Contribute to skglm#

skglm is a continuous endeavour that relies on community efforts to last and evolve. Your contribution is welcome and highly valuable. You can help with

bug report

skglm runs unit tests on the codebase to prevent bugs. Help us tighten these tests by reporting any bug that you encounter. To do so, use the issue section.

feature request

We are constantly improving skglm and we would like to align that with the user needs. We highly appreciate any suggestion to extend or add new features to skglm. You can use the the issue section to make suggestions.

pull request

You may have fixed a bug, added a feature, or even fixed a small typo in the documentation… You can submit a pull request to integrate your changes and we will reach out to you shortly. If this is your first pull request, you can refer to this scikit-learn guide.

As part of the scikit-learn-contrib GitHub organization, we adopt the scikit-learn code of conduct.

Note

If you are willing to contribute with code to skglm, check the section below to learn how to install the development version.

Setup skglm on your local machine#

Here are the key steps to help you setup skglm on your machine in case you want to contribute with code or documentation.

  1. Fork the repository and run the following command to clone it on your local machine, make sure to replace {YOUR_GITHUB_USERNAME} with your GitHub username

$ git clone https://github.com/{YOUR_GITHUB_USERNAME}/skglm

  1. cd to skglm directory and install it in edit mode by running

$ cd skglm
$ pip install -e .

  1. To build the documentation locally, run

$ cd doc
$ pip install .[doc]
$ make html

$ cd doc
$ pip install .[doc]
$ make html-noplot