Contributing

This project is a community effort, and everyone is welcome to contribute.

The project is hosted on https://github.com/scikit-learn/scikit-learn

The decision making process and governance structure of scikit-learn is laid out in the governance document: Scikit-learn governance and decision-making.

Scikit-learn is somewhat selective 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 Issues for New Contributors to get started.

In case you experience issues using this package, do not hesitate to submit a ticket to the GitHub issue tracker. You are also welcome to post feature requests or pull requests.

Ways to contribute

There are many ways to contribute to scikit-learn, with the most common ones being contribution of code or documentation to the project. Improving the documentation is no less important than improving the library itself. If you find a typo in the documentation, or have made improvements, do not hesitate to send an email to the mailing list or preferably submit a GitHub pull request. Full documentation can be found under the doc/ directory.

But there are many other ways to help. In particular helping to improve, triage, and investigate issues and reviewing other developers’ pull requests are very valuable contributions that decrease the burden on the project maintainers.

Another way to contribute is to report issues you’re facing, and give a “thumbs up” on issues that others reported and that are relevant to you. It also helps us if you spread the word: reference the project from your blog and articles, link to it from your website, or simply star to say “I use it”:

In case a contribution/issue involves changes to the API principles or changes to dependencies or supported versions, it must be backed by a Enhancement proposals (SLEPs), where a SLEP must be submitted as a pull-request to enhancement proposals using the SLEP template and follows the decision-making process outlined in Scikit-learn governance and decision-making.

Star

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. 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:

How to make a good bug report

When you submit an issue to Github, 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 short reproducible code snippet, this way anyone can try to reproduce the bug easily (see this for more details). If your snippet is longer than around 50 lines, please link to a gist or a github repo.

  • If not feasible to include a reproducible snippet, please be specific about what estimators and/or functions are involved and the shape of the data.

  • If an exception is raised, please provide the full traceback.

  • Please include your operating system type and version number, as well as your Python, scikit-learn, numpy, and scipy versions. This information can be found by running the following code snippet:

    >>> import sklearn
>>> sklearn.show_versions()

  • Please ensure all code snippets and error messages are formatted in appropriate code blocks. See Creating and highlighting code blocks for more details.

If you want to help curate issues, read the following.

Contributing code

Note

To avoid duplicating work, it is highly advised that you search through the issue tracker and the PR list. 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 to get some feedbacks from core developers.

One easy way to find an issue to work on is by applying the “help wanted” label in your search. This lists all the issues that have been unclaimed so far. In order to claim an issue for yourself, please comment exactly /take on it for the CI to automatically assign the issue to you.

Video resources

These videos are step-by-step introductions on how to contribute to scikit-learn, and are a great companion to the following text guidelines. Please make sure to still check our guidelines below, since they describe our latest up-to-date workflow.

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 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 on GitHub if you do not already have one.

  2. Fork the project repository: 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.

  3. Clone your fork of the scikit-learn repo from your GitHub account to your local disk:

    git clone git@github.com:YourLogin/scikit-learn.git  # add --depth 1 if your connection is slow
cd scikit-learn

  1. Follow steps 2-7 in Building from source to build scikit-learn in development mode and return to this document.

  2. Install the development dependencies:

    pip install pytest pytest-cov flake8 mypy numpydoc black==22.3.0

  1. 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:

    git remote add upstream git@github.com:scikit-learn/scikit-learn.git

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

    origin  git@github.com:YourLogin/scikit-learn.git (fetch)
origin  git@github.com:YourLogin/scikit-learn.git (push)
upstream        git@github.com:scikit-learn/scikit-learn.git (fetch)
upstream        git@github.com:scikit-learn/scikit-learn.git (push)

You should now have a working installation of scikit-learn, and your git repository properly configured. The next steps now describe the process of modifying code and submitting a PR:

  1. Synchronize your main branch with the upstream/main branch, more details on GitHub Docs:

    git checkout main
git fetch upstream
git merge upstream/main

  2. Create a feature branch to hold your development changes:

    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!

