scikit-optimize

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Scikit-Optimize

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 here <http://docs.scipy.org/doc/scipy/reference/optimize.html>_.

.. figure:: https://raw.githubusercontent.com/holgern/scikit-optimize/main/media/bo-objective.png :alt: Approximated objective

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

Maintaining the codebase

This repo is a copy of the original repositoy at https://github.com/scikit-optimize/scikit-optimize/. As the original repo is now in read-only mode, i decided to continue the development on it on my own. I still have credentials for pypi, so I will publish new releases at https://pypi.org/project/scikit-optimize/. I did my best to include all open PR since 2021 in the new release of scikit-optimize 0.10.

https://scikit-optimize.github.io/ has been moved to http://scikit-optimize.readthedocs.io/.

Important links

• Project website https://scikit-optimize.readthedocs.io/
• Example notebooks - can be found in examples_.
• Discussion forum <https://github.com/scikit-optimize/scikit-optimize/discussions>__
• Issue tracker - https://github.com/holgern/scikit-optimize/issues
• Releases - https://pypi.python.org/pypi/scikit-optimize
• Conda feedstock - https://github.com/conda-forge/scikit-optimize-feedstock

Install

scikit-optimize requires

• Python >= 3.8
• NumPy (>= 1.20.3)
• SciPy (>= 0.19.1)
• joblib (>= 0.11)
• scikit-learn >= 1.0.0
• matplotlib >= 2.0.0

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 <https://conda-forge.org/>_ 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.

Getting started

Find the minimum of the noisy function f(x) over the range -2 < x < 2 with skopt:

.. code:: python

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:

.. code:: python

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 <https://scikit-optimize.readthedocs.io/en/latest/auto_examples/bayesian-optimization.html>__ and the other examples_.

Development

The library is still experimental and under development. Checkout the next milestone <https://github.com/holgern/scikit-optimize/milestones>__ for the plans for the next release or look at some easy issues <https://github.com/holgern/scikit-optimize/issues?q=is%3Aissue+is%3Aopen+label%3AEasy>__ to get started contributing.

The development version can be installed through:

::

git clone https://github.com/holgern/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 <https://docs.pytest.org/en/latest/mark.html>__. If a tests runs longer than 1 second, it is marked as slow, else as fast.

All contributors are welcome!

Pre-commit-config

Installation

::

    pip install pre-commit


Using homebrew

::

brew install pre-commit

pre-commit --version
pre-commit 2.10.0

Install the git hook scripts

::

    pre-commit install


Run against all the files
~~~~~~~~~~~~~~~~~~~~~~~~~
::

    pre-commit run --all-files
    pre-commit run --show-diff-on-failure --color=always --all-files


Update package rev in pre-commit yaml

::

pre-commit autoupdate
pre-commit run --show-diff-on-failure --color=always --all-files

Making a Release

The release procedure is almost completely automated. By tagging a new release,
CI will build all required packages and push them to PyPI. To make a release,
create a new issue and work through the following checklist:

* [ ] check if the dependencies in `setup.py` are valid or need unpinning,
* [ ] check that the `doc/whats_new/v0.X.rst` is up-to-date,
* [ ] did the last build of master succeed?
* [ ] create a [new release](https://github.com/holgern/scikit-optimize/releases),
* [ ] ping [conda-forge](https://github.com/conda-forge/scikit-optimize-feedstock).

Before making a release, we usually create a release candidate. If the next
release is v0.X, then the release candidate should be tagged v0.Xrc1.
Mark the release candidate as a "pre-release" on GitHub when you tag it.

Made possible by
----------------

The scikit-optimize project was made possible with the support of

.. image:: https://avatars1.githubusercontent.com/u/18165687?v=4&s=128
   :alt: Wild Tree Tech
   :target: https://wildtreetech.com

.. image:: https://i.imgur.com/lgxboT5.jpg
    :alt: NYU Center for Data Science
    :target: https://cds.nyu.edu/

.. image:: https://i.imgur.com/V1VSIvj.jpg
    :alt: NSF
    :target: https://www.nsf.gov

.. image:: https://i.imgur.com/3enQ6S8.jpg
    :alt: Northrop Grumman
    :target: https://www.northropgrumman.com/Pages/default.aspx

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.


.. |pypi| image:: https://img.shields.io/pypi/v/scikit-optimize.svg
   :target: https://pypi.python.org/pypi/scikit-optimize
.. |conda| image:: https://anaconda.org/conda-forge/scikit-optimize/badges/version.svg
   :target: https://anaconda.org/conda-forge/scikit-optimize
.. |CI Status| image:: https://github.com/holgern/scikit-optimize/actions/workflows/ci.yml/badge.svg?branch=main
   :target: https://github.com/holgern/scikit-optimize/actions/workflows/ci.yml?query=branch%3Amain
.. |Logo| image:: https://avatars2.githubusercontent.com/u/18578550?v=4&s=80
.. |binder| image:: https://mybinder.org/badge.svg
   :target: https://mybinder.org/v2/gh/holgern/scikit-optimize/main?filepath=examples
.. |Zenodo DOI| image:: https://zenodo.org/badge/768077165.svg
   :target: https://zenodo.org/doi/10.5281/zenodo.10804382
.. |scipy.optimize| replace:: ``scipy.optimize``
.. _scipy.optimize: https://docs.scipy.org/doc/scipy/reference/optimize.html
.. _examples: https://scikit-optimize.readthedocs.io/en/latest/auto_examples/index.html
.. |codecov| image:: https://codecov.io/gh/holgern/scikit-optimize/graph/badge.svg?token=9Mp32drAPj
   :target: https://codecov.io/gh/holgern/scikit-optimize