Contributing to julearn

Setting up the development environment

Before your first contribution, you might want to install all the required tools to be able to test that everything is according to the required development guidelines.

Download the source code

Choose a folder where you will place the code and clone the julearn Github repository:

git clone https://github.com/juaml/julearn.git

Create a virtual environment

This step can be omitted, but it is highly recommended, as the development version of julearn can interfere with the other versions that you might use in your projects.

Check the venv or conda env documentation on how to create one.

Install the requirements

The required packages for development are specified within 3 files in the repository, according to the different build stages.

The following is an example on how to install them using pip:

cd julearn
pip install -r requirements.txt
pip install -r test-requirements.txt
pip install -r docs-requirements.txt
pip install -r dev-requirements.txt

Install julearn

To install julearn in editable/development mode, simple run:

python setup.py develop

Now you are ready for the first contribution.

Note

Every time that you run setup.py develop, the version is going to be automatically set based on the git history. Nevertheless, this change should not be committed (changes to _version.py). Running git stash at this point will forget the local changes to _version.py.

Contributing with a pull request

The first step, before doing any edit to the code, is to find the issue in the julearn Github repository that you will address and explain what your contribution will do. If it does not exist, you can create it. The idea is that after your pull request is merged, the issue is fixed/closed.

Fork the julearn Github repository

In order to push into a repository, you will need to have push permissions. Github provides a way of doing this by forking (creating a copy) the repository into one under your Github user account in.

Simply go to julearn Github and click on the ^Fork^ button on the top-right corner.

Once you do this, there will be a julearn repository under your username. For example fraimondo/julearn.

Now add this as an other remote repository to the current julearn local git. Replace fraimondo with your Github username.

git remote add fraimondo git@github.com:fraimondo/julearn.git

Create a branch for your modifications

All your modifications that address this issue will be placed in a new branch. First, make sure you have the latest commit in your local main branch.

git checkout main
git pull --rebase origin main

Then, execute the following command to create a new branch, replacing <BRANCH_NAME> with a name that relates to the issue.

git checkout -b <BRANCH_NAME>

Do the changes

Simply use your preferred code editor to do the modifications you want.

Note

You must also add a line in docs/changes/latest.inc where you briefly explain the modifications you made. If it’s your first contribution, also add yourself to docs/changes/contributors.inc.

Commit and Push

This is the standard git workflow. Replace <USERNAME>> with your Github username.

# Add the files you created with git add # Commit the changes with git commit # Commit the changes with git push <USERNAME> <BRANCH_NAME>

Test and build the documentation

Before creating the pull request (PR), make sure that all the test pass and the documentation can be correctly built.

To check the code style, run:

flake8

To run the spell check, run:

codespell julearn/ docs/ examples/

To run the test, execute:

pytest -v

To build the docs:

cd docs
make html

To view the documentation, open docs/_build/html/index.html.

In case you remove some files or change their filename you can run into errors when using make html. In this situation you can use make clean to clean up the already build files and then rerun make html.

If any of this fails, go back to Do the changes

Create a pull request

Now that you are happy with your contribution, just navigate to your fork of the julearn repository in Github and click on the ^Compare & pull request^ button.

Fill in the pull request message and you will be in contact with the julearn manitainers who will review your contribution. If they suggest any modification, go back to Do the changes.

Once everyone is happy, your modifications will be included in the development version and later on, in the release version.

Writing Examples

Examples are run and displayed in HTML format using sphinx gallery. There are two sub directories in the examples directory: basic and advanced. To add an example, just create a .py file that starts either with plot_ or run_, dependending on whether the example generates a figure or not.

The first lines of the example should be a python block comment with a title, a description of the example an the following include directive to be able to use the links.

The format use for text is RST. Check the sphinx RST reference for more details.

Example of the first lines:

"""
Simple Binary Classification
============================

This example uses the 'iris' dataset and performs a simple binary
classification using a Support Vector Machine classifier.

.. include:: ../../links.inc
"""

The rest of the script will be executed as normal python code. In order to render the output and embed formatted text within the code, you need to add a 79 # (a full line) at the point in which you want to render and add text. Each line of text shall be preceded with #. The code that it’s not commented will be executed.

The following example will create 3 texts and render the output between the texts.

###############################################################################
# Imports needed for the example
from seaborn import load_dataset
from julearn import run_cross_validation
from julearn.utils import configure_logging

###############################################################################
df_iris = load_dataset('iris')

###############################################################################
# The dataset has three kind of species. We will keep two to perform a binary
# classification.
df_iris = df_iris[df_iris['species'].isin(['versicolor', 'virginica'])]

Finally, when the example is done, you can run as a normal python script. To generate the HTML, just build the docs:

cd docs
make html