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.. _contributing:
============
Contributing
============
.. currentmodule:: sklearn
This project is a community effort, and everyone is welcome to
contribute.
The project is hosted on https://github.com/scikit-learn/scikit-learn
The decision making process and governance structure of scikit-learn is laid
out in the governance document: :ref:`governance`.
Scikit-learn is somewhat :ref:`selective <selectiveness>` when it comes to
adding new algorithms, and the best way to contribute and to help the project
is to start working on known issues.
See :ref:`new_contributors` to get started.
.. topic:: **Our community, our values**
We are a community based on openness and friendly, didactic,
discussions.
We aspire to treat everybody equally, and value their contributions. We
are particularly seeking people from underrepresented backgrounds in Open
Source Software and scikit-learn in particular to participate and
contribute their expertise and experience.
Decisions are made based on technical merit and consensus.
Code is not the only way to help the project. Reviewing pull
requests, answering questions to help others on mailing lists or
issues, organizing and teaching tutorials, working on the website,
improving the documentation, are all priceless contributions.
We abide by the principles of openness, respect, and consideration of
others of the Python Software Foundation:
https://www.python.org/psf/codeofconduct/
In case you experience issues using this package, do not hesitate to submit a
ticket to the
`GitHub issue tracker
<https://github.com/scikit-learn/scikit-learn/issues>`_. You are also
welcome to post feature requests or pull requests.
Ways to contribute
==================
There are many ways to contribute to scikit-learn, with the most common ones
being contribution of code or documentation to the project. Improving the
documentation is no less important than improving the library itself. If you
find a typo in the documentation, or have made improvements, do not hesitate to
send an email to the mailing list or preferably submit a GitHub pull request.
Full documentation can be found under the doc/ directory.
But there are many other ways to help. In particular helping to
:ref:`improve, triage, and investigate issues <bug_triaging>` and
:ref:`reviewing other developers' pull requests <code_review>` are very
valuable contributions that decrease the burden on the project
maintainers.
Another way to contribute is to report issues you're facing, and give a "thumbs
up" on issues that others reported and that are relevant to you. It also helps
us if you spread the word: reference the project from your blog and articles,
link to it from your website, or simply star to say "I use it":
In case a contribution/issue involves changes to the API principles
or changes to dependencies or supported versions, it must be backed by a
:ref:`slep`, where a SLEP must be submitted as a pull-request to
`enhancement proposals <https://scikit-learn-enhancement-proposals.readthedocs.io>`_
using the `SLEP template <https://scikit-learn-enhancement-proposals.readthedocs.io/en/latest/slep_template.html>`_
and follows the decision-making process outlined in :ref:`governance`.
.. raw:: html
<a class="github-button" href="https://github.com/scikit-learn/scikit-learn"
data-icon="octicon-star" data-size="large" data-show-count="true" aria-label="Star
scikit-learn/scikit-learn on GitHub">Star</a>
<script async defer src="https://buttons.github.io/buttons.js"></script>
.. topic:: Contributing to related projects
Scikit-learn thrives in an ecosystem of several related projects, which also
may have relevant issues to work on, including smaller projects such as:
* `scikit-learn-contrib <https://github.com/search?q=org%3Ascikit-learn-contrib+is%3Aissue+is%3Aopen+sort%3Aupdated-desc&type=Issues>`__
* `joblib <https://github.com/joblib/joblib/issues>`__
* `sphinx-gallery <https://github.com/sphinx-gallery/sphinx-gallery/issues>`__
* `numpydoc <https://github.com/numpy/numpydoc/issues>`__
* `liac-arff <https://github.com/renatopp/liac-arff>`__
and larger projects:
* `numpy <https://github.com/numpy/numpy/issues>`__
* `scipy <https://github.com/scipy/scipy/issues>`__
* `matplotlib <https://github.com/matplotlib/matplotlib/issues>`__
* and so on.
Look for issues marked "help wanted" or similar.
Helping these projects may help Scikit-learn too.
See also :ref:`related_projects`.
Submitting a bug report or a feature request
============================================
We use GitHub issues to track all bugs and feature requests; feel free to open
an issue if you have found a bug or wish to see a feature implemented.
In case you experience issues using this package, do not hesitate to submit a
ticket to the
`Bug Tracker <https://github.com/scikit-learn/scikit-learn/issues>`_. You are
also welcome to post feature requests or pull requests.
It is recommended to check that your issue complies with the
following rules before submitting:
- Verify that your issue is not being currently addressed by other
`issues <https://github.com/scikit-learn/scikit-learn/issues?q=>`_
or `pull requests <https://github.com/scikit-learn/scikit-learn/pulls?q=>`_.
- If you are submitting an algorithm or feature request, please verify that
the algorithm fulfills our
`new algorithm requirements
<http://scikit-learn.org/stable/faq.html#what-are-the-inclusion-criteria-for-new-algorithms>`_.
- If you are submitting a bug report, we strongly encourage you to follow the guidelines in
:ref:`filing_bugs`.
.. _filing_bugs:
How to make a good bug report
-----------------------------
When you submit an issue to `Github
<https://github.com/scikit-learn/scikit-learn/issues>`__, please do your best to
follow these guidelines! This will make it a lot easier to provide you with good
feedback:
- The ideal bug report contains a :ref:`short reproducible code snippet
<minimal_reproducer>`, this way
anyone can try to reproduce the bug easily (see `this
<https://stackoverflow.com/help/mcve>`_ for more details). If your snippet is
longer than around 50 lines, please link to a `gist
<https://gist.github.com>`_ or a github repo.
- If not feasible to include a reproducible snippet, please be specific about
what **estimators and/or functions are involved and the shape of the data**.
- If an exception is raised, please **provide the full traceback**.
- Please include your **operating system type and version number**, as well as
your **Python, scikit-learn, numpy, and scipy versions**. This information
can be found by running the following code snippet::
>>> import sklearn
>>> sklearn.show_versions() # doctest: +SKIP
- Please ensure all **code snippets and error messages are formatted in
appropriate code blocks**. See `Creating and highlighting code blocks
<https://help.github.com/articles/creating-and-highlighting-code-blocks>`_
for more details.
