scikit-learn sklearn Contributing contribute It is hosted on https github com scikit learn scikit learn This project is a community effort and everyone is welcome to contributing

Answer:
.. _contributing:

============
Contributing
============

.. currentmodule:: sklearn

This project is a community effort, and everyone is welcome to
contribute. It is hosted on https://github.com/scikit-learn/scikit-learn.
The decision making process and governance structure of scikit-learn is laid
out in :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
create a GitHub issue 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":

.. raw:: html

  <p>
    <object
      data="https://img.shields.io/github/stars/scikit-learn/scikit-learn?style=for-the-badge&logo=github"
      type="image/svg+xml">
    </object>
  </p>

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`.

.. dropdown:: 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/issues>`__

  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`.

Automated Contributions Policy
==============================

Please refrain from submitting issues or pull requests generated by
fully-automated tools. Maintainers reserve the right, at their sole discretion,
to close such submissions and to block any account responsible for them.

Ideally, contributions should follow from a human-to-human discussion in the
form of an issue.

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
   <https://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. 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:

  .. prompt:: bash

    python -c "import sklearn; sklearn.show_versions()"

- 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 about :ref:`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.

To maintain the quality of the codebase and ease the review process, any
contribution must conform to the project's :ref:`coding guidelines
<coding-guidelines>`, in particular:

- Don't modify unrelated lines to keep the PR focused on the scope stated in its
  description or issue.
- Only write inline comments that add value and avoid stating the obvious: explain
  the "why" rather than the "what".
- **Most importantly**: Do not contribute code that you don't understand.

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

4. Follow steps 2-6 in :ref:`install_bleeding_edge` to build scikit-learn in
   development mode and return to this document.

5. Install the development dependencies:

   .. prompt:: bash

        pip install pytest pytest-cov ruff mypy numpydoc black==24.3.0

.. _upstream:

6. 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

7. Check that the `upstream` and `origin` remote aliases are configured correctly
   by running `git remote -v` which should display:

   .. code-block:: text

        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. It could be useful to run some test to verify your installation.
Please refer to :ref:`pytest_tips` for examples.

The next steps now describe the process of modifying code and submitting a PR:

8. 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

9. 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!

10. (**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`.

11. 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

12. 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
    notification to potential reviewers. You may want to consider sending an message to
    the `discord <https://discord.com/invite/h9qyrK8Jc8>`_ in the development
    channel for more visibility if your pull request does not receive attention after
    a couple of days (instant replies are not guaranteed though).

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/doc>`_ 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.
An incomplete contribution -- where you expect to do more work before receiving
a full review -- should be marked as a `draft pull request
<https://docs.github.com/en/pull-requests/collaborating-with-pull-requests/proposing-changes-to-your-work-with-pull-requests/changing-the-stage-of-a-pull-request>`__
and changed to "ready for review" when it matures. Draft PRs may be useful to:
indicate you are working on something to avoid duplicated work, request
broad review of functionality or API, or seek collaborators. Draft PRs 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 "ready for review". The
**bolded** ones are especially important:

1. **Give your pull request a helpful title** that summarizes what your
   contribution does. This title will often become the commit message once
   merged so it should summarize 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. If your PR is likely to affect users, you need to add a changelog entry describing
   your PR changes, see the `following README <https://github.com/scikit-learn/scikit-learn/blob/main/doc/whats_new/upcoming_changes/README.md>`
   for more details.

6. Follow the :ref:`coding-guidelines`.

7. When applicable, use the validation tools and scripts in the :mod:`sklearn.utils`
   module. A list of utility routines available for developers can be found in the
   :ref:`developers-utils` page.

8. 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, or it only partially resolves the target
   issue, create a link to them without using the keywords (e.g., ``Towards #1234``).

9. 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.

10. 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.

11. 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 unit test coverage (at least 80%, better 100%), check with:

  .. prompt:: bash

    pip install pytest pytest-cov
    pytest --cov sklearn path/to/tests

  See also :ref:`testing_coverage`.

* Run static analysis with `mypy`:

  .. prompt:: bash

      mypy sklearn

  This 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.