  3. (Optional) Install pre-commit to run code style checks before each commit:

    pip install pre-commit
pre-commit install

    pre-commit checks can be disabled for a particular commit with git commit -n.

  4. 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:

    git add modified_files
git commit

    to record your changes in Git, then push the changes to your GitHub account with:

    git push -u origin my_feature

  5. Follow these instructions to create a pull request from your fork. This will send an email to the committers. You may want to consider sending an email to the mailing list for more visibility.

Note

If you are modifying a Cython module, you have to re-compile after modifications and before testing them:

pip install --no-build-isolation -e .

Use the --no-build-isolation flag to avoid compiling the whole project each time, only the files you have modified.

It is often helpful to keep your local feature branch synchronized with the latest changes of the main scikit-learn repository:

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.

Pull request checklist

Before a PR can be merged, it needs to be approved by two core developers. Please prefix the title of your pull request with [MRG] if the contribution is complete and should be subjected to a detailed review. An incomplete contribution – where you expect to do more work before receiving a full review – should be prefixed [WIP] (to indicate a work in progress) and changed to [MRG] when it matures. WIPs may be useful to: indicate you are working on something to avoid duplicated work, request broad review of functionality or API, or seek collaborators. WIPs often benefit from the inclusion of a task list in the PR description.

In order to ease the reviewing process, we recommend that your contribution complies with the following rules before marking a PR as [MRG]. The bolded ones are especially important:

  1. Give your pull request a helpful title that 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 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 Useful pytest aliases and flags.

  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 Documentation guidelines. The CI will also build the docs: please refer to Generated documentation on GitHub Actions.

  4. Tests are necessary for enhancements to be accepted. Bug-fixes or new features should be provided with non-regression tests. These tests verify the correct behavior of the fix or feature. In this manner, further modifications on the code base are granted to be consistent with the desired behavior. In the case of bug fixes, at the time of the PR, the non-regression tests should fail for the code base in the main branch and pass for the PR code.

  5. Run black to auto-format your code.

    black .

    See black’s editor integration documentation to configure your editor to run black.

  6. Run flake8 to make sure you followed the project coding conventions.

    flake8 .

  7. Follow the Coding guidelines.

  8. When applicable, use the validation tools and scripts in the sklearn.utils submodule. A list of utility routines available for developers can be found in the Utilities for Developers page.

  9. Often pull requests resolve one or more other issues (or pull requests). If merging your pull request means that some other issues/PRs should be closed, you should use keywords to create link to them (e.g., Fixes #1234; multiple issues/PRs are allowed as long as each one is preceded by a keyword). Upon merging, those issues/PRs will automatically be closed by GitHub. If your pull request is simply related to some other issues/PRs, create a link to them without using the keywords (e.g., See also #1234).

  10. PRs should often substantiate the change, through benchmarks of performance and efficiency (see Monitoring performance) 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/ directory for reference. Examples should demonstrate why the new functionality is useful in practice and, if possible, compare it to other methods available in scikit-learn.

  11. New features have some maintenance overhead. We expect PR authors to take part in the maintenance for the code they submit, at least initially. New features need to be illustrated with narrative documentation in the user guide, with small code snippets. If relevant, please also add references in the literature, with PDF links when possible.

  12. The user guide should also include expected time and space complexity of the algorithm and scalability, e.g. “this algorithm can scale to a large number of samples > 100000, but does not scale in dimensionality: n_features is expected to be lower than 100”.

You can also check our Code Review Guidelines to get an idea of what reviewers will expect.

You can check for common programming errors with the following tools:

  • Code with a good unittest coverage (at least 80%, better 100%), check with:

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

    see also Testing and improving test coverage

    Run static analysis with mypy:

    mypy sklearn

    must not produce new errors in your pull request. Using # type: ignore annotation can be a workaround for a few cases that are not supported by mypy, in particular,

    • when importing C or Cython modules

    • on properties with decorators

Bonus points for contributions that include a performance analysis with a benchmark script and profiling output (see Monitoring performance).

