Developers’ Tips and Tricks#

Productivity and sanity-preserving tips#

In this section we gather some useful advice and tools that may increase your quality-of-life when reviewing pull requests, running unit tests, and so forth. Some of these tricks consist of userscripts that require a browser extension such as TamperMonkey or GreaseMonkey; to set up userscripts you must have one of these extensions installed, enabled and running. We provide userscripts as GitHub gists; to install them, click on the “Raw” button on the gist page.

Folding and unfolding outdated diffs on pull requests#

GitHub hides discussions on PRs when the corresponding lines of code have been changed in the mean while. This userscript provides a shortcut (Control-Alt-P at the time of writing but look at the code to be sure) to unfold all such hidden discussions at once, so you can catch up.

Checking out pull requests as remote-tracking branches#

In your local fork, add to your .git/config, under the [remote "upstream"] heading, the line:

fetch = +refs/pull/*/head:refs/remotes/upstream/pr/*

You may then use git checkout pr/PR_NUMBER to navigate to the code of the pull-request with the given number. (Read more in this gist.)

Display code coverage in pull requests#

To overlay the code coverage reports generated by the CodeCov continuous integration, consider this browser extension. The coverage of each line will be displayed as a color background behind the line number.

Useful pytest aliases and flags#

The full test suite takes fairly long to run. For faster iterations, it is possibly to select a subset of tests using pytest selectors. In particular, one can run a single test based on its node ID:

pytest -v sklearn/linear_model/tests/test_logistic.py::test_sparsify

or use the -k pytest parameter to select tests based on their name. For instance,:

pytest sklearn/tests/test_common.py -v -k LogisticRegression

will run all common tests for the LogisticRegression estimator.

When a unit test fails, the following tricks can make debugging easier:

  1. The command line argument pytest -l instructs pytest to print the local variables when a failure occurs.

  2. The argument pytest --pdb drops into the Python debugger on failure. To instead drop into the rich IPython debugger ipdb, you may set up a shell alias to:

    pytest --pdbcls=IPython.terminal.debugger:TerminalPdb --capture no

Other pytest options that may become useful include:

  • -x which exits on the first failed test,

  • --lf to rerun the tests that failed on the previous run,

  • --ff to rerun all previous tests, running the ones that failed first,

  • -s so that pytest does not capture the output of print() statements,

  • --tb=short or --tb=line to control the length of the logs,

  • --runxfail also run tests marked as a known failure (XFAIL) and report errors.

Since our continuous integration tests will error if FutureWarning isn’t properly caught, it is also recommended to run pytest along with the -Werror::FutureWarning flag.

Standard replies for reviewing#

It may be helpful to store some of these in GitHub’s saved replies for reviewing:

Issue: Usage questions

You are asking a usage question. The issue tracker is for bugs and new features. For usage questions, it is recommended to try [Stack Overflow](https://stackoverflow.com/questions/tagged/scikit-learn) or [the Mailing List](https://mail.python.org/mailman/listinfo/scikit-learn).

Unfortunately, we need to close this issue as this issue tracker is a communication tool used for the development of scikit-learn. The additional activity created by usage questions crowds it too much and impedes this development. The conversation can continue here, however there is no guarantee that it will receive attention from core developers.

Issue: You’re welcome to update the docs

Please feel free to offer a pull request updating the documentation if you feel it could be improved.

Issue: Self-contained example for bug

Please provide [self-contained example code](https://scikit-learn.org/dev/developers/minimal_reproducer.html), including imports and data (if possible), so that other contributors can just run it and reproduce your issue. Ideally your example code should be minimal.

Issue: Software versions

To help diagnose your issue, please paste the output of:
```py
import sklearn; sklearn.show_versions()
```
Thanks.

Issue: Code blocks

Readability can be greatly improved if you [format](https://help.github.com/articles/creating-and-highlighting-code-blocks/) your code snippets and complete error messages appropriately. For example:

    ```python
    print(something)
    ```

generates:

```python
print(something)
```

And:

    ```pytb
    Traceback (most recent call last):
        File "<stdin>", line 1, in <module>
    ImportError: No module named 'hello'
    ```

generates:

```pytb
Traceback (most recent call last):
    File "<stdin>", line 1, in <module>
ImportError: No module named 'hello'
```

You can edit your issue descriptions and comments at any time to improve readability. This helps maintainers a lot. Thanks!