If you want to help curate issues, read :ref:`the following
<bug_triaging>`.
Contributing code
=================
.. note::
To avoid duplicating work, it is highly advised that you search through the
`issue tracker <https://github.com/scikit-learn/scikit-learn/issues>`_ and
the `PR list <https://github.com/scikit-learn/scikit-learn/pulls>`_.
If in doubt about duplicated work, or if you want to work on a non-trivial
feature, it's recommended to first open an issue in
the `issue tracker <https://github.com/scikit-learn/scikit-learn/issues>`_
to get some feedbacks from core developers.
One easy way to find an issue to work on is by applying the "help wanted"
label in your search. This lists all the issues that have been unclaimed
so far. In order to claim an issue for yourself, please comment exactly
``/take`` on it for the CI to automatically assign the issue to you.
Video resources
---------------
These videos are step-by-step introductions on how to contribute to
scikit-learn, and are a great companion to the following text guidelines.
Please make sure to still check our guidelines below, since they describe our
latest up-to-date workflow.
- Crash Course in Contributing to Scikit-Learn & Open Source Projects:
`Video <https://youtu.be/5OL8XoMMOfA>`__,
`Transcript
<https://github.com/data-umbrella/event-transcripts/blob/main/2020/05-andreas-mueller-contributing.md>`__
- Example of Submitting a Pull Request to scikit-learn:
`Video <https://youtu.be/PU1WyDPGePI>`__,
`Transcript
<https://github.com/data-umbrella/event-transcripts/blob/main/2020/06-reshama-shaikh-sklearn-pr.md>`__
- Sprint-specific instructions and practical tips:
`Video <https://youtu.be/p_2Uw2BxdhA>`__,
`Transcript
<https://github.com/data-umbrella/data-umbrella-scikit-learn-sprint/blob/master/3_transcript_ACM_video_vol2.md>`__
- 3 Components of Reviewing a Pull Request:
`Video <https://youtu.be/dyxS9KKCNzA>`__,
`Transcript
<https://github.com/data-umbrella/event-transcripts/blob/main/2021/27-thomas-pr.md>`__
.. note::
In January 2021, the default branch name changed from ``master`` to ``main``
for the scikit-learn GitHub repository to use more inclusive terms.
These videos were created prior to the renaming of the branch.
For contributors who are viewing these videos to set up their
working environment and submitting a PR, ``master`` should be replaced to ``main``.
How to contribute
-----------------
The preferred way to contribute to scikit-learn is to fork the `main
repository <https://github.com/scikit-learn/scikit-learn/>`__ on GitHub,
then submit a "pull request" (PR).
In the first few steps, we explain how to locally install scikit-learn, and
how to set up your git repository:
1. `Create an account <https://github.com/join>`_ on
GitHub if you do not already have one.
2. Fork the `project repository
<https://github.com/scikit-learn/scikit-learn>`__: click on the 'Fork'
button near the top of the page. This creates a copy of the code under your
account on the GitHub user account. For more details on how to fork a
repository see `this guide <https://help.github.com/articles/fork-a-repo/>`_.
3. Clone your fork of the scikit-learn repo from your GitHub account to your
local disk:
.. prompt:: bash $
git clone git@github.com:YourLogin/scikit-learn.git # add --depth 1 if your connection is slow
cd scikit-learn
3. Follow steps 2-7 in :ref:`install_bleeding_edge` to build scikit-learn in
development mode and return to this document.
4. Install the development dependencies:
.. prompt:: bash $
pip install pytest pytest-cov flake8 mypy numpydoc black==22.3.0
.. _upstream:
5. Add the ``upstream`` remote. This saves a reference to the main
scikit-learn repository, which you can use to keep your repository
synchronized with the latest changes:
.. prompt:: bash $
git remote add upstream git@github.com:scikit-learn/scikit-learn.git
6. Check that the `upstream` and `origin` remote aliases are configured correctly
by running `git remote -v` which should display::
origin git@github.com:YourLogin/scikit-learn.git (fetch)
origin git@github.com:YourLogin/scikit-learn.git (push)
upstream git@github.com:scikit-learn/scikit-learn.git (fetch)
upstream git@github.com:scikit-learn/scikit-learn.git (push)
You should now have a working installation of scikit-learn, and your git
repository properly configured. The next steps now describe the process of
modifying code and submitting a PR:
7. Synchronize your ``main`` branch with the ``upstream/main`` branch,
more details on `GitHub Docs <https://docs.github.com/en/github/collaborating-with-issues-and-pull-requests/syncing-a-fork>`_:
.. prompt:: bash $
git checkout main
git fetch upstream
git merge upstream/main
8. Create a feature branch to hold your development changes:
.. prompt:: bash $
git checkout -b my_feature
and start making changes. Always use a feature branch. It's good
practice to never work on the ``main`` branch!
9. (**Optional**) Install `pre-commit <https://pre-commit.com/#install>`_ to
run code style checks before each commit:
.. prompt:: bash $
pip install pre-commit
pre-commit install
pre-commit checks can be disabled for a particular commit with
`git commit -n`.
10. Develop the feature on your feature branch on your computer, using Git to
do the version control. When you're done editing, add changed files using
``git add`` and then ``git commit``:
.. prompt:: bash $
git add modified_files
git commit
to record your changes in Git, then push the changes to your GitHub
account with:
.. prompt:: bash $
git push -u origin my_feature
11. Follow `these
<https://help.github.com/articles/creating-a-pull-request-from-a-fork>`_
instructions to create a pull request from your fork. This will send an
email to the committers. You may want to consider sending an email to the
mailing list for more visibility.
.. note::
If you are modifying a Cython module, you have to re-compile after
modifications and before testing them:
.. prompt:: bash $
pip install --no-build-isolation -e .
Use the ``--no-build-isolation`` flag to avoid compiling the whole project
each time, only the files you have modified.