.. seealso::

   For two very well documented and more detailed guides on development
   workflow, please pay a visit to the `Scipy Development Workflow
   <http://scipy.github.io/devdocs/dev/dev_quickstart.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.
* Github Actions are used for various tasks, including building wheels and
  source distributions.
* Cirrus CI is used to build on ARM.

.. _commit_markers:

Commit message markers
^^^^^^^^^^^^^^^^^^^^^^

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
[cd build cirrus]      CD is run only for Cirrus CI
[lint skip]            Azure pipeline skips linting
[scipy-dev]            Build & test with our dependencies (numpy, scipy, etc.) development builds
[free-threaded]        Build & test with CPython 3.13 free-threaded
[pyodide]              Build & test with Pyodide
[azure parallel]       Run Azure CI jobs in parallel
[cirrus arm]           Run Cirrus CI ARM test
[float32]              Run float32 tests by setting `SKLEARN_RUN_FLOAT32_TESTS=1`. See :ref:`environment_variable` for more details
[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.

.. _build_lock_files:

Build lock files
^^^^^^^^^^^^^^^^

CIs use lock files to build environments with specific versions of dependencies. When a
PR needs to modify the dependencies or their versions, the lock files should be updated
accordingly. This can be done by adding the following comment directly in the GitHub
Pull Request (PR) discussion:

.. code-block:: text

  @scikit-learn-bot update lock-files

A bot will push a commit to your PR branch with the updated lock files in a few minutes.
Make sure to tick the *Allow edits from maintainers* checkbox located at the bottom of
the right sidebar of the PR. You can also specify the options `--select-build`,
`--skip-build`, and `--select-tag` as in a command line. Use `--help` on the script
`build_tools/update_environments_and_lock_files.py` for more information. For example,

.. code-block:: text

  @scikit-learn-bot update lock-files --select-tag main-ci --skip-build doc

The bot will automatically add :ref:`commit message markers <commit_markers>` to the
commit for certain tags. If you want to add more markers manually, you can do so using
the `--commit-marker` option. For example, the following comment will trigger the bot to
update documentation-related lock files and add the `[doc build]` marker to the commit:

.. code-block:: text

  @scikit-learn-bot update lock-files --select-build doc --commit-marker "[doc build]"

Resolve conflicts in lock files
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

Here is a bash snippet that helps resolving conflicts in environment and lock files:

.. prompt:: bash

  # pull latest upstream/main
  git pull upstream main --no-rebase
  # resolve conflicts - keeping the upstream/main version for specific files
  git checkout --theirs  build_tools/*/*.lock build_tools/*/*environment.yml \
      build_tools/*/*lock.txt build_tools/*/*requirements.txt
  git add build_tools/*/*.lock build_tools/*/*environment.yml \
      build_tools/*/*lock.txt build_tools/*/*requirements.txt
  git merge --continue

This will merge `upstream/main` into our branch, automatically prioritising the
`upstream/main` for conflicting environment and lock files (this is good enough, because
we will re-generate the lock files afterwards).

Note that this only fixes conflicts in environment and lock files and you might have
other conflicts to resolve.

Finally, we have to re-generate the environment and lock files for the CIs, as described
in :ref:`Build lock files <build_lock_files>`, or by running:

.. prompt:: bash

  python build_tools/update_environments_and_lock_files.py

.. _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!

- **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.

- **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.

- **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/method/class docstrings:** Also known as "API documentation", these
  describe what the object does and details any parameters, attributes and
  methods. Docstrings live alongside the code in `sklearn/
  <https://github.com/scikit-learn/scikit-learn/tree/main/sklearn>`_, and are generated
  generated according to `doc/api_reference.py
  <https://github.com/scikit-learn/scikit-learn/blob/main/doc/api_reference.py>`_. To
  add, update, remove, or deprecate a public API that is listed in :ref:`api_ref`, this
  is the place to look at.
* **User guide:** These provide more detailed information about the algorithms
  implemented in scikit-learn and generally live in the root
  `doc/ <https://github.com/scikit-learn/scikit-learn/tree/main/doc>`_ directory
  and
  `doc/modules/ <https://github.com/scikit-learn/scikit-learn/tree/main/doc/modules>`_.
* **Examples:** These provide full code examples that may demonstrate the use
  of scikit-learn modules, compare different algorithms or discuss their
  interpretation, etc. Examples live in
  `examples/ <https://github.com/scikit-learn/scikit-learn/tree/main/examples>`_.
* **Other reStructuredText documents:** These provide various other useful information
  (e.g., the :ref:`contributing` guide) and live in
  `doc/ <https://github.com/scikit-learn/scikit-learn/tree/main/doc>`_.

