Several improvments

.gitattributes

@@ -1 +1 @@
-build_tools/*  linguist-documentation=true
+.github/*  linguist-documentation=true

.github/workflows/ci.yml

@@ -0,0 +1,94 @@
+name: CI
+on:
+  push:
+    branches: [ main ]
+    tags: [ 'v[0-9]+\.[0-9]+.*' ]
+  pull_request: { branches: [main] }
+  workflow_call:
+
+jobs:
+  lint:
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v2
+      - uses: actions/setup-python@v2
+        with:
+          python-version: "3.9"
+      - uses: pre-commit/[email protected]
+
+  test1:
+    runs-on: ${{ matrix.os }}
+    strategy:
+      matrix:
+        os: [ubuntu-latest, windows-latest, macos-latest]
+        python-version: ["3.8", "3.9", "3.10", "3.11"]
+    steps:
+    - uses: actions/checkout@master
+    - name: Setup Python
+      uses: actions/setup-python@master
+      with:
+        python-version: ${{ matrix.python-version }}
+    - name: Install packages
+      run: |
+        pip install -U pip wheel
+        pip install .
+        pip install .[dev,plots]
+        python -c 'import skopt; print(skopt.__version__)'
+    - name: Run tests
+      run: pytest
+
+  test2:
+    runs-on: ${{ matrix.os }}
+    strategy:
+      matrix:
+        os: [ubuntu-latest]
+        python-version: ["3.8", "3.9", "3.10", "3.11"]
+        scikit-learn-version: ["1.4.1.post1", "1.3.2", "1.2.2", "1.1.3", "1.0.2"]
+        numpy-version: ["1.20.3", "1.22.4","1.24.4", "1.26.4"]
+        exclude:
+          - python-version: "3.8"
+            scikit-learn-version: "1.4.1.post1"
+          - python-version: "3.8"
+            numpy-version: "1.26.4"
+          - python-version: "3.11"
+            scikit-learn-version: "1.0.2"
+          - python-version: "3.11"
+            scikit-learn-version: "1.1.3"
+          - python-version: "3.10"
+            numpy-version: "1.20.3"
+          - python-version: "3.11"
+            numpy-version: "1.20.3"
+          - python-version: "3.11"
+            numpy-version: "1.22.4"
+    steps:
+    - uses: actions/checkout@master
+    - name: Setup Python
+      uses: actions/setup-python@master
+      with:
+        python-version: ${{ matrix.python-version }}
+    - name: Install packages
+      run: |
+        pip install -U pip wheel
+        pip install numpy==${{ matrix.numpy-version }}
+        pip install scikit-learn==${{ matrix.scikit-learn-version }}
+        pip install .
+        pip install .[dev,plots]
+        python -c 'import skopt; print(skopt.__version__)'
+    - name: Run tests
+      run: pytest
+
+  doctest:
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v2
+      - uses: actions/setup-python@v2
+        with:
+          python-version: "3.10"
+      - name: Install packages
+        run: |
+            pip install -U pip wheel
+            pip install .
+            pip install .[dev,plots,doc]
+            python -c 'import skopt; print(skopt.__version__)'
+      - name: Run doc test
+        run: cd doc; make doctest

.gitignore

@@ -15,6 +15,7 @@ doc/modules/generated/
 .vscode/
 *.pkl
 *.gz
+*.log
 # mac OS users
 .DS_Store
 

AUTHORS.md

@@ -8,6 +8,7 @@ Scikit-Optimize is a community effort. Contributors include, in alphabetical ord
 * [Alexander Fabisch][AlexanderFabisch]
 * [Thomas Fan][thomasjpfan]
 * [Tim Head][betatim]
+* [kernc][kernc]
 * [Manoj Kumar][mechcoder]
 * [Gilles Louppe][glouppe]
 * [Katie Malone][cmmalone]
@@ -37,3 +38,4 @@ The scikit-optimize logo was a contribution by
 [mirca]: http://mirca.github.io/
 [iaroslav-ai]: http://iaroslav-ai.github.io/
 [holgern]: https://github.com/holgern
+[kernc]: https://github.com/kernc

CONTRIBUTING.md

@@ -1,4 +1,5 @@
-# Contributing
+Contributing
+============
 
 Scikit-Optimize is an open-source project and contributions of all kinds
 are welcome. We believe in this [code of conduct](CONDUCT.md).