It is often helpful to keep your local feature branch synchronized with the
latest changes of the main scikit-learn repository:
.. prompt:: bash $
git fetch upstream
git merge upstream/main
Subsequently, you might need to solve the conflicts. You can refer to the
`Git documentation related to resolving merge conflict using the command
line
<https://help.github.com/articles/resolving-a-merge-conflict-using-the-command-line/>`_.
.. topic:: Learning git:
The `Git documentation <https://git-scm.com/documentation>`_ and
http://try.github.io are excellent resources to get started with git,
and understanding all of the commands shown here.
.. _pr_checklist:
Pull request checklist
----------------------
Before a PR can be merged, it needs to be approved by two core developers.
Please prefix the title of your pull request with ``[MRG]`` if the
contribution is complete and should be subjected to a detailed review. An
incomplete contribution -- where you expect to do more work before receiving
a full review -- should be prefixed ``[WIP]`` (to indicate a work in
progress) and changed to ``[MRG]`` when it matures. WIPs may be useful to:
indicate you are working on something to avoid duplicated work, request
broad review of functionality or API, or seek collaborators. WIPs often
benefit from the inclusion of a `task list
<https://github.com/blog/1375-task-lists-in-gfm-issues-pulls-comments>`_ in
the PR description.
In order to ease the reviewing process, we recommend that your contribution
complies with the following rules before marking a PR as ``[MRG]``. The
**bolded** ones are especially important:
1. **Give your pull request a helpful title** that summarises what your
contribution does. This title will often become the commit message once
merged so it should summarise your contribution for posterity. In some
cases "Fix <ISSUE TITLE>" is enough. "Fix #<ISSUE NUMBER>" is never a
good title.
2. **Make sure your code passes the tests**. The whole test suite can be run
with `pytest`, but it is usually not recommended since it takes a long
time. It is often enough to only run the test related to your changes:
for example, if you changed something in
`sklearn/linear_model/_logistic.py`, running the following commands will
usually be enough:
- `pytest sklearn/linear_model/_logistic.py` to make sure the doctest
examples are correct
- `pytest sklearn/linear_model/tests/test_logistic.py` to run the tests
specific to the file
- `pytest sklearn/linear_model` to test the whole
:mod:`~sklearn.linear_model` module
- `pytest doc/modules/linear_model.rst` to make sure the user guide
examples are correct.
- `pytest sklearn/tests/test_common.py -k LogisticRegression` to run all our
estimator checks (specifically for `LogisticRegression`, if that's the
estimator you changed).
There may be other failing tests, but they will be caught by the CI so
you don't need to run the whole test suite locally. For guidelines on how
to use ``pytest`` efficiently, see the :ref:`pytest_tips`.
3. **Make sure your code is properly commented and documented**, and **make
sure the documentation renders properly**. To build the documentation, please
refer to our :ref:`contribute_documentation` guidelines. The CI will also
build the docs: please refer to :ref:`generated_doc_CI`.
4. **Tests are necessary for enhancements to be
accepted**. Bug-fixes or new features should be provided with
`non-regression tests
<https://en.wikipedia.org/wiki/Non-regression_testing>`_. These tests
verify the correct behavior of the fix or feature. In this manner, further
modifications on the code base are granted to be consistent with the
desired behavior. In the case of bug fixes, at the time of the PR, the
non-regression tests should fail for the code base in the ``main`` branch
and pass for the PR code.
5. Run `black` to auto-format your code.
.. prompt:: bash $
black .
See black's
`editor integration documentation <https://black.readthedocs.io/en/stable/integrations/editors.html>`_
to configure your editor to run `black`.
6. **Make sure that your PR does not add PEP8 violations**. To check the
code that you changed, you can run the following command (see
:ref:`above <upstream>` to set up the ``upstream`` remote):
.. prompt:: bash $
git diff upstream/main -u -- "*.py" | flake8 --diff
or `make flake8-diff` which should work on unix-like system.
7. Follow the :ref:`coding-guidelines`.
8. When applicable, use the validation tools and scripts in the
``sklearn.utils`` submodule. A list of utility routines available
for developers can be found in the :ref:`developers-utils` page.
9. Often pull requests resolve one or more other issues (or pull requests).
If merging your pull request means that some other issues/PRs should
be closed, you should `use keywords to create link to them
<https://github.com/blog/1506-closing-issues-via-pull-requests/>`_
(e.g., ``Fixes #1234``; multiple issues/PRs are allowed as long as each
one is preceded by a keyword). Upon merging, those issues/PRs will
automatically be closed by GitHub. If your pull request is simply
related to some other issues/PRs, create a link to them without using
the keywords (e.g., ``See also #1234``).
10. PRs should often substantiate the change, through benchmarks of
performance and efficiency (see :ref:`monitoring_performances`) or through
examples of usage. Examples also illustrate the features and intricacies of
the library to users. Have a look at other examples in the `examples/
<https://github.com/scikit-learn/scikit-learn/tree/main/examples>`_
directory for reference. Examples should demonstrate why the new
functionality is useful in practice and, if possible, compare it to other
methods available in scikit-learn.
11. New features have some maintenance overhead. We expect PR authors
to take part in the maintenance for the code they submit, at least
initially. New features need to be illustrated with narrative
documentation in the user guide, with small code snippets.
If relevant, please also add references in the literature, with PDF links
when possible.
12. The user guide should also include expected time and space complexity
of the algorithm and scalability, e.g. "this algorithm can scale to a
large number of samples > 100000, but does not scale in dimensionality:
n_features is expected to be lower than 100".
You can also check our :ref:`code_review` to get an idea of what reviewers
will expect.
You can check for common programming errors with the following tools:
* Code with a good unittest coverage (at least 80%, better 100%), check
with:
.. prompt:: bash $
pip install pytest pytest-cov
pytest --cov sklearn path/to/tests_for_package
see also :ref:`testing_coverage`
Run static analysis with `mypy`:
.. prompt:: bash $
mypy sklearn
must not produce new errors in your pull request. Using `# type: ignore`
annotation can be a workaround for a few cases that are not supported by
mypy, in particular,
- when importing C or Cython modules
- on properties with decorators
Bonus points for contributions that include a performance analysis with
a benchmark script and profiling output (see :ref:`monitoring_performances`).
