A python binding for egobox crates
Project description
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
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]
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
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.
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