README.rst

@@ -6,19 +6,19 @@
 Scikit-Optimize
 ===============
 
-Scikit-Optimize, or ``skopt``, is a simple and efficient library to
-minimize (very) expensive and noisy black-box functions. It implements
+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.
+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/holgern/scikit-optimize/blob/master/media/bo-objective.png
+.. 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``.
@@ -33,9 +33,10 @@ i decided to continue on my own. I still have credentials for pypi, so I will pu
 Important links
 ---------------
 
--  Static documentation - `Static
-   documentation <https://scikit-optimize.github.io/>`__
+-  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
@@ -57,16 +58,16 @@ 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:
+This installs the essentials. To install plotting functionality,
+you can instead do:
 ::
 
     pip install 'scikit-optimize[plots]'
 
-This will install matplotlib along with scikit-optimize.
+This will additionally install Matplotlib.
 
-In addition there is a `conda-forge <https://conda-forge.org/>`_ package
-of scikit-optimize:
+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
@@ -110,7 +111,7 @@ class:
 
 
 Read our `introduction to bayesian
-optimization <https://scikit-optimize.github.io/stable/auto_examples/bayesian-optimization.html>`__
+optimization <https://scikit-optimize.readthedocs.io/en/latest/auto_examples/bayesian-optimization.html>`__
 and the other examples_.
 
 
@@ -149,12 +150,14 @@ Installation
 ~~~~~~~~~~~~
 
 ::
+
     pip install pre-commit
 
 
 Using homebrew
 ~~~~~~~~~~~~~~
 ::
+
     brew install pre-commit
 
     pre-commit --version
@@ -164,19 +167,22 @@ 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
 
@@ -209,7 +215,7 @@ 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
+   :target: https://wildtreetech.com
 
 .. image:: https://i.imgur.com/lgxboT5.jpg
     :alt: NYU Center for Data Science
@@ -221,7 +227,7 @@ The scikit-optimize project was made possible with the support of
 
 .. image:: https://i.imgur.com/3enQ6S8.jpg
     :alt: Northrop Grumman
-    :target: http://www.northropgrumman.com/Pages/default.aspx
+    :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.
@@ -231,9 +237,13 @@ recognition, feel free to add them to the "Made possible by" list.
    :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/master?filepath=examples
+   :target: https://mybinder.org/v2/gh/holgern/scikit-optimize/main?filepath=examples
 .. |Zenodo DOI| image:: https://zenodo.org/badge/54340642.svg
    :target: https://zenodo.org/badge/latestdoi/54340642
-.. _examples: https://scikit-optimize.github.io/stable/auto_examples/index.html
+.. |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