Also check out the :ref:`performance-howto` guide for more details on
profiling and Cython optimizations.
.. note::
The current state of the scikit-learn code base is not compliant with
all of those guidelines, but we expect that enforcing those constraints
on all new contributions will get the overall code base quality in the
right direction.
.. note::
For two very well documented and more detailed guides on development
workflow, please pay a visit to the `Scipy Development Workflow
<https://docs.scipy.org/doc/scipy/reference/dev/contributor/development_workflow.html>`_ -
and the `Astropy Workflow for Developers
<https://astropy.readthedocs.io/en/latest/development/workflow/development_workflow.html>`_
sections.
Continuous Integration (CI)
^^^^^^^^^^^^^^^^^^^^^^^^^^^
* Azure pipelines are used for testing scikit-learn on Linux, Mac and Windows,
with different dependencies and settings.
* CircleCI is used to build the docs for viewing, for linting with flake8, and
for testing with ARM64 / aarch64 on Linux
Please note that if one of the following markers appear in the latest commit
message, the following actions are taken.
====================== ===================
Commit Message Marker Action Taken by CI
---------------------- -------------------
[ci skip] CI is skipped completely
[cd build] CD is run (wheels and source distribution are built)
[cd build gh] CD is run only for GitHub Actions
[lint skip] Azure pipeline skips linting
[scipy-dev] Build & test with our dependencies (numpy, scipy, etc ...) development builds
[icc-build] Build & test with the Intel C compiler (ICC)
[pypy] Build & test with PyPy
[doc skip] Docs are not built
[doc quick] Docs built, but excludes example gallery plots
[doc build] Docs built including example gallery plots (very long)
====================== ===================
Note that, by default, the documentation is built but only the examples
that are directly modified by the pull request are executed.
.. _stalled_pull_request:
Stalled pull requests
^^^^^^^^^^^^^^^^^^^^^
As contributing a feature can be a lengthy process, some
pull requests appear inactive but unfinished. In such a case, taking
them over is a great service for the project.
A good etiquette to take over is:
* **Determine if a PR is stalled**
* A pull request may have the label "stalled" or "help wanted" if we
have already identified it as a candidate for other contributors.
* To decide whether an inactive PR is stalled, ask the contributor if
she/he plans to continue working on the PR in the near future.
Failure to respond within 2 weeks with an activity that moves the PR
forward suggests that the PR is stalled and will result in tagging
that PR with "help wanted".
Note that if a PR has received earlier comments on the contribution
that have had no reply in a month, it is safe to assume that the PR
is stalled and to shorten the wait time to one day.
After a sprint, follow-up for un-merged PRs opened during sprint will
be communicated to participants at the sprint, and those PRs will be
tagged "sprint". PRs tagged with "sprint" can be reassigned or
declared stalled by sprint leaders.
* **Taking over a stalled PR**: To take over a PR, it is important to
comment on the stalled PR that you are taking over and to link from the
new PR to the old one. The new PR should be created by pulling from the
old one.
Stalled and Unclaimed Issues
^^^^^^^^^^^^^^^^^^^^^^^^^^^^
Generally speaking, issues which are up for grabs will have a
`"help wanted" <https://github.com/scikit-learn/scikit-learn/labels/help%20wanted>`_.
tag. However, not all issues which need contributors will have this tag,
as the "help wanted" tag is not always up-to-date with the state
of the issue. Contributors can find issues which are still up for grabs
using the following guidelines:
* First, to **determine if an issue is claimed**:
* Check for linked pull requests
* Check the conversation to see if anyone has said that they're working on
creating a pull request
* If a contributor comments on an issue to say they are working on it,
a pull request is expected within 2 weeks (new contributor) or 4 weeks
(contributor or core dev), unless an larger time frame is explicitly given.
Beyond that time, another contributor can take the issue and make a
pull request for it. We encourage contributors to comment directly on the
stalled or unclaimed issue to let community members know that they will be
working on it.
* If the issue is linked to a :ref:`stalled pull request <stalled_pull_request>`,
we recommend that contributors follow the procedure
described in the :ref:`stalled_pull_request`
section rather than working directly on the issue.
.. _new_contributors:
Issues for New Contributors
---------------------------
New contributors should look for the following tags when looking for issues. We
strongly recommend that new contributors tackle "easy" issues first: this helps
the contributor become familiar with the contribution workflow, and for the core
devs to become acquainted with the contributor; besides which, we frequently
underestimate how easy an issue is to solve!
.. topic:: good first issue tag
A great way to start contributing to scikit-learn is to pick an item from
the list of `good first issues
<https://github.com/scikit-learn/scikit-learn/labels/good%20first%20issue>`_
in the issue tracker. Resolving these issues allow you to start contributing
to the project without much prior knowledge. If you have already contributed
to scikit-learn, you should look at Easy issues instead.
.. topic:: Easy tag
If you have already contributed to scikit-learn, another great way to contribute
to scikit-learn is to pick an item from the list of `Easy issues
<https://github.com/scikit-learn/scikit-learn/labels/Easy>`_ in the issue
tracker. Your assistance in this area will be greatly appreciated by the
more experienced developers as it helps free up their time to concentrate on
other issues.
.. topic:: help wanted tag
We often use the help wanted tag to mark issues regardless of difficulty. Additionally,
we use the help wanted tag to mark Pull Requests which have been abandoned
by their original contributor and are available for someone to pick up where the original
contributor left off. The list of issues with the help wanted tag can be found
`here <https://github.com/scikit-learn/scikit-learn/labels/help%20wanted>`_.
Note that not all issues which need contributors will have this tag.
.. _contribute_documentation:
Documentation
=============
We are glad to accept any sort of documentation: function docstrings,
reStructuredText documents (like this one), tutorials, etc. reStructuredText
documents live in the source code repository under the ``doc/`` directory.