.. dropdown:: Guidelines for writing docstrings

  * When documenting the parameters and attributes, here is a list of some
    well-formatted examples

    .. code-block:: text

      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:

    * Use Python basic types. (``bool`` instead of ``boolean``)
    * Use parenthesis for defining shapes: ``array-like of shape (n_samples,)``
      or ``array-like of shape (n_samples, n_features)``
    * For strings with multiple options, use brackets: ``input: {'log',
      'squared', 'multinomial'}``
    * 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``.
    * Specify ``dataframe`` when "frame-like" features are being used, such as
      the column names.
    * 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``.
    * 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.
    * 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``.

  * 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:

    .. code-block:: text

      See Also
      --------
      SelectKBest : Select features based on the k highest scores.
      SelectFpr : Select features based on a false positive rate test.

  * Add one or two snippets of code in "Example" section to show how it can be used.

.. dropdown:: Guidelines for writing the user guide and other reStructuredText documents

  It is important to keep a good compromise between mathematical and algorithmic
  details, and give intuition to the reader on what the algorithm does.

  * Begin with a concise, hand-waving explanation of what the algorithm/code does on
    the data.

  * Highlight the usefulness of the feature and its recommended application.
    Consider including the algorithm's complexity
    (:math:`O\left(g\left(n\right)\right)`) if available, as "rules of thumb" can
    be very machine-dependent. Only if those complexities are not available, then
    rules of thumb may be provided instead.

  * Incorporate a relevant figure (generated from an example) to provide intuitions.

  * Include one or two short code examples to demonstrate the feature's usage.

  * Introduce any necessary mathematical equations, followed by references. By
    deferring the mathematical aspects, the documentation becomes more accessible
    to users primarily interested in understanding the feature's practical
    implications rather than its underlying mechanics.

  * When editing reStructuredText (``.rst``) files, try to keep line length under
    88 characters when possible (exceptions include links and tables).

  * In scikit-learn reStructuredText files both single and double backticks
    surrounding text will render as inline literal (often used for code, e.g.,
    `list`). This is due to specific configurations we have set. Single
    backticks should be used nowadays.

  * Too much information makes it difficult for users to access the content they
    are interested in. Use dropdowns to factorize it by using the following syntax

    .. code-block:: rst

      .. dropdown:: Dropdown title

        Dropdown content.

    The snippet above will result in the following dropdown:

    .. dropdown:: Dropdown title

      Dropdown content.

  * Information that can be hidden by default using dropdowns is:

    * low hierarchy sections such as `References`, `Properties`, etc. (see for
      instance the subsections in :ref:`det_curve`);

    * in-depth mathematical details;

    * narrative that is use-case specific;

    * in general, narrative that may only interest users that want to go beyond
      the pragmatics of a given tool.

  * Do not use dropdowns for the low level section `Examples`, as it should stay
    visible to all users. Make sure that the `Examples` section comes right after
    the main discussion with the least possible folded section in-between.

  * Be aware that dropdowns break cross-references. If that makes sense, hide the
    reference along with the text mentioning it. Else, do not use dropdown.

.. dropdown:: Guidelines for writing references

  * 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 the "References" section in docstrings, see
    :func:`sklearn.metrics.silhouette_score` as an example.

  * To cross-reference to other pages in the scikit-learn documentation use the
    reStructuredText cross-referencing syntax:

    * **Section:** to link to an arbitrary section in the documentation, use
      reference labels (see `Sphinx docs
      <https://www.sphinx-doc.org/en/master/usage/restructuredtext/roles.html#ref-role>`_).
      For example:

      .. code-block:: rst

          .. _my-section:

          My section
          ----------

          This is the text of the section.