Issue/Comment: Linking to code

Friendly advice: for clarity's sake, you can link to code like [this](https://help.github.com/articles/creating-a-permanent-link-to-a-code-snippet/).

Issue/Comment: Linking to comments

Please use links to comments, which make it a lot easier to see what you are referring to, rather than just linking to the issue. See [this](https://stackoverflow.com/questions/25163598/how-do-i-reference-a-specific-issue-comment-on-github) for more details.

PR-NEW: Better description and title

Thanks for the pull request! Please make the title of the PR more descriptive. The title will become the commit message when this is merged. You should state what issue (or PR) it fixes/resolves in the description using the syntax described [here](https://scikit-learn.org/dev/developers/contributing.html#contributing-pull-requests).

PR-NEW: Fix #

Please use "Fix #issueNumber" in your PR description (and you can do it more than once). This way the associated issue gets closed automatically when the PR is merged. For more details, look at [this](https://github.com/blog/1506-closing-issues-via-pull-requests).

PR-NEW or Issue: Maintenance cost

Every feature we include has a [maintenance cost](https://scikit-learn.org/dev/faq.html#why-are-you-so-selective-on-what-algorithms-you-include-in-scikit-learn). Our maintainers are mostly volunteers. For a new feature to be included, we need evidence that it is often useful and, ideally, [well-established](https://scikit-learn.org/dev/faq.html#what-are-the-inclusion-criteria-for-new-algorithms) in the literature or in practice. Also, we expect PR authors to take part in the maintenance for the code they submit, at least initially. That doesn't stop you implementing it for yourself and publishing it in a separate repository, or even [scikit-learn-contrib](https://scikit-learn-contrib.github.io).

PR-WIP: What’s needed before merge?

Please clarify (perhaps as a TODO list in the PR description) what work you believe still needs to be done before it can be reviewed for merge. When it is ready, please prefix the PR title with `[MRG]`.

PR-WIP: Regression test needed

Please add a [non-regression test](https://en.wikipedia.org/wiki/Non-regression_testing) that would fail at main but pass in this PR.

PR-WIP: PEP8

You have some [PEP8](https://www.python.org/dev/peps/pep-0008/) violations, whose details you can see in the Circle CI `lint` job. It might be worth configuring your code editor to check for such errors on the fly, so you can catch them before committing.

PR-MRG: Patience

Before merging, we generally require two core developers to agree that your pull request is desirable and ready. [Please be patient](https://scikit-learn.org/dev/faq.html#why-is-my-pull-request-not-getting-any-attention), as we mostly rely on volunteered time from busy core developers. (You are also welcome to help us out with [reviewing other PRs](https://scikit-learn.org/dev/developers/contributing.html#code-review-guidelines).)

PR-MRG: Add to what’s new

Please add an entry to the change log at `doc/whats_new/v*.rst`. Like the other entries there, please reference this pull request with `:pr:` and credit yourself (and other contributors if applicable) with `:user:`.

PR: Don’t change unrelated

Please do not change unrelated lines. It makes your contribution harder to review and may introduce merge conflicts to other pull requests.

Debugging CI issues#

CI issues may arise for a variety of reasons, so this is by no means a comprehensive guide, but rather a list of useful tips and tricks.

Using a lock-file to get an environment close to the CI#

conda-lock can be used to create a conda environment with the exact same conda and pip packages as on the CI. For example, the following command will create a conda environment named scikit-learn-doc that is similar to the CI:

conda-lock install -n scikit-learn-doc build_tools/circle/doc_linux-64_conda.lock

Note

It only works if you have the same OS as the CI build (check platform: in the lock-file). For example, the previous command will only work if you are on a Linux machine. Also this may not allow you to reproduce some of the issues that are more tied to the particularities of the CI environment, for example CPU architecture reported by OpenBLAS in sklearn.show_versions().

