scikit-optimize 0.10.2
Sequential model-based optimization toolbox.
License: BSD 3-clause


|Logo|

|pypi| |conda| |CI Status| |binder| |codecov| |Zenodo DOI|

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