          To refer to itself use :ref:`my-section`.

      You should not modify existing sphinx reference labels as this would break
      existing cross references and external links pointing to specific sections
      in the scikit-learn documentation.

    * **Glossary:** linking to a term in the :ref:`glossary`:

      .. code-block:: rst

          :term:`cross_validation`

    * **Function:** to link to the documentation of a function, use the full import
      path to the function:

      .. code-block:: rst

          :func:`~sklearn.model_selection.cross_val_score`

      However, if there is a `.. currentmodule::` directive above you in the document,
      you will only need to use the path to the function succeeding the current
      module specified. For example:

      .. code-block:: rst

          .. currentmodule:: sklearn.model_selection

          :func:`cross_val_score`

    * **Class:** to link to documentation of a class, use the full import path to the
      class, unless there is a `.. currentmodule::` directive in the document above
      (see above):

      .. code-block:: rst

          :class:`~sklearn.preprocessing.StandardScaler`

You can edit the documentation using any text editor, and then generate the
HTML output by following :ref:`building_documentation`. The resulting HTML files
will be placed in ``_build/html/`` and are viewable in a web browser, for instance by
opening the local ``_build/html/index.html`` file or by running a local server

.. prompt:: bash

  python -m http.server -d _build/html

.. _building_documentation:

Building the documentation
--------------------------

**Before submitting a pull request check if your modifications have introduced
new sphinx warnings by building the documentation locally and try to fix them.**

First, make sure you have :ref:`properly installed <install_bleeding_edge>` the
development version. On top of that, building the documentation requires installing some
additional packages:

..
    packaging is not needed once setuptools starts shipping packaging>=17.0

.. prompt:: bash

    pip install sphinx sphinx-gallery numpydoc matplotlib Pillow pandas \
                polars scikit-image packaging seaborn sphinx-prompt \
                sphinxext-opengraph sphinx-copybutton plotly pooch \
                pydata-sphinx-theme sphinxcontrib-sass sphinx-design \
                sphinx-remove-toctrees

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 web site without
the example gallery:

.. prompt:: bash

    make

The documentation will be generated in the ``_build/html/stable`` directory
and are viewable in a web browser, for instance by opening the local
``_build/html/stable/index.html`` file.
To also generate the example gallery you can use:

.. prompt:: bash

    make html

This will run all the examples, which takes a while. You can also run only a few examples based on their file names.
Here is a way to run all examples with filenames containing `plot_calibration`:

.. prompt:: bash

    EXAMPLES_PATTERN="plot_calibration" make html

You can use regular expressions for more advanced use cases.

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

.. admonition:: Sphinx version
   :class: warning

   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?q=repo%3Ascikit-learn%2Fscikit-learn+%2F%5C%2Fsphinx-%5B0-9.%5D%2B%2F+path%3Abuild_tools%2Fcircle%2Fdoc_linux-64_conda.lock&type=code>`_
   to know the exact version.

.. _generated_doc_CI:

Generated documentation on GitHub Actions
-----------------------------------------

When you change the documentation in a pull request, GitHub Actions automatically
builds it. To view the documentation generated by GitHub Actions, simply go to the
bottom of your PR page, look for the item "Check the rendered docs here!" and
click on 'details' next to it:

.. image:: ../images/generated-doc-ci.png
   :align: center

.. _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%.

.. dropdown:: 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 ``pyplot``
  fixture which can be used with ``matplotlib``.

  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

.. dropdown:: 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 `pytest --cov sklearn /path/to/tests`. 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_codebase.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 `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:
    Something is happening that clearly shouldn't happen.
    Wrong results as well as unexpected errors from estimators go here.

: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 class, function, method, attribute or parameter is renamed,
we still support the old one for two releases and issue a deprecation warning when it is
called, passed, or accessed.