You can edit the documentation using any text editor, and then generate the
HTML output by typing ``make`` from the ``doc/`` directory. Alternatively,
``make html`` may be used to generate the documentation **with** the example
gallery (which takes quite some time). The resulting HTML files will be
placed in ``_build/html/stable`` and are viewable in a web browser.
Building the documentation
--------------------------
First, make sure you have :ref:`properly installed <install_bleeding_edge>`
the development version.
..
packaging is not needed once setuptools starts shipping packaging>=17.0
Building the documentation requires installing some additional packages:
.. prompt:: bash $
pip install sphinx sphinx-gallery numpydoc matplotlib Pillow pandas \
scikit-image packaging seaborn sphinx-prompt \
sphinxext-opengraph
To build the documentation, you need to be in the ``doc`` folder:
.. prompt:: bash $
cd doc
In the vast majority of cases, you only need to generate the full web site,
without the example gallery:
.. prompt:: bash $
make
The documentation will be generated in the ``_build/html/stable`` directory.
To also generate the example gallery you can use:
.. prompt:: bash $
make html
This will run all the examples, which takes a while. If you only want to
generate a few examples, you can use:
.. prompt:: bash $
EXAMPLES_PATTERN=your_regex_goes_here make html
This is particularly useful if you are modifying a few examples.
Set the environment variable `NO_MATHJAX=1` if you intend to view
the documentation in an offline setting.
To build the PDF manual, run:
.. prompt:: bash $
make latexpdf
.. warning:: **Sphinx version**
While we do our best to have the documentation build under as many
versions of Sphinx as possible, the different versions tend to
behave slightly differently. To get the best results, you should
use the same version as the one we used on CircleCI. Look at this
`github search <https://github.com/search?utf8=%E2%9C%93&q=sphinx+repo%3Ascikit-learn%2Fscikit-learn+extension%3Ash+path%3Abuild_tools%2Fcircle&type=Code>`_
to know the exact version.
Guidelines for writing documentation
------------------------------------
It is important to keep a good compromise between mathematical and algorithmic
details, and give intuition to the reader on what the algorithm does.
Basically, to elaborate on the above, it is best to always
start with a small paragraph with a hand-waving explanation of what the
method does to the data. Then, it is very helpful to point out why the feature is
useful and when it should be used - the latter also including "big O"
(:math:`O\left(g\left(n\right)\right)`) complexities of the algorithm, as opposed
to just *rules of thumb*, as the latter can be very machine-dependent. If those
complexities are not available, then rules of thumb may be provided instead.
Secondly, a generated figure from an example (as mentioned in the previous
paragraph) should then be included to further provide some intuition.
Next, one or two small code examples to show its use can be added.
Next, any math and equations, followed by references,
can be added to further the documentation. Not starting the
documentation with the maths makes it more friendly towards
users that are just interested in what the feature will do, as
opposed to how it works "under the hood".
Finally, follow the formatting rules below to make it consistently good:
* Add "See Also" in docstrings for related classes/functions.
* "See Also" in docstrings should be one line per reference,
with a colon and an explanation, for example::
See Also
--------
SelectKBest : Select features based on the k highest scores.
SelectFpr : Select features based on a false positive rate test.
* When documenting the parameters and attributes, here is a list of some
well-formatted examples::
n_clusters : int, default=3
The number of clusters detected by the algorithm.
some_param : {'hello', 'goodbye'}, bool or int, default=True
The parameter description goes here, which can be either a string
literal (either `hello` or `goodbye`), a bool, or an int. The default
value is True.
array_parameter : {array-like, sparse matrix} of shape (n_samples, n_features) or (n_samples,)
This parameter accepts data in either of the mentioned forms, with one
of the mentioned shapes. The default value is
`np.ones(shape=(n_samples,))`.
list_param : list of int
typed_ndarray : ndarray of shape (n_samples,), dtype=np.int32
sample_weight : array-like of shape (n_samples,), default=None
multioutput_array : ndarray of shape (n_samples, n_classes) or list of such arrays
In general have the following in mind:
1. Use Python basic types. (``bool`` instead of ``boolean``)
2. Use parenthesis for defining shapes: ``array-like of shape (n_samples,)``
or ``array-like of shape (n_samples, n_features)``
3. For strings with multiple options, use brackets:
``input: {'log', 'squared', 'multinomial'}``
4. 1D or 2D data can be a subset of
``{array-like, ndarray, sparse matrix, dataframe}``. Note that ``array-like``
can also be a ``list``, while ``ndarray`` is explicitly only a ``numpy.ndarray``.
5. Specify ``dataframe`` when "frame-like" features are being used, such
as the column names.
6. When specifying the data type of a list, use ``of`` as a delimiter:
``list of int``. When the parameter supports arrays giving details
about the shape and/or data type and a list of such arrays, you can
use one of ``array-like of shape (n_samples,) or list of such arrays``.
7. When specifying the dtype of an ndarray, use e.g. ``dtype=np.int32``
after defining the shape:
``ndarray of shape (n_samples,), dtype=np.int32``. You can specify
multiple dtype as a set:
``array-like of shape (n_samples,), dtype={np.float64, np.float32}``.
If one wants to mention arbitrary precision, use `integral` and
`floating` rather than the Python dtype `int` and `float`. When both
`int` and `floating` are supported, there is no need to specify the
dtype.
8. When the default is ``None``, ``None`` only needs to be specified at the
end with ``default=None``. Be sure to include in the docstring, what it
means for the parameter or attribute to be ``None``.
* For unwritten formatting rules, try to follow existing good works:
* When bibliographic references are available with `arxiv <https://arxiv.org/>`_
or `Digital Object Identifier <https://www.doi.org/>`_ identification numbers,
use the sphinx directives `:arxiv:` or `:doi:`. For example, see references in
:ref:`Spectral Clustering Graphs <spectral_clustering_graph>`.
* For "References" in docstrings, see the Silhouette Coefficient
(:func:`sklearn.metrics.silhouette_score`).
* When editing reStructuredText (``.rst``) files, try to keep line length under
80 characters when possible (exceptions include links and tables).