Also check out the How to optimize for speed guide for more details on profiling and Cython optimizations.

Note

The current state of the scikit-learn code base is not compliant with all of those guidelines, but we expect that enforcing those constraints on all new contributions will get the overall code base quality in the right direction.

Note

For two very well documented and more detailed guides on development workflow, please pay a visit to the Scipy Development Workflow - and the Astropy Workflow for Developers sections.

Continuous Integration (CI)

  • Azure pipelines are used for testing scikit-learn on Linux, Mac and Windows, with different dependencies and settings.

  • CircleCI is used to build the docs for viewing, for linting with flake8, and for testing with ARM64 / aarch64 on Linux

Please note that if one of the following markers appear in the latest commit message, the following actions are taken.

Commit Message Marker

Action Taken by CI

[ci skip]

CI is skipped completely

[cd build]

CD is run (wheels and source distribution are built)

[cd build gh]

CD is run only for GitHub Actions

[lint skip]

Azure pipeline skips linting

[scipy-dev]

Build & test with our dependencies (numpy, scipy, etc …) development builds

[nogil]

Build & test with the nogil experimental branches of CPython, Cython, NumPy, SciPy…

[icc-build]

Build & test with the Intel C compiler (ICC)

[pypy]

Build & test with PyPy

[doc skip]

Docs are not built

[doc quick]

Docs built, but excludes example gallery plots

[doc build]

Docs built including example gallery plots (very long)

Note that, by default, the documentation is built but only the examples that are directly modified by the pull request are executed.

Stalled pull 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”. 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 stalled pull request, we recommend that contributors follow the procedure described in the Stalled pull requests section rather than working directly on the issue.

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!

Documentation

We are glad to accept any sort of documentation: function docstrings, reStructuredText documents (like this one), tutorials, etc. reStructuredText documents live in the source code repository under the doc/ directory.

You can edit the documentation using any text editor, and then generate the HTML output by typing make from the doc/ directory. Alternatively, make html may be used to generate the documentation with the example gallery (which takes quite some time). The resulting HTML files will be placed in _build/html/stable and are viewable in a web browser.

Building the documentation

First, make sure you have properly installed the development version.

Building the documentation requires installing some additional packages:

pip install sphinx sphinx-gallery numpydoc matplotlib Pillow pandas \
            scikit-image packaging seaborn sphinx-prompt \
            sphinxext-opengraph

To build the documentation, you need to be in the doc folder:

cd doc

In the vast majority of cases, you only need to generate the full web site, without the example gallery:

make

The documentation will be generated in the _build/html/stable directory. To also generate the example gallery you can use:

make html

This will run all the examples, which takes a while. If you only want to generate a few examples, you can use:

EXAMPLES_PATTERN=your_regex_goes_here make html

This is particularly useful if you are modifying a few examples.

Set the environment variable NO_MATHJAX=1 if you intend to view the documentation in an offline setting.

To build the PDF manual, run:

make latexpdf

Warning

Sphinx version

While we do our best to have the documentation build under as many versions of Sphinx as possible, the different versions tend to behave slightly differently. To get the best results, you should use the same version as the one we used on CircleCI. Look at this github search to know the exact version.

Guidelines for writing documentation

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

Basically, to elaborate on the above, it is best to always start with a small paragraph with a hand-waving explanation of what the method does to the data. Then, it is very helpful to point out why the feature is useful and when it should be used - the latter also including “big O” (\(O\left(g\left(n\right)\right)\)) complexities of the algorithm, as opposed to just rules of thumb, as the latter can be very machine-dependent. If those complexities are not available, then rules of thumb may be provided instead.

Secondly, a generated figure from an example (as mentioned in the previous paragraph) should then be included to further provide some intuition.

Next, one or two small code examples to show its use can be added.

Next, any math and equations, followed by references, can be added to further the documentation. Not starting the documentation with the maths makes it more friendly towards users that are just interested in what the feature will do, as opposed to how it works “under the hood”.

Finally, follow the formatting rules below to make it consistently good:

  • Add “See Also” in docstrings for related classes/functions.