.. rubric:: Deprecating a class or a function

Suppose the function ``zero_one`` is renamed to ``zero_one_loss``, we add the decorator
:class:`utils.deprecated` 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 0.13 and will be "
        "removed in 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)

One also needs to move ``zero_one`` from ``API_REFERENCE`` to
``DEPRECATED_API_REFERENCE`` and add ``zero_one_loss`` to ``API_REFERENCE`` in the
``doc/api_reference.py`` file to reflect the changes in :ref:`api_ref`.

.. rubric:: Deprecating an attribute or a method

If an attribute or a method is to be deprecated, use the decorator
:class:`~utils.deprecated` on the property. Please note that the
:class:`~utils.deprecated` decorator should be placed before the ``property`` decorator
if there is one, so that the docstrings can be rendered properly. For instance, renaming
an attribute ``labels_`` to ``classes_`` can be done as::

    @deprecated(
        "Attribute `labels_` was deprecated in 0.13 and will be removed in 0.15. Use "
        "`classes_` instead"
    )
    @property
    def labels_(self):
        return self.classes_

.. rubric:: Deprecating a parameter

If a parameter has to be deprecated, a ``FutureWarning`` warning must be raised
manually. 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 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 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.

The warning message should also include a brief explanation of the change and point
users to an alternative.

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:

.. code-block:: rst

  .. 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 parameter description in the docstring needs to be updated accordingly by adding
a ``versionchanged`` directive with the old and new default value, pointing to the
version when the change will be effective:

.. code-block:: rst

    .. versionchanged:: 0.22
       The default value for `n_clusters` will change from 5 to 10 in version 0.22.

Finally, 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.

.. _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.

.. dropdown:: Important aspects to be covered in any code review

  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:

.. dropdown:: 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 master. 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 :meth:`~linear_model.LinearRegression.fit` method of
  :class:`~linear_model.LinearRegression`, what you're looking for
  might just be the call the :func:`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. For more information see
  :ref:`cython`.
- 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>`_.