* Do not modify sphinx labels as this would break existing cross references and
external links pointing to specific sections in the
scikit-learn documentation.
* Before submitting your pull request check if your modifications have
introduced new sphinx warnings and try to fix them.
.. _generated_doc_CI:
Generated documentation on CircleCI
-----------------------------------
When you change the documentation in a pull request, CircleCI automatically
builds it. To view the documentation generated by CircleCI, simply go at the
bottom of your PR page and look for the "ci/circleci: doc artifact" link.
.. _testing_coverage:
Testing and improving test coverage
===================================
High-quality `unit testing <https://en.wikipedia.org/wiki/Unit_testing>`_
is a corner-stone of the scikit-learn development process. For this
purpose, we use the `pytest <https://docs.pytest.org>`_
package. The tests are functions appropriately named, located in `tests`
subdirectories, that check the validity of the algorithms and the
different options of the code.
Running `pytest` in a folder will run all the tests of the corresponding
subpackages. For a more detailed `pytest` workflow, please refer to the
:ref:`pr_checklist`.
We expect code coverage of new features to be at least around 90%.
Writing matplotlib related tests
--------------------------------
Test fixtures ensure that a set of tests will be executing with the appropriate
initialization and cleanup. The scikit-learn test suite implements a fixture
which can be used with ``matplotlib``.
``pyplot``
The ``pyplot`` fixture should be used when a test function is dealing with
``matplotlib``. ``matplotlib`` is a soft dependency and is not required.
This fixture is in charge of skipping the tests if ``matplotlib`` is not
installed. In addition, figures created during the tests will be
automatically closed once the test function has been executed.
To use this fixture in a test function, one needs to pass it as an
argument::
def test_requiring_mpl_fixture(pyplot):
# you can now safely use matplotlib
Workflow to improve test coverage
---------------------------------
To test code coverage, you need to install the `coverage
<https://pypi.org/project/coverage/>`_ package in addition to pytest.
1. Run 'make test-coverage'. The output lists for each file the line
numbers that are not tested.
2. Find a low hanging fruit, looking at which lines are not tested,
write or adapt a test specifically for these lines.
3. Loop.
.. _monitoring_performances:
Monitoring performance
======================
*This section is heavily inspired from the* `pandas documentation
<https://pandas.pydata.org/docs/development/contributing.html#running-the-performance-test-suite>`_.
When proposing changes to the existing code base, it's important to make sure
that they don't introduce performance regressions. Scikit-learn uses
`asv benchmarks <https://github.com/airspeed-velocity/asv>`_ to monitor the
performance of a selection of common estimators and functions. You can view
these benchmarks on the `scikit-learn benchmark page <https://scikit-learn.org/scikit-learn-benchmarks>`_.
The corresponding benchmark suite can be found in the `scikit-learn/asv_benchmarks` directory.
To use all features of asv, you will need either `conda` or `virtualenv`. For
more details please check the `asv installation webpage
<https://asv.readthedocs.io/en/latest/installing.html>`_.
First of all you need to install the development version of asv:
.. prompt:: bash $
pip install git+https://github.com/airspeed-velocity/asv
and change your directory to `asv_benchmarks/`:
.. prompt:: bash $
cd asv_benchmarks/
The benchmark suite is configured to run against your local clone of
scikit-learn. Make sure it is up to date:
.. prompt:: bash $
git fetch upstream
In the benchmark suite, the benchmarks are organized following the same
structure as scikit-learn. For example, you can compare the performance of a
specific estimator between ``upstream/main`` and the branch you are working on:
.. prompt:: bash $
asv continuous -b LogisticRegression upstream/main HEAD
The command uses conda by default for creating the benchmark environments. If
you want to use virtualenv instead, use the `-E` flag:
.. prompt:: bash $
asv continuous -E virtualenv -b LogisticRegression upstream/main HEAD
You can also specify a whole module to benchmark:
.. prompt:: bash $
asv continuous -b linear_model upstream/main HEAD
You can replace `HEAD` by any local branch. By default it will only report the
benchmarks that have change by at least 10%. You can control this ratio with
the `-f` flag.
To run the full benchmark suite, simply remove the `-b` flag :
.. prompt:: bash $
asv continuous upstream/main HEAD
However this can take up to two hours. The `-b` flag also accepts a regular
expression for a more complex subset of benchmarks to run.
To run the benchmarks without comparing to another branch, use the `run`
command:
.. prompt:: bash $
asv run -b linear_model HEAD^!
You can also run the benchmark suite using the version of scikit-learn already
installed in your current Python environment:
.. prompt:: bash $
asv run --python=same
It's particularly useful when you installed scikit-learn in editable mode to
avoid creating a new environment each time you run the benchmarks. By default
the results are not saved when using an existing installation. To save the
results you must specify a commit hash:
.. prompt:: bash $
asv run --python=same --set-commit-hash=<commit hash>
Benchmarks are saved and organized by machine, environment and commit. To see
the list of all saved benchmarks:
.. prompt:: bash $
asv show
and to see the report of a specific run:
.. prompt:: bash $
asv show <commit hash>
When running benchmarks for a pull request you're working on please report the
results on github.
The benchmark suite supports additional configurable options which can be set
in the `benchmarks/config.json` configuration file. For example, the benchmarks
can run for a provided list of values for the `n_jobs` parameter.
More information on how to write a benchmark and how to use asv can be found in
the `asv documentation <https://asv.readthedocs.io/en/latest/index.html>`_.
.. _issue_tracker_tags:
Issue Tracker Tags
==================
All issues and pull requests on the
`GitHub issue tracker <https://github.com/scikit-learn/scikit-learn/issues>`_
should have (at least) one of the following tags:
:Bug / Crash:
Something is happening that clearly shouldn't happen.
Wrong results as well as unexpected errors from estimators go here.
:Cleanup / Enhancement:
Improving performance, usability, consistency.
:Documentation:
Missing, incorrect or sub-standard documentations and examples.
:New Feature:
Feature requests and pull requests implementing a new feature.