  • “See Also” in docstrings should be one line per reference, with a colon and an explanation, for example:

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

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

    n_clusters : int, default=3
    The number of clusters detected by the algorithm.

some_param : {'hello', 'goodbye'}, bool or int, default=True
    The parameter description goes here, which can be either a string
    literal (either `hello` or `goodbye`), a bool, or an int. The default
    value is True.

array_parameter : {array-like, sparse matrix} of shape (n_samples, n_features) or (n_samples,)
    This parameter accepts data in either of the mentioned forms, with one
    of the mentioned shapes. The default value is
    `np.ones(shape=(n_samples,))`.

list_param : list of int

typed_ndarray : ndarray of shape (n_samples,), dtype=np.int32

sample_weight : array-like of shape (n_samples,), default=None

multioutput_array : ndarray of shape (n_samples, n_classes) or list of such arrays

    In general have the following in mind:

    1. Use Python basic types. (bool instead of boolean)

    2. Use parenthesis for defining shapes: array-like of shape (n_samples,) or array-like of shape (n_samples, n_features)

    3. For strings with multiple options, use brackets: input: {'log', 'squared', 'multinomial'}

    4. 1D or 2D data can be a subset of {array-like, ndarray, sparse matrix, dataframe}. Note that array-like can also be a list, while ndarray is explicitly only a numpy.ndarray.

    5. Specify dataframe when “frame-like” features are being used, such as the column names.

    6. When specifying the data type of a list, use of as a delimiter: list of int. When the parameter supports arrays giving details about the shape and/or data type and a list of such arrays, you can use one of array-like of shape (n_samples,) or list of such arrays.

    7. When specifying the dtype of an ndarray, use e.g. dtype=np.int32 after defining the shape: ndarray of shape (n_samples,), dtype=np.int32. You can specify multiple dtype as a set: array-like of shape (n_samples,), dtype={np.float64, np.float32}. If one wants to mention arbitrary precision, use integral and floating rather than the Python dtype int and float. When both int and floating are supported, there is no need to specify the dtype.

    8. When the default is None, None only needs to be specified at the end with default=None. Be sure to include in the docstring, what it means for the parameter or attribute to be None.

  • For unwritten formatting rules, try to follow existing good works:

  • When editing reStructuredText (.rst) files, try to keep line length under 80 characters when possible (exceptions include links and tables).

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

  • Before submitting your pull request check if your modifications have introduced new sphinx warnings and try to fix them.

Generated 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 at the bottom of your PR page and look for the “Check the rendered docs here!” link:

../_images/generated-doc-ci.png

Testing and improving test coverage

High-quality unit testing is a corner-stone of the scikit-learn development process. For this purpose, we use the pytest 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 Pull request checklist.

We expect code coverage of new features to be at least around 90%.

Workflow to improve test coverage

To test code coverage, you need to install the coverage package in addition to pytest.

  1. Run ‘make test-coverage’. The output lists for each file the line

    numbers that are not tested.

  2. Find a low hanging fruit, looking at which lines are not tested,

    write or adapt a test specifically for these lines.

  3. Loop.

Monitoring performance

This section is heavily inspired from the pandas documentation.

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 to monitor the performance of a selection of common estimators and functions. You can view these benchmarks on the scikit-learn benchmark page. The corresponding benchmark suite can be found in the scikit-learn/asv_benchmarks directory.

To use all features of asv, you will need either conda or virtualenv. For more details please check the asv installation webpage.

First of all you need to install the development version of asv:

pip install git+https://github.com/airspeed-velocity/asv

and change your directory to asv_benchmarks/:

cd asv_benchmarks/

The benchmark suite is configured to run against your local clone of scikit-learn. Make sure it is up to date:

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:

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:

asv continuous -E virtualenv -b LogisticRegression upstream/main HEAD

You can also specify a whole module to benchmark:

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 :

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:

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:

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:

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:

asv show

and to see the report of a specific run:

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.