doc/auto_examples/bayesian-optimization.ipynb

@@ -4,7 +4,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "\n# Bayesian optimization with `skopt`\n\nGilles Louppe, Manoj Kumar July 2016.\nReformatted by Holger Nahrstaedt 2020\n\n.. currentmodule:: skopt\n\n## Problem statement\n\nWe are interested in solving\n\n\\begin{align}x^* = arg \\min_x f(x)\\end{align}\n\nunder the constraints that\n\n- $f$ is a black box for which no closed form is known\n  (nor its gradients);\n- $f$ is expensive to evaluate;\n- and evaluations of $y = f(x)$ may be noisy.\n\n**Disclaimer.** If you do not have these constraints, then there\nis certainly a better optimization algorithm than Bayesian optimization.\n\nThis example uses :class:`plots.plot_gaussian_process` which is available\nsince version 0.8.\n\n## Bayesian optimization loop\n\nFor $t=1:T$:\n\n1. Given observations $(x_i, y_i=f(x_i))$ for $i=1:t$, build a\n   probabilistic model for the objective $f$. Integrate out all\n   possible true functions, using Gaussian process regression.\n\n2. optimize a cheap acquisition/utility function $u$ based on the\n   posterior distribution for sampling the next point.\n   $x_{t+1} = arg \\min_x u(x)$\n   Exploit uncertainty to balance exploration against exploitation.\n\n3. Sample the next observation $y_{t+1}$ at $x_{t+1}$.\n\n\n## Acquisition functions\n\nAcquisition functions $u(x)$ specify which sample $x$: should be\ntried next:\n\n- Expected improvement (default):\n  $-EI(x) = -\\mathbb{E} [f(x) - f(x_t^+)]$\n- Lower confidence bound: $LCB(x) = \\mu_{GP}(x) + \\kappa \\sigma_{GP}(x)$\n- Probability of improvement: $-PI(x) = -P(f(x) \\geq f(x_t^+) + \\kappa)$\n\nwhere $x_t^+$ is the best point observed so far.\n\nIn most cases, acquisition functions provide knobs (e.g., $\\kappa$) for\ncontrolling the exploration-exploitation trade-off.\n- Search in regions where $\\mu_{GP}(x)$ is high (exploitation)\n- Probe regions where uncertainty $\\sigma_{GP}(x)$ is high (exploration)\n"
+        "\n# Bayesian optimization with `skopt`\n\nGilles Louppe, Manoj Kumar July 2016.\nReformatted by Holger Nahrstaedt 2020\n\n.. currentmodule:: skopt\n\n## Problem statement\n\nWe are interested in solving\n\n\\begin{align}x^* = arg \\\\min_x f(x)\\end{align}\n\nunder the constraints that\n\n- $f$ is a black box for which no closed form is known\n  (nor its gradients);\n- $f$ is expensive to evaluate;\n- and evaluations of $y = f(x)$ may be noisy.\n\n**Disclaimer.** If you do not have these constraints, then there\nis certainly a better optimization algorithm than Bayesian optimization.\n\nThis example uses :class:`plots.plot_gaussian_process` which is available\nsince version 0.8.\n\n## Bayesian optimization loop\n\nFor $t=1:T$:\n\n1. Given observations $(x_i, y_i=f(x_i))$ for $i=1:t$, build a\n   probabilistic model for the objective $f$. Integrate out all\n   possible true functions, using Gaussian process regression.\n\n2. optimize a cheap acquisition/utility function $u$ based on the\n   posterior distribution for sampling the next point.\n   $x_{t+1} = arg \\\\min_x u(x)$\n   Exploit uncertainty to balance exploration against exploitation.\n\n3. Sample the next observation $y_{t+1}$ at $x_{t+1}$.\n\n\n## Acquisition functions\n\nAcquisition functions $u(x)$ specify which sample $x$: should be\ntried next:\n\n- Expected improvement (default):\n  $-EI(x) = -\\\\mathbb{E} [f(x) - f(x_t^+)]$\n- Lower confidence bound: $LCB(x) = \\\\mu_{GP}(x) + \\\\kappa \\\\sigma_{GP}(x)$\n- Probability of improvement: $-PI(x) = -P(f(x) \\\\geq f(x_t^+) + \\\\kappa)$\n\nwhere $x_t^+$ is the best point observed so far.\n\nIn most cases, acquisition functions provide knobs (e.g., $\\\\kappa$) for\ncontrolling the exploration-exploitation trade-off.\n- Search in regions where $\\\\mu_{GP}(x)$ is high (exploitation)\n- Probe regions where uncertainty $\\\\sigma_{GP}(x)$ is high (exploration)\n"
       ]
     },
     {

doc/auto_examples/bayesian-optimization.py

@@ -1,4 +1,4 @@
-"""
+r"""
 ==================================
 Bayesian optimization with `skopt`
 ==================================

doc/auto_examples/bayesian-optimization.py.md5

@@ -1 +1 @@
-d7c44aa29f20fbdb1c77eecf706db4d9
+7832badf66cceec028004ff84be09974