of the optimizer named Egor and the surrogate model Gpx, mixture of Gaussian processes, from the EGObox libraries written in Rust.

Installation

pip install egobox

This installation also provides the gpx command-line interface:

gpx --help

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]
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

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 and examples folder for more information on the usage of the optimizer and mixture of Gaussian processes surrogate model.