Scikit-Optimize, or skopt, is a simple and efficient library to minimize (very) expensive and noisy black-box functions. It implements several methods for sequential model-based optimization. skopt is reusable in many contexts and accessible.

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.

• Static documentation - Static documentation
• Example notebooks - can be found under the examples/ directory.
• Issue tracker - https://github.com/scikit-optimize/scikit-optimize/issues
• Releases - https://pypi.python.org/pypi/scikit-optimize

pip install scikit-optimize

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 read our introduction to bayesian optimization and the other examples.

The library is still experimental and under heavy development. Checkout the next milestone for the plans for the next release or look at some easy issues to get started contributing.

The development version can be installed through:

git clone https://github.com/scikit-optimize/scikit-optimize.git
cd scikit-optimize
pip install -r requirements.txt
python setup.py develop

Run the tests by executing nosetests in the top level directory.

All contributors are welcome!