Development happens against the
main branch following the
GitHub flow model.
Contributors should use their own forks of the repository. In their fork, they
create feature branches off of
main, and their pull requests should
main branch. Maintainers are responsible for prompt
review of pull requests.
Pull requests against
main trigger automated tests that are run
through Azure DevOps, GitHub Actions, and CircleCI. Additional test suites are
run periodically. When adding new code paths or features, tests are a
requirement to complete a pull request. They should be added in the
Documentation should be provided with pull requests that add or change functionality. This includes comments in the code itself, docstrings, and user guides. For exceptions to this rule the pull request author should coordinate with a maintainer. For changes that fix bugs, add new features, change APIs, etc., i.e., for changes that are relevant to developers and/or users please also add an entry in CHANGES.md in the section corresponding to the next release, since that’s where your change will be included. If you’re a new contributor please also add yourself to AUTHORS.md.
Advanced installation instructions¶
While working on Fairlearn itself you may want to install it in editable mode. This allows you to test the changed functionality. First, clone the repository locally via
git clone firstname.lastname@example.org:fairlearn/fairlearn.git
To install in editable mode using
pip install -e .
from the repository root path.
To verify that the code works as expected run
python ./scripts/install_requirements.py --pinned False python -m pytest -s ./test/unit
Fairlearn currently includes plotting functionality that requires the
matplotlib package to be installed. Since this is for a niche use case
Fairlearn comes without
matplotlib by default. To install Fairlearn
with its full feature set simply append
customplots to the install
pip install -e .[customplots]
The Requirements Files¶
The prerequisites for Fairlearn are split between three separate files:
files are consumed
by setup.py to specify the dependencies to be
documented in the wheel files.
To help simplify installation of the prerequisites, we have the
script which runs
pip install on all three of the above files.
This script will also optionally pin the requirements to any lower bound specified (by changing any
== in each file).
Onboarding guide for users of version 0.2 or earlier
Up to version 0.2, Fairlearn contained only the exponentiated gradient method.
The Fairlearn repository now has a more comprehensive scope and aims to
incorporate other methods. The same exponentiated gradient technique is now
fairlearn.reductions.ExponentiatedGradient. While in the past
exponentiated gradient was invoked via
import numpy as np from fairlearn.classred import expgrad from fairlearn.moments import DP estimator = LogisticRegression() # or any other estimator exponentiated_gradient_result = expgrad(X, sensitive_features, y, estimator, constraints=DP()) positive_probabilities = exponentiated_gradient_result.best_classifier(X) randomized_predictions = (positive_probabilities >= np.random.rand(len(positive_probabilities))) * 1
the equivalent operation is now
from fairlearn.reductions import ExponentiatedGradient, DemographicParity estimator = LogisticRegression() # or any other estimator exponentiated_gradient = ExponentiatedGradient(estimator, constraints=DemographicParity()) exponentiated_gradient.fit(X, y, sensitive_features=sensitive_features) randomized_predictions = exponentiated_gradient.predict(X)
Please open a new issue if you encounter any problems.
Investigating automated test failures¶
For every pull request to
main with automated tests, you can check
the logs of the tests to find the root cause of failures. Our tests currently
run through Azure Pipelines with steps for setup, testing, and teardown. The
Checks tab of a pull request contains a link to the
Azure Pipelines page),
where you can review the logs by clicking on a specific step in the automated
test sequence. If you encounter problems with this workflow, please reach out
through GitHub issues.
To run the same tests locally, find the corresponding pipeline definition (a
yml file) in the
devops directory. It either directly contains
the command to execute the tests (usually starting with
python -m pytest) or it refers to a template file with the command.
Building the website¶
The website is built using Sphinx and some of its extensions. Specifically, the website is available for all our releases to allow users to check the documentation of the version of the package that they are using.
To be able to build the documentation you need to install all the
pip install -r requirements-dev.txt.
When making changes to the documentation at least run the following command to build the website using your changes:
python -m sphinx -v -b html -n -j auto docs docs/_build/html
or use the shortcut
This will generate the website in the directory mentioned at the end of the command. Navigate to that directory and find the corresponding files where you made changes, open them in the browser and verify that your changes render properly and links are working as expected.
To fully build the website for all versions use the following script:
python scripts/build_documentation.py --documentation-path=docs --output-path=docs/_build/html
or the shortcut
The comprehensive set of commands to build the website is in our CircleCI configuration file in the .circleci directory of the repository.