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https://github.com/holgern/scikit-optimize

Sequential model-based optimization with a `scipy.optimize` interface
https://github.com/holgern/scikit-optimize

binder hyperparameter-optimization hyperparameter-search hyperparameter-tuning maschine-learning optimization scientific-visualization scikit-learn sequential-recommendation visualization

Last synced: 27 days ago
JSON representation

Sequential model-based optimization with a `scipy.optimize` interface

Host: GitHub
URL: https://github.com/holgern/scikit-optimize
Owner: holgern
License: bsd-3-clause
Created: 2024-03-06T12:32:17.000Z (8 months ago)
Default Branch: main
Last Pushed: 2024-06-04T18:09:27.000Z (5 months ago)
Last Synced: 2024-09-27T20:22:50.377Z (about 1 month ago)
Topics: binder, hyperparameter-optimization, hyperparameter-search, hyperparameter-tuning, maschine-learning, optimization, scientific-visualization, scikit-learn, sequential-recommendation, visualization
Language: Python
Homepage: http://scikit-optimize.readthedocs.io/
Size: 19.8 MB
Stars: 13
Watchers: 3
Forks: 2
Open Issues: 2
Metadata Files:
- Readme: README.rst
- Changelog: CHANGELOG.md
- Contributing: CONTRIBUTING.md
- License: LICENSE
- Authors: AUTHORS.md

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README

        
|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 `_.

.. 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

   `__

-  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 `_

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 `__

and the other examples_.

Development

-----------

The library is still experimental and under 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/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 `__. 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