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CONTRIBUTING.md

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How to contribute

Did you find a bug?

  • Ensure the bug was not already reported by searching on GitHub under Issues.
  • If you're unable to find an open issue addressing the problem, open a new one. Be sure to include a title and clear description, as much relevant information as possible, and a code sample or an executable test case demonstrating the expected behavior that is not occurring.
  • Be sure to add the complete error messages.

Do you have a feature request?

  • Ensure that it hasn't been yet implemented in the main branch of the repository and that there's not an Issue requesting it yet.
  • Open a new issue and make sure to describe it clearly, mention how it improves the project and why its useful.

Do you want to fix a bug or implement a feature?

Bug fixes and features are added through pull requests (PRs).

PR submission guidelines

  • Keep each PR focused. While it's more convenient, do not combine several unrelated fixes together. Create as many branches as needing to keep each PR focused.
  • Ensure that your PR includes a test that fails without your patch, and passes with it.
  • Ensure the PR description clearly describes the problem and solution. Include the relevant issue number if applicable.
  • Do not mix style changes/fixes with "functional" changes. It's very difficult to review such PRs and it most likely get rejected.
  • Do not add/remove vertical whitespace. Preserve the original style of the file you edit as much as you can.
  • Do not turn an already submitted PR into your development playground. If after you submitted PR, you discovered that more work is needed - close the PR, do the required work and then submit a new PR. Otherwise each of your commits requires attention from maintainers of the project.
  • If, however, you submitted a PR and received a request for changes, you should proceed with commits inside that PR, so that the maintainer can see the incremental fixes and won't need to review the whole PR again. In the exception case where you realize it'll take many many commits to complete the requests, then it's probably best to close the PR, do the work and then submit it again. Use common sense where you'd choose one way over another.

Local setup for working on a PR

Clone the repository

  • HTTPS: git clone https://github.com/Nixtla/statsforecast.git
  • SSH: git clone git@github.com:Nixtla/statsforecast.git
  • GitHub CLI: gh repo clone Nixtla/statsforecast

Install git hooks

Before doing any changes to the code, please install the git hooks that run automatic scripts during each commit and merge to strip the notebooks of superfluous metadata (and avoid merge conflicts).

Installation instructions can be found here: https://nbdev.fast.ai/getting_started.html#install

Once installed via pip or conda run the following command in CLI:

nbdev_install_hooks

Anaconda Setup

1. Set up a conda environment

The repo comes with an environment.yml file in dev directory, which contains the libraries needed to run all the tests. In order to set up the environment you must have conda installed, we recommend miniconda.

Once you have conda go to the top level directory of the repository and run:

conda env create -f dev/environment.yml
2. Install the library

Once you have your environment setup, activate it using conda activate statsforecast and then install the library in editable mode using pip install -e ".[dev]"

Docker setup

A Docker alternative is provided in the dev directory.

Side Notes:

  • It is not recommended to install the environment.yml file via Docker as it requires additional RAM for setup.
  • requirements.txt was created as an alternative to environment.yml for installation purposes; it is not recommended to use it as setup file as in some cases installation of prophet library fails (tested fails: MacOS).

In the project's root directory it is possible to find Makefile that serves as command center. It offers several commands:

  • build: Command to build Docker file [optional]
  • run: Run jupyter notebook using Docker image [build dependent]
  • buildless: Run jupyter notebook using Docker image without building the image
  • address: Show the ipaddress and port of Jupyter Notebook
  • stop: Stops statsforecast container
  • remove: Deletes statsforecast Docker image

Preview Changes

You can preview changes in your local browser before pushing by using the nbdev_preview. This command also installs Quarto if not available.

Building the library

The library is built using the notebooks contained in the nbs folder. If you want to make any changes to the library you have to find the relevant notebook, make your changes and then call nbdev_export.

Running tests

If you're working on the local interface you can just use nbdev_test.

Linters

This project uses a couple of linters to validate different aspects of the code. Before opening a PR, please make sure that it passes all the linting tasks by following the next steps.

Run the linting tasks

  • mypy statsforecast/
  • flake8 --select=F statsforecast/

Cleaning notebooks

Since the notebooks output cells can vary from run to run (even if they produce the same outputs) the notebooks are cleaned before committing them. Please make sure to run nbdev_clean before committing your changes.

Do you want to contribute to the documentation?

  • Docs are automatically created from the notebooks in the nbs folder.
  • In order to modify the documentation:
    1. Find the relevant notebook.
    2. Make your changes.
    3. Run all cells.
    4. If you are modifying library notebooks (not in nbs/examples), clean all outputs using Edit > Clear All Outputs.
    5. Run nbdev_preview.
    6. Clean the notebook metadata using nbdev_clean.