Scikit-Optimize

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 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 here.

Approximated objective function after 50 iterations of gp_minimize. Plot made using skopt.plots.plot_objective.

Important links

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

Install

The latest released version of scikit-optimize is v0.4, which you can install with:

pip install numpy # explicitly install this first
pip install scikit-optimize

In addition there is a conda-forge package for version 0.3 of scikit-optimize:

conda install -c conda-forge scikit-optimize

Using conda-forge is probably the easiest way to install scikit-optimize on Windows.

Getting started

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.

Development

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 -e.

Run all tests by executing pytest in the top level directory.

To only run the subset of tests with short run time, you can use pytest -m 'fast_test' (pytest -m 'slow_test' is also possible). To exclude all slow running tests try pytest -m 'not slow_test'.

This is implemented using pytest attributes. If a tests runs longer than 1 second, it is marked as slow, else as fast.

All contributors are welcome!

Commercial support

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.

Made possible by

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.