nlp-recipes/CONTRIBUTING.md

Contribution Guidelines

Contribution are welcome! Here's a few things to know:

• Contribution Guidelines
  • Microsoft Contributor License Agreement
  • Steps to Contributing
  • Coding Guidelines
  • Code of Conduct
    • Do not point fingers
    • Provide code feedback based on evidence
    • Ask questions do not give answers

Microsoft Contributor License Agreement

Most contributions require you to agree to a Contributor License Agreement (CLA) declaring that you have the right to, and actually do, grant us the rights to use your contribution. For details, visit https://cla.microsoft.com.

When you submit a pull request, a CLA-bot will automatically determine whether you need to provide a CLA and decorate the PR appropriately (e.g., label, comment). Simply follow the instructions provided by the bot. You will only need to do this once across all repos using our CLA.

Steps to Contributing

Here are the basic steps to get started with your first contribution. Please reach out with any questions.

1. Use open issues to discuss the proposed changes. Create an issue describing changes if necessary to collect feedback. Also, please use provided labels to tag issues so everyone can easily sort issues of interest.

2. Fork the repo so you can make and test local changes.

3. Create a new branch for the issue. We suggest prefixing the branch with your username and then a descriptive title: (e.g. gramhagen/update_contributing_docs)

4. Create a test that replicates the issue.

5. Make code changes.

6. Ensure unit tests pass and code style / formatting is consistent (see wiki for more details).

7. We use pre-commit package to run our pre-commit hooks. We use black formatter and flake8 linting on each commit. In order to set up pre-commit on your machine, follow the steps here, please note that you only need to run these steps the first time you use pre-commit for this project.

   • Update your conda environment, pre-commit is part of the yaml file or just do

    $ pip install pre-commit
   

   • Set up pre-commit by running following command, this will put pre-commit under your .git/hooks directory.

   $ pre-commit install
   

   $ git commit -m "message" 
   

   • Each time you commit, git will run the pre-commit hooks (black and flake8 for now) on any python files that are getting committed and are part of the git index. If black modifies/formats the file, or if flake8 finds any linting errors, the commit will not succeed. You will need to stage the file again if black changed the file, or fix the issues identified by flake8 and and stage it again.

   • To run pre-commit on all files just run

   $ pre-commit run --all-files
   

8. Create a pull request against staging branch.

Note: We use the staging branch to land all new features, so please remember to create the Pull Request against staging.

Once the features included in a milestone are complete we will merge staging into master and make a release. See the wiki for more detail about our merge strategy.

Coding Guidelines

We strive to maintain high quality code to make the utilities in the repository easy to understand, use, and extend. We also work hard to maintain a friendly and constructive environment. We've found that having clear expectations on the development process and consistent style helps to ensure everyone can contribute and collaborate effectively.

Please review the coding guidelines wiki page to see more details about the expectations for development approach and style.

Code of Conduct

This project has adopted the Microsoft Open Source Code of Conduct.

For more information see the Code of Conduct FAQ or contact opencode@microsoft.com with any additional questions or comments.

Apart from the official Code of Conduct developed by Microsoft, in the Recommenders team we adopt the following behaviors, to ensure a great working environment:

Do not point fingers

Let’s be constructive.

Click here to see some examples

"This method is missing docstrings" instead of "YOU forgot to put docstrings".

Provide code feedback based on evidence

When making code reviews, try to support your ideas based on evidence (papers, library documentation, stackoverflow, etc) rather than your personal preferences.

Click here to see some examples

"When reviewing this code, I saw that the Python implementation the metrics are based on classes, however, scikit-learn and tensorflow use functions. We should follow the standard in the industry."

Ask questions do not give answers

Try to be empathic.

Click here to see some examples

• Would it make more sense if ...?
• Have you considered this ... ?