skoptimize 0.9.0
Sequential model-based optimization toolbox.
License: BSD 3-clause


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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 <http://docs.scipy.org/doc/scipy/reference/optimize.html>`_.

.. figure:: https://github.com/scikit-optimize/scikit-optimize/blob/master/media/bo-objective.png
   :alt: Approximated objective

Approximated objective function after 50 iterations of ``gp_minimize``.
Plot made using ``skopt.plots.plot_objective``.

Important links
---------------

-  Static documentation - `Static
   documentation <https://scikit-optimize.github.io/>`__
-  Example notebooks - can be found in examples_.
-  Issue tracker -
   https://github.com/scikit-optimize/scikit-optimize/issues
-  Releases - https://pypi.python.org/pypi/scikit-optimize

Install
-------

scikit-optimize requires

* Python >= 3.6
* NumPy (>= 1.13.3)
* SciPy (>= 0.19.1)
* joblib (>= 0.11)
* scikit-learn >= 0.20
* matplotlib >= 2.0.0

You can install the latest release with:
::

    pip install scikit-optimize

This installs an essential version of scikit-optimize. To install scikit-optimize
with plotting functionality, you can instead do:
::

    pip install 'scikit-optimize[plots]'

This will install matplotlib along with scikit-optimize.

In addition 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.github.io/stable/auto_examples/bayesian-optimization.html>`__
and the other examples_.


Development
-----------

The library is still experimental and under heavy development. Checkout
the `next
milestone <https://github.com/scikit-optimize/scikit-optimize/milestones>`__
for the plans for the next release or look at some `easy
issues <https://github.com/scikit-optimize/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/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 <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!


Making a Release
~~~~~~~~~~~~~~~~

The release procedure is almost completely automated. By tagging a new release
travis will build all required packages and push them to PyPI. To make a release
create a new issue and work through the following checklist:

* update the version tag in ``__init__.py``
* update the version tag mentioned in the README
* 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/scikit-optimize/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 in
``__init__.py``. Mark a release candidate as a "pre-release"
on GitHub when you tag it.


Commercial support
------------------

Feel free to `get in touch <mailto:tim@wildtreetech.com>`_ 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

.. image:: https://avatars1.githubusercontent.com/u/18165687?v=4&s=128
   :alt: Wild Tree Tech
   :target: http://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: http://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.


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.. _examples: https://scikit-optimize.github.io/stable/auto_examples/index.html