Issue Tracker Tags

All issues and pull requests on the GitHub issue tracker should have (at least) one of the following tags:

Bug / Crash:

Something is happening that clearly shouldn’t happen. Wrong results as well as unexpected errors from estimators go here.

Cleanup / Enhancement:

Improving performance, usability, consistency.

Documentation:

Missing, incorrect or sub-standard documentations and examples.

New Feature:

Feature requests and pull requests implementing a new feature.

There are four other tags to help new contributors:

good first issue:

This issue is ideal for a first contribution to scikit-learn. Ask for help if the formulation is unclear. If you have already contributed to scikit-learn, look at Easy issues instead.

Easy:

This issue can be tackled without much prior experience.

Moderate:

Might need some knowledge of machine learning or the package, but is still approachable for someone new to the project.

help wanted:

This tag marks an issue which currently lacks a contributor or a PR that needs another contributor to take over the work. These issues can range in difficulty, and may not be approachable for new contributors. Note that not all issues which need contributors will have this tag.

Maintaining backwards compatibility

Deprecation

If any publicly accessible method, function, attribute or parameter is renamed, we still support the old one for two releases and issue a deprecation warning when it is called/passed/accessed. E.g., if the function zero_one is renamed to zero_one_loss, we add the decorator deprecated (from sklearn.utils) to zero_one and call zero_one_loss from that function:

from ..utils import deprecated

def zero_one_loss(y_true, y_pred, normalize=True):
    # actual implementation
    pass

@deprecated("Function 'zero_one' was renamed to 'zero_one_loss' "
            "in version 0.13 and will be removed in release 0.15. "
            "Default behavior is changed from 'normalize=False' to "
            "'normalize=True'")
def zero_one(y_true, y_pred, normalize=False):
    return zero_one_loss(y_true, y_pred, normalize)

If an attribute is to be deprecated, use the decorator deprecated on a property. Please note that the property decorator should be placed before the deprecated decorator for the docstrings to be rendered properly. E.g., renaming an attribute labels_ to classes_ can be done as:

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

If a parameter has to be deprecated, a FutureWarning warning must be raised too. In the following example, k is deprecated and renamed to n_clusters:

import warnings

def example_function(n_clusters=8, k='deprecated'):
    if k != 'deprecated':
        warnings.warn("'k' was renamed to n_clusters in version 0.13 and "
                      "will be removed in 0.15.",
                      FutureWarning)
        n_clusters = k

When the change is in a class, we validate and raise warning in fit:

import warnings

class ExampleEstimator(BaseEstimator):
    def __init__(self, n_clusters=8, k='deprecated'):
        self.n_clusters = n_clusters
        self.k = k

    def fit(self, X, y):
        if self.k != 'deprecated':
            warnings.warn("'k' was renamed to n_clusters in version 0.13 and "
                          "will be removed in 0.15.",
                          FutureWarning)
            self._n_clusters = self.k
        else:
            self._n_clusters = self.n_clusters

As in these examples, the warning message should always give both the version in which the deprecation happened and the version in which the old behavior will be removed. If the deprecation happened in version 0.x-dev, the message should say deprecation occurred in version 0.x and the removal will be in 0.(x+2), so that users will have enough time to adapt their code to the new behaviour. For example, if the deprecation happened in version 0.18-dev, the message should say it happened in version 0.18 and the old behavior will be removed in version 0.20.

In addition, a deprecation note should be added in the docstring, recalling the same information as the deprecation warning as explained above. Use the .. deprecated:: directive:

.. deprecated:: 0.13
   ``k`` was renamed to ``n_clusters`` in version 0.13 and will be removed
   in 0.15.

What’s more, a deprecation requires a test which ensures that the warning is raised in relevant cases but not in other cases. The warning should be caught in all other tests (using e.g., @pytest.mark.filterwarnings), and there should be no warning in the examples.

