Scikit-Optimize, or skopt, is a simple and efficient library for
optimizing (very) expensive and noisy black-box functions. It implements
several methods for sequential model-based optimization. skopt aims
to be accessible and easy to use in many contexts.
The library is built on top of NumPy, SciPy, and Scikit-Learn.
We do not perform gradient-based optimization. For gradient-based
optimization algorithms, look at scipy.optimize.
Approximated objective function after 50 iterations of gp_minimize.
Plot made using skopt.plots.plot_objective.
- Project website
- Example notebooks - can be found in examples.
- Discussion forum
- Issue tracker
- Releases - https://pypi.org/project/scikit-optimize
scikit-optimize requires Python >= 3.6. You can install the latest release with:
pip install scikit-optimize
This installs the essentials. To install plotting functionality, you can instead do:
pip install 'scikit-optimize[plots]'
This will additionally install Matplotlib.
If you're using Anaconda platform, there is a conda-forge package of scikit-optimize:
conda install -c conda-forge scikit-optimize
Using conda-forge is probably the easiest way to install scikit-optimize on Windows.
Find the minimum of the noisy function f(x) over the range
-2 < x < 2 with skopt:
import numpy as np
from skopt import gp_minimize
def f(x):
return (np.sin(5 * x[0]) * (1 - np.tanh(x[0] ** 2)) +
np.random.randn() * 0.1)
res = gp_minimize(f, [(-2.0, 2.0)])For more control over the optimization loop you can use the skopt.Optimizer
class:
from skopt import Optimizer
opt = Optimizer([(-2.0, 2.0)])
for i in range(20):
suggested = opt.ask()
y = f(suggested)
opt.tell(suggested, y)
print('iteration:', i, suggested, y)Read our introduction to bayesian optimization and the other examples.
See CONTRIBUTING.md.
Feel free to get in touch if you need commercial support or would like to sponsor development. Resources go towards paying for additional work by seasoned engineers and researchers.
The scikit-optimize project was made possible with the support of
If your employer allows you to work on scikit-optimize during the day and would like recognition, feel free to add them to the "Made possible by" list.