There are four other tags to help new contributors:
:good first issue:
This issue is ideal for a first contribution to scikit-learn. Ask for help
if the formulation is unclear. If you have already contributed to
scikit-learn, look at Easy issues instead.
:Easy:
This issue can be tackled without much prior experience.
:Moderate:
Might need some knowledge of machine learning or the package,
but is still approachable for someone new to the project.
:help wanted:
This tag marks an issue which currently lacks a contributor or a
PR that needs another contributor to take over the work. These
issues can range in difficulty, and may not be approachable
for new contributors. Note that not all issues which need
contributors will have this tag.
.. _backwards-compatibility:
Maintaining backwards compatibility
===================================
.. _contributing_deprecation:
Deprecation
-----------
If any publicly accessible method, function, attribute or parameter
is renamed, we still support the old one for two releases and issue
a deprecation warning when it is called/passed/accessed.
E.g., if the function ``zero_one`` is renamed to ``zero_one_loss``,
we add the decorator ``deprecated`` (from ``sklearn.utils``)
to ``zero_one`` and call ``zero_one_loss`` from that function::
from ..utils import deprecated
def zero_one_loss(y_true, y_pred, normalize=True):
# actual implementation
pass
@deprecated("Function 'zero_one' was renamed to 'zero_one_loss' "
"in version 0.13 and will be removed in release 0.15. "
"Default behavior is changed from 'normalize=False' to "
"'normalize=True'")
def zero_one(y_true, y_pred, normalize=False):
return zero_one_loss(y_true, y_pred, normalize)
If an attribute is to be deprecated,
use the decorator ``deprecated`` on a property. Please note that the
``property`` decorator should be placed before the ``deprecated``
decorator for the docstrings to be rendered properly.
E.g., renaming an attribute ``labels_`` to ``classes_`` can be done as::
@deprecated("Attribute `labels_` was deprecated in version 0.13 and "
"will be removed in 0.15. Use `classes_` instead")
@property
def labels_(self):
return self.classes_
If a parameter has to be deprecated, a ``FutureWarning`` warning
must be raised too.
In the following example, k is deprecated and renamed to n_clusters::
import warnings
def example_function(n_clusters=8, k='deprecated'):
if k != 'deprecated':
warnings.warn("'k' was renamed to n_clusters in version 0.13 and "
"will be removed in 0.15.",
FutureWarning)
n_clusters = k
When the change is in a class, we validate and raise warning in ``fit``::
import warnings
class ExampleEstimator(BaseEstimator):
def __init__(self, n_clusters=8, k='deprecated'):
self.n_clusters = n_clusters
self.k = k
def fit(self, X, y):
if self.k != 'deprecated':
warnings.warn("'k' was renamed to n_clusters in version 0.13 and "
"will be removed in 0.15.",
FutureWarning)
self._n_clusters = self.k
else:
self._n_clusters = self.n_clusters
As in these examples, the warning message should always give both the
version in which the deprecation happened and the version in which the
old behavior will be removed. If the deprecation happened in version
0.x-dev, the message should say deprecation occurred in version 0.x and
the removal will be in 0.(x+2), so that users will have enough time to
adapt their code to the new behaviour. For example, if the deprecation happened
in version 0.18-dev, the message should say it happened in version 0.18
and the old behavior will be removed in version 0.20.
In addition, a deprecation note should be added in the docstring, recalling the
same information as the deprecation warning as explained above. Use the
``.. deprecated::`` directive::
.. deprecated:: 0.13
``k`` was renamed to ``n_clusters`` in version 0.13 and will be removed
in 0.15.
What's more, a deprecation requires a test which ensures that the warning is
raised in relevant cases but not in other cases. The warning should be caught
in all other tests (using e.g., ``@pytest.mark.filterwarnings``),
and there should be no warning in the examples.
Change the default value of a parameter
---------------------------------------
If the default value of a parameter needs to be changed, please replace the
default value with a specific value (e.g., ``warn``) and raise
``FutureWarning`` when users are using the default value. The following
example assumes that the current version is 0.20 and that we change the
default value of ``n_clusters`` from 5 (old default for 0.20) to 10
(new default for 0.22)::
import warnings
def example_function(n_clusters='warn'):
if n_clusters == 'warn':
warnings.warn("The default value of n_clusters will change from "
"5 to 10 in 0.22.", FutureWarning)
n_clusters = 5
When the change is in a class, we validate and raise warning in ``fit``::
import warnings
class ExampleEstimator:
def __init__(self, n_clusters='warn'):
self.n_clusters = n_clusters
def fit(self, X, y):
if self.n_clusters == 'warn':
warnings.warn("The default value of n_clusters will change from "
"5 to 10 in 0.22.", FutureWarning)
self._n_clusters = 5
Similar to deprecations, the warning message should always give both the
version in which the change happened and the version in which the old behavior
will be removed. The docstring needs to be updated accordingly. We need a test
which ensures that the warning is raised in relevant cases but not in other
cases. The warning should be caught in all other tests
(using e.g., ``@pytest.mark.filterwarnings``), and there should be no warning
in the examples.
.. currentmodule:: sklearn
.. _code_review:
Code Review Guidelines
======================
Reviewing code contributed to the project as PRs is a crucial component of
scikit-learn development. We encourage anyone to start reviewing code of other
developers. The code review process is often highly educational for everybody
involved. This is particularly appropriate if it is a feature you would like to
use, and so can respond critically about whether the PR meets your needs. While
each pull request needs to be signed off by two core developers, you can speed
up this process by providing your feedback.
.. note::
The difference between an objective improvement and a subjective nit isn't
always clear. Reviewers should recall that code review is primarily about
reducing risk in the project. When reviewing code, one should aim at
preventing situations which may require a bug fix, a deprecation, or a
retraction. Regarding docs: typos, grammar issues and disambiguations are
better addressed immediately.
Here are a few important aspects that need to be covered in any code review,
from high-level questions to a more detailed check-list.
- Do we want this in the library? Is it likely to be used? Do you, as
a scikit-learn user, like the change and intend to use it? Is it in
the scope of scikit-learn? Will the cost of maintaining a new
feature be worth its benefits?