contributing                Contributing                  currentmodule   sklearn  This project is a community effort  and everyone is welcome to contribute  It is hosted on https   github com scikit learn scikit learn  The decision making process and governance structure of scikit learn is laid out in  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 create a GitHub issue 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       raw   html     p       object       data  https   img shields io github stars scikit learn scikit learn style for the badge logo github        type  image svg xml         object      p   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       dropdown   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 issues        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    Automated Contributions Policy                                 Please refrain from submitting issues or pull requests generated by fully automated tools  Maintainers reserve the right  at their sole discretion  to close such submissions and to block any account responsible for them   Ideally  contributions should follow from a human to human discussion in the form of an issue   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     https   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  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        prompt   bash      python  c  import sklearn  sklearn show versions       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 about  ref  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   To maintain the quality of the codebase and ease the review process  any contribution must conform to the project s  ref  coding guidelines  coding guidelines    in particular     Don t modify unrelated lines to keep the PR focused on the scope stated in its   description or issue    Only write inline comments that add value and avoid stating the obvious  explain   the  why  rather than the  what       Most importantly    Do not contribute code that you don t understand   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  4  Follow steps 2 6 in  ref  install bleeding edge  to build scikit learn in    development mode and return to this document   5  Install the development dependencies         prompt   bash          pip install pytest pytest cov ruff mypy numpydoc black  24 3 0      upstream   6  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  7  Check that the  upstream  and  origin  remote aliases are configured correctly    by running  git remote  v  which should display         code block   text          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  It could be useful to run some test to verify your installation  Please refer to  ref  pytest tips  for examples   The next steps now describe the process of modifying code and submitting a PR   8  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  9  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   10     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    11  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  12  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     notification to potential reviewers  You may want to consider sending an message to     the  discord  https   discord com invite h9qyrK8Jc8    in the development     channel for more visibility if your pull request does not receive attention after     a couple of days  instant replies are not guaranteed though    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 doc    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  An incomplete contribution    where you expect to do more work before receiving a full review    should be marked as a  draft pull request  https   docs github com en pull requests collaborating with pull requests proposing changes to your work with pull requests changing the stage of a pull request     and changed to  ready for review  when it matures  Draft PRs may be useful to  indicate you are working on something to avoid duplicated work  request broad review of functionality or API  or seek collaborators  Draft PRs 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  ready for review   The   bolded   ones are especially important   1    Give your pull request a helpful title   that summarizes what your    contribution does  This title will often become the commit message once    merged so it should summarize 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  If your PR is likely to affect users  you need to add a changelog entry describing    your PR changes  see the  following README  https   github com scikit learn scikit learn blob main doc whats new upcoming changes README md      for more details   6  Follow the  ref  coding guidelines    7  When applicable  use the validation tools and scripts in the  mod  sklearn utils     module  A list of utility routines available for developers can be found in the     ref  developers utils  page   8  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  or it only partially resolves the target    issue  create a link to them without using the keywords  e g     Towards  1234      9  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   10  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   11  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 unit test coverage  at least 80   better 100    check with        prompt   bash      pip install pytest pytest cov     pytest   cov sklearn path to tests    See also  ref  testing coverage      Run static analysis with  mypy         prompt   bash        mypy sklearn    This 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      seealso       For two very well documented and more detailed guides on development    workflow  please pay a visit to the  Scipy Development Workflow     http   scipy github io devdocs dev dev quickstart 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    Github Actions are used for various tasks  including building wheels and   source distributions    Cirrus CI is used to build on ARM       commit markers   Commit message markers                         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  cd build cirrus       CD is run only for Cirrus CI  lint skip             Azure pipeline skips linting  scipy dev             Build   test with our dependencies  numpy  scipy  etc   development builds  free threaded         Build   test with CPython 3 13 free threaded  pyodide               Build   test with Pyodide  azure parallel        Run Azure CI jobs in parallel  cirrus arm            Run Cirrus CI ARM test  float32               Run float32 tests by setting  SKLEARN RUN FLOAT32 TESTS 1   See  ref  environment variable  for more details  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       build lock files   Build lock files                   CIs use lock files to build environments with specific versions of dependencies  When a PR needs to modify the dependencies or their versions  the lock files should be updated accordingly  This can be done by adding the following comment directly in the GitHub Pull Request  PR  discussion      code block   text     scikit learn bot update lock files  A bot will push a commit to your PR branch with the updated lock files in a few minutes  Make sure to tick the  Allow edits from maintainers  checkbox located at the bottom of the right sidebar of the PR  You can also specify the options    select build      skip build   and    select tag  as in a command line  Use    help  on the script  build tools update environments and lock files py  for more information  For example      code block   text     scikit learn bot update lock files   select tag main