Scikit-Optimize, or skopt, is a simple and efficient library for sequential model-based optimization, accessible to everybody and reusable in various contexts.

The library is built on top of NumPy, SciPy and Scikit-Learn.

We do not do gradient-based optimization. For gradient-based optimization you should be looking 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

These instructions will setup the latest released version of scikit-optimize. Currently scikit-optimize relies on a yet unreleased version of scikit-learn. This means you will have to install that version by hand and probably want to create a separate virtualenv or conda environment for it.

pip install -e git+https://github.com/scikit-learn/scikit-learn.git#egg=scikit-learn-0.18dev

After this you can install scikit-optimize with:

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.

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.

Contributors are welcome!