Change the default value of a parameter

If the default value of a parameter needs to be changed, please replace the default value with a specific value (e.g., warn) and raise FutureWarning when users are using the default value. The following example assumes that the current version is 0.20 and that we change the default value of n_clusters from 5 (old default for 0.20) to 10 (new default for 0.22):

import warnings

def example_function(n_clusters='warn'):
    if n_clusters == 'warn':
        warnings.warn("The default value of n_clusters will change from "
                      "5 to 10 in 0.22.", FutureWarning)
        n_clusters = 5

When the change is in a class, we validate and raise warning in fit:

import warnings

class ExampleEstimator:
    def __init__(self, n_clusters='warn'):
        self.n_clusters = n_clusters

    def fit(self, X, y):
        if self.n_clusters == 'warn':
          warnings.warn("The default value of n_clusters will change from "
                        "5 to 10 in 0.22.", FutureWarning)
          self._n_clusters = 5

Similar to deprecations, the warning message should always give both the version in which the change happened and the version in which the old behavior will be removed.

The 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:

.. 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 Guidelines

Reviewing code contributed to the project as PRs is a crucial component of scikit-learn development. We encourage anyone to start reviewing code of other developers. The code review process is often highly educational for everybody involved. This is particularly appropriate if it is a feature you would like to use, and so can respond critically about whether the PR meets your needs. While each pull request needs to be signed off by two core developers, you can speed up this process by providing your feedback.

Note

The difference between an objective improvement and a subjective nit isn’t always clear. Reviewers should recall that code review is primarily about reducing risk in the project. When reviewing code, one should aim at preventing situations which may require a bug fix, a deprecation, or a retraction. Regarding docs: typos, grammar issues and disambiguations are better addressed immediately.

Here are a few important aspects that need to be covered in any code review, from high-level questions to a more detailed check-list.

  • Do we want this in the library? Is it likely to be used? Do you, as a scikit-learn user, like the change and intend to use it? Is it in the scope of scikit-learn? Will the cost of maintaining a new feature be worth its benefits?

  • Is the code consistent with the API of scikit-learn? Are public functions/classes/parameters well named and intuitively designed?

  • Are all public functions/classes and their parameters, return types, and stored attributes named according to scikit-learn conventions and documented clearly?

  • Is any new functionality described in the user-guide and illustrated with examples?

  • Is every public function/class tested? Are a reasonable set of parameters, their values, value types, and combinations tested? Do the tests validate that the code is correct, i.e. doing what the documentation says it does? If the change is a bug-fix, is a non-regression test included? Look at this 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 Documentation section for more details), and are the plots instructive?

Standard replies for reviewing includes some frequent comments that reviewers may make.

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 Code Review Guidelines, 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.

Reading the existing code base

Reading and digesting an existing code base is always a difficult exercise that takes time and experience to main. Even though we try to write simple code in general, understanding the code can seem overwhelming at first, given the sheer size of the project. Here is a list of tips that may help make this task easier and faster (in no particular order).

  • Get acquainted with the APIs of scikit-learn objects: understand what fit, predict, 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 fit methods. Sometimes, only a very small portion of the code is doing the actual job. For example looking at the fit() method of LinearRegression, what you’re looking for might just be the call the scipy.linalg.lstsq, but it is buried into multiple lines of input checking and the handling of different kinds of parameters.

  • Due to the use of Inheritance, some methods may be implemented in parent classes. All estimators inherit at least from BaseEstimator, and from a Mixin class (e.g. 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. out is then an iterable containing the values returned by some_function for each call.

  • We use Cython to write fast code. Cython code is located in .pyx and .pxd files. Cython code has a more C-like flavor: we use pointers, perform manual memory allocation, etc. Having some minimal experience in C / C++ is pretty much mandatory here.

  • Master your tools.

    • With such a big project, being efficient with your favorite editor or IDE goes a long way towards digesting the code base. Being able to quickly jump (or peek) to a function/class/attribute definition helps a lot. So does being able to quickly see where a given name is used in a file.

    • git 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). This can also be done directly on GitHub. 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.

    git config blame.ignoreRevsFile .git-blame-ignore-revs

    Find out more information in black’s documentation for avoiding ruining git blame.