ci   skip build doc  The bot will automatically add  ref  commit message markers  commit markers   to the commit for certain tags  If you want to add more markers manually  you can do so using the    commit marker  option  For example  the following comment will trigger the bot to update documentation related lock files and add the   doc build   marker to the commit      code block   text     scikit learn bot update lock files   select build doc   commit marker   doc build    Resolve conflicts in lock files                                  Here is a bash snippet that helps resolving conflicts in environment and lock files      prompt   bash      pull latest upstream main   git pull upstream main   no rebase     resolve conflicts   keeping the upstream main version for specific files   git checkout   theirs  build tools     lock build tools    environment yml         build tools    lock txt build tools    requirements txt   git add build tools     lock build tools    environment yml         build tools    lock txt build tools    requirements txt   git merge   continue  This will merge  upstream main  into our branch  automatically prioritising the  upstream main  for conflicting environment and lock files  this is good enough  because we will re generate the lock files afterwards    Note that this only fixes conflicts in environment and lock files and you might have other conflicts to resolve   Finally  we have to re generate the environment and lock files for the CIs  as described in  ref  Build lock files  build lock files    or by running      prompt   bash    python build tools update environments and lock files py      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       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       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       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 method class docstrings    Also known as  API documentation   these   describe what the object does and details any parameters  attributes and   methods  Docstrings live alongside the code in  sklearn     https   github com scikit learn scikit learn tree main sklearn     and are generated   generated according to  doc api reference py    https   github com scikit learn scikit learn blob main doc api reference py     To   add  update  remove  or deprecate a public API that is listed in  ref  api ref   this   is the place to look at      User guide    These provide more detailed information about the algorithms   implemented in scikit learn and generally live in the root    doc   https   github com scikit learn scikit learn tree main doc    directory   and    doc modules   https   github com scikit learn scikit learn tree main doc modules         Examples    These provide full code examples that may demonstrate the use   of scikit learn modules  compare different algorithms or discuss their   interpretation  etc  Examples live in    examples   https   github com scikit learn scikit learn tree main examples         Other reStructuredText documents    These provide various other useful information    e g   the  ref  contributing  guide  and live in    doc   https   github com scikit learn scikit learn tree main doc          dropdown   Guidelines for writing docstrings      When documenting the parameters and attributes  here is a list of some     well formatted examples         code block   text        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         Use Python basic types     bool   instead of   boolean          Use parenthesis for defining shapes    array like of shape  n samples           or   array like of shape  n samples  n features          For strings with multiple options  use brackets    input    log          squared    multinomial           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          Specify   dataframe   when  frame like  features are being used  such as       the column names        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          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        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         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          code block   text        See Also                      SelectKBest   Select features based on the k highest scores        SelectFpr   Select features based on a false positive rate test       Add one or two snippets of code in  Example  section to show how it can be used       dropdown   Guidelines for writing the user guide and other reStructuredText documents    It is important to keep a good compromise between mathematical and algorithmic   details  and give intuition to the reader on what the algorithm does       Begin with a concise  hand waving explanation of what the algorithm code does on     the data       Highlight the usefulness of the feature and its recommended application      Consider including the algorithm s complexity       math  O left g left n right  right    if available  as  rules of thumb  can     be very machine dependent  Only if those complexities are not available  then     rules of thumb may be provided instead       Incorporate a relevant figure  generated from an example  to provide intuitions       Include one or two short code examples to demonstrate the feature s usage       Introduce any necessary mathematical equations  followed by references  By     deferring the mathematical aspects  the documentation becomes more accessible     to users primarily interested in understanding the feature s practical     implications rather than its underlying mechanics       When editing reStructuredText     rst    files  try to keep line length under     88 characters when possible  exceptions include links and tables        In scikit learn reStructuredText files both single and double backticks     surrounding text will render as inline literal  often used for code  e g        list    This is due to specific configurations we have set  Single     backticks should be used nowadays       Too much information makes it difficult for users to access the content they     are interested in  Use dropdowns to factorize it by using the following syntax         code block   rst           dropdown   Dropdown title          Dropdown content       The snippet above will result in the following dropdown          dropdown   Dropdown title        Dropdown content       Information that can be hidden by default using dropdowns is         low hierarchy sections such as  References    Properties   etc   see for       instance the subsections in  ref  det curve           in depth mathematical details         narrative that is use case specific         in general  narrative that may only interest users that want to go beyond       the pragmatics of a given tool       Do not use dropdowns for the low level section  Examples   as it should stay     visible to all users  Make sure that the  Examples  section comes right after     the main discussion with the least possible folded section in between       Be aware that dropdowns break cross references  If that makes sense  hide the     reference along with the text mentioning it  Else  do not use dropdown       dropdown   Guidelines for writing references      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 the  References  section in docstrings  see      func  sklearn metrics silhouette score  as an example       To cross reference to other pages in the scikit learn documentation use the     reStructuredText cross referencing syntax           Section    to link to an arbitrary section in the documentation  use       reference labels  see  Sphinx docs        https   www sphinx doc org en master usage restructuredtext