scikit-optimize 0.10.2

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-hypernode.com/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-hypernode.com/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:

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 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

pip install pre-commit

brew install pre-commit

pre-commit --version
pre-commit 2.10.0

pre-commit install

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

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

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.