- Is the code consistent with the API of scikit-learn? Are public
functions/classes/parameters well named and intuitively designed?
- Are all public functions/classes and their parameters, return types, and
stored attributes named according to scikit-learn conventions and documented clearly?
- Is any new functionality described in the user-guide and illustrated with examples?
- Is every public function/class tested? Are a reasonable set of
parameters, their values, value types, and combinations tested? Do
the tests validate that the code is correct, i.e. doing what the
documentation says it does? If the change is a bug-fix, is a
non-regression test included? Look at `this
<https://jeffknupp.com/blog/2013/12/09/improve-your-python-understanding-unit-testing>`__
to get started with testing in Python.
- Do the tests pass in the continuous integration build? If
appropriate, help the contributor understand why tests failed.
- Do the tests cover every line of code (see the coverage report in the build
log)? If not, are the lines missing coverage good exceptions?
- Is the code easy to read and low on redundancy? Should variable names be
improved for clarity or consistency? Should comments be added? Should comments
be removed as unhelpful or extraneous?
- Could the code easily be rewritten to run much more efficiently for
relevant settings?
- Is the code backwards compatible with previous versions? (or is a
deprecation cycle necessary?)
- Will the new code add any dependencies on other libraries? (this is
unlikely to be accepted)
- Does the documentation render properly (see the
:ref:`contribute_documentation` section for more details), and are the plots
instructive?
:ref:`saved_replies` includes some frequent comments that reviewers may make.
.. _communication:
Communication Guidelines
------------------------
Reviewing open pull requests (PRs) helps move the project forward. It is a
great way to get familiar with the codebase and should motivate the
contributor to keep involved in the project. [1]_
- Every PR, good or bad, is an act of generosity. Opening with a positive
comment will help the author feel rewarded, and your subsequent remarks may
be heard more clearly. You may feel good also.
- Begin if possible with the large issues, so the author knows they’ve been
understood. Resist the temptation to immediately go line by line, or to open
with small pervasive issues.
- Do not let perfect be the enemy of the good. If you find yourself making
many small suggestions that don't fall into the :ref:`code_review`, consider
the following approaches:
- refrain from submitting these;
- prefix them as "Nit" so that the contributor knows it's OK not to address;
- follow up in a subsequent PR, out of courtesy, you may want to let the
original contributor know.
- Do not rush, take the time to make your comments clear and justify your
suggestions.
- You are the face of the project. Bad days occur to everyone, in that
occasion you deserve a break: try to take your time and stay offline.
.. [1] Adapted from the numpy `communication guidelines
<https://numpy.org/devdocs/dev/reviewer_guidelines.html#communication-guidelines>`_.
Reading the existing code base
==============================
Reading and digesting an existing code base is always a difficult exercise
that takes time and experience to main. Even though we try to write simple
code in general, understanding the code can seem overwhelming at first,
given the sheer size of the project. Here is a list of tips that may help
make this task easier and faster (in no particular order).
- Get acquainted with the :ref:`api_overview`: understand what :term:`fit`,
:term:`predict`, :term:`transform`, etc. are used for.
- Before diving into reading the code of a function / class, go through the
docstrings first and try to get an idea of what each parameter / attribute
is doing. It may also help to stop a minute and think *how would I do this
myself if I had to?*
- The trickiest thing is often to identify which portions of the code are
relevant, and which are not. In scikit-learn **a lot** of input checking
is performed, especially at the beginning of the :term:`fit` methods.
Sometimes, only a very small portion of the code is doing the actual job.
For example looking at the ``fit()`` method of
:class:`~linear_model.LinearRegression`, what you're looking for
might just be the call the ``scipy.linalg.lstsq``, but it is buried into
multiple lines of input checking and the handling of different kinds of
parameters.
- Due to the use of `Inheritance
<https://en.wikipedia.org/wiki/Inheritance_(object-oriented_programming)>`_,
some methods may be implemented in parent classes. All estimators inherit
at least from :class:`~base.BaseEstimator`, and
from a ``Mixin`` class (e.g. :class:`~base.ClassifierMixin`) that enables default
behaviour depending on the nature of the estimator (classifier, regressor,
transformer, etc.).
- Sometimes, reading the tests for a given function will give you an idea of
what its intended purpose is. You can use ``git grep`` (see below) to find
all the tests written for a function. Most tests for a specific
function/class are placed under the ``tests/`` folder of the module
- You'll often see code looking like this:
``out = Parallel(...)(delayed(some_function)(param) for param in
some_iterable)``. This runs ``some_function`` in parallel using `Joblib
<https://joblib.readthedocs.io/>`_. ``out`` is then an iterable containing
the values returned by ``some_function`` for each call.
- We use `Cython <https://cython.org/>`_ to write fast code. Cython code is
located in ``.pyx`` and ``.pxd`` files. Cython code has a more C-like
flavor: we use pointers, perform manual memory allocation, etc. Having
some minimal experience in C / C++ is pretty much mandatory here.
- Master your tools.
- With such a big project, being efficient with your favorite editor or
IDE goes a long way towards digesting the code base. Being able to quickly
jump (or *peek*) to a function/class/attribute definition helps a lot.
So does being able to quickly see where a given name is used in a file.
- `git <https://git-scm.com/book/en>`_ also has some built-in killer
features. It is often useful to understand how a file changed over time,
using e.g. ``git blame`` (`manual
<https://git-scm.com/docs/git-blame>`_). This can also be done directly
on GitHub. ``git grep`` (`examples
<https://git-scm.com/docs/git-grep#_examples>`_) is also extremely
useful to see every occurrence of a pattern (e.g. a function call or a
variable) in the code base.
- Configure `git blame` to ignore the commit that migrated the code style to
`black`.
.. prompt:: bash $
git config blame.ignoreRevsFile .git-blame-ignore-revs
Find out more information in black's
`documentation for avoiding ruining git blame <https://black.readthedocs.io/en/stable/guides/introducing_black_to_your_project.html#avoiding-ruining-git-blame>`_.
Reference in New Issue View Git Blame Copy Permalink