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scikitoptimize 0.10.2

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.

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:
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

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

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.