roles html ref role            For example            code block   rst                my section             My section                                 This is the text of the section             To refer to itself use  ref  my section          You should not modify existing sphinx reference labels as this would break       existing cross references and external links pointing to specific sections       in the scikit learn documentation           Glossary    linking to a term in the  ref  glossary             code block   rst             term  cross validation           Function    to link to the documentation of a function  use the full import       path to the function            code block   rst             func   sklearn model selection cross val score         However  if there is a     currentmodule    directive above you in the document        you will only need to use the path to the function succeeding the current       module specified  For example            code block   rst               currentmodule   sklearn model selection             func  cross val score           Class    to link to documentation of a class  use the full import path to the       class  unless there is a     currentmodule    directive in the document above        see above             code block   rst             class   sklearn preprocessing StandardScaler   You can edit the documentation using any text editor  and then generate the HTML output by following  ref  building documentation   The resulting HTML files will be placed in    build html    and are viewable in a web browser  for instance by opening the local    build html index html   file or by running a local server     prompt   bash    python  m http server  d  build html       building documentation   Building the documentation                               Before submitting a pull request check if your modifications have introduced new sphinx warnings by building the documentation locally and try to fix them     First  make sure you have  ref  properly installed  install bleeding edge   the development version  On top of that  building the documentation requires installing some additional packages          packaging is not needed once setuptools starts shipping packaging  17 0     prompt   bash      pip install sphinx sphinx gallery numpydoc matplotlib Pillow pandas                   polars scikit image packaging seaborn sphinx prompt                   sphinxext opengraph sphinx copybutton plotly pooch                   pydata sphinx theme sphinxcontrib sass sphinx design                   sphinx remove toctrees  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 web site without the example gallery      prompt   bash      make  The documentation will be generated in the    build html stable   directory and are viewable in a web browser  for instance by opening the local    build html stable index html   file  To also generate the example gallery you can use      prompt   bash      make html  This will run all the examples  which takes a while  You can also run only a few examples based on their file names  Here is a way to run all examples with filenames containing  plot calibration       prompt   bash      EXAMPLES PATTERN  plot calibration  make html  You can use regular expressions for more advanced use cases   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     admonition   Sphinx version     class  warning     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 q repo 3Ascikit learn 2Fscikit learn  2F 5C 2Fsphinx  5B0 9  5D 2B 2F path 3Abuild tools 2Fcircle 2Fdoc linux 64 conda lock type code       to know the exact version        generated doc CI   Generated documentation on GitHub Actions                                            When you change the documentation in a pull request  GitHub Actions automatically builds it  To view the documentation generated by GitHub Actions  simply go to the bottom of your PR page  look for the item  Check the rendered docs here   and click on  details  next to it      image      images generated doc ci png     align  center      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       dropdown   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   pyplot     fixture which can be used with   matplotlib       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     dropdown   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  pytest   cov sklearn  path to tests   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 codebase 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  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      Something is happening that clearly shouldn t happen      Wrong results as well as unexpected errors from estimators go here    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 class  function  method  attribute or parameter is renamed  we still support the old one for two releases and issue a deprecation warning when it is called  passed  or accessed      rubric   Deprecating a class or a function  Suppose the function   zero one   is renamed to   zero one loss    we add the decorator  class  utils deprecated  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 0 13 and will be            removed in 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   One also needs to move   zero one   from   API REFERENCE   to   DEPRECATED API REFERENCE   and add   zero one loss   to   API REFERENCE   in the   doc api reference py   file to reflect the changes in  ref  api ref       rubric   Deprecating an attribute or a method  If an attribute or a method is to be deprecated  use the decorator  class   utils deprecated  on the property  Please note that the  class   utils deprecated  decorator should be placed before the   property   decorator if there is one  so that the docstrings can be rendered properly  For instance  renaming an attribute   labels    to   classes    can be done as         deprecated           Attribute  labels   was deprecated in 0 13 and will be removed in 0 15  Use             classes   instead             property     def labels  self           return self classes      rubric   Deprecating a parameter  If a parameter has to be deprecated  a   FutureWarning   warning must be raised manually  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 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 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   The warning message should also include a brief explanation of the change and point users to an alternative   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      code block   rst       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 parameter description in the docstring needs to be updated accordingly by adding a   versionchanged   directive with the old and new default value  pointing to the version when the change will be effective      code block   rst         versionchanged   0 22        The default value for  n clusters  will change from 5 to 10 in version 0 22   Finally  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       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      dropdown   Important aspects to be covered in any code review    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      dropdown   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 master  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  meth   linear model LinearRegression fit  method of    class   linear model LinearRegression   what you re looking for   might just be the call the  func  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  For more information see    ref  cython     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