If you don’t have the same OS as the CI build you can still create a conda environment from the right environment yaml file, although it won’t be as close as the CI environment as using the associated lock-file. For example for the doc build:

conda env create -n scikit-learn-doc -f build_tools/circle/doc_environment.yml -y

This may not give you exactly the same package versions as in the CI for a variety of reasons, for example:

  • some packages may have had new releases between the time the lock files were last updated in the main branch and the time you run the conda create command. You can always try to look at the version in the lock-file and specify the versions by hand for some specific packages that you think would help reproducing the issue.

  • different packages may be installed by default depending on the OS. For example, the default BLAS library when installing numpy is OpenBLAS on Linux and MKL on Windows.

Also the problem may be OS specific so the only way to be able to reproduce would be to have the same OS as the CI build.

Debugging memory errors in Cython with valgrind#

While python/numpy’s built-in memory management is relatively robust, it can lead to performance penalties for some routines. For this reason, much of the high-performance code in scikit-learn is written in cython. This performance gain comes with a tradeoff, however: it is very easy for memory bugs to crop up in cython code, especially in situations where that code relies heavily on pointer arithmetic.

Memory errors can manifest themselves a number of ways. The easiest ones to debug are often segmentation faults and related glibc errors. Uninitialized variables can lead to unexpected behavior that is difficult to track down. A very useful tool when debugging these sorts of errors is valgrind.

Valgrind is a command-line tool that can trace memory errors in a variety of code. Follow these steps:

  1. Install valgrind on your system.

  2. Download the python valgrind suppression file: valgrind-python.supp.

  3. Follow the directions in the README.valgrind file to customize your python suppressions. If you don’t, you will have spurious output coming related to the python interpreter instead of your own code.

  4. Run valgrind as follows:

    valgrind -v --suppressions=valgrind-python.supp python my_test_script.py

The result will be a list of all the memory-related errors, which reference lines in the C-code generated by cython from your .pyx file. If you examine the referenced lines in the .c file, you will see comments which indicate the corresponding location in your .pyx source file. Hopefully the output will give you clues as to the source of your memory error.

For more information on valgrind and the array of options it has, see the tutorials and documentation on the valgrind web site.

Building and testing for the ARM64 platform on a x86_64 machine#

ARM-based machines are a popular target for mobile, edge or other low-energy deployments (including in the cloud, for instance on Scaleway or AWS Graviton).

Here are instructions to setup a local dev environment to reproduce ARM-specific bugs or test failures on a x86_64 host laptop or workstation. This is based on QEMU user mode emulation using docker for convenience (see multiarch/qemu-user-static).

Note

The following instructions are illustrated for ARM64 but they also apply to ppc64le, after changing the Docker image and Miniforge paths appropriately.

Prepare a folder on the host filesystem and download the necessary tools and source code:

mkdir arm64
pushd arm64
wget https://github.com/conda-forge/miniforge/releases/latest/download/Miniforge3-Linux-aarch64.sh
git clone https://github.com/scikit-learn/scikit-learn.git

Use docker to install QEMU user mode and run an ARM64v8 container with access to your shared folder under the /io mount point:

docker run --rm --privileged multiarch/qemu-user-static --reset -p yes
docker run -v`pwd`:/io --rm -it arm64v8/ubuntu /bin/bash

In the container, install miniforge3 for the ARM64 (a.k.a. aarch64) architecture:

bash Miniforge3-Linux-aarch64.sh
# Choose to install miniforge3 under: `/io/miniforge3`

Whenever you restart a new container, you will need to reinit the conda env previously installed under /io/miniforge3:

/io/miniforge3/bin/conda init
source /root/.bashrc

as the /root home folder is part of the ephemeral docker container. Every file or directory stored under /io is persistent on the other hand.

You can then build scikit-learn as usual (you will need to install compiler tools and dependencies using apt or conda as usual). Building scikit-learn takes a lot of time because of the emulation layer, however it needs to be done only once if you put the scikit-learn folder under the /io mount point.

Then use pytest to run only the tests of the module you are interested in debugging.

The Meson Build Backend#

Since scikit-learn 1.5.0 we use meson-python as the build tool. Meson is a new tool for scikit-learn and the PyData ecosystem. It is used by several other packages that have written good guides about what it is and how it works.

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