testbed/fairlearn__fairlearn/CONTRIBUTING.md

# Contributing to fairlearn

This project welcomes contributions and suggestions.

## Developer certificate of origin

Contributions require you to sign a _developer certificate of origin_ (DCO)
declaring that you have the right to, and actually do, grant us the rights to
use your contribution. For details, visit https://developercertificate.org/.

When you submit a pull request, a DCO-bot will automatically determine whether
you need to provide a DCO and decorate the PR appropriately (e.g., label,
comment).

Signing off means you need to have your name and email address attached as a
commit comment, which you can automate using git hooks as shown
[here](https://stackoverflow.com/questions/15015894/git-add-signed-off-by-line-using-format-signoff-not-working/46536244#46536244).

## Code of conduct

This project has adopted the [GitHub community
guidelines](https://help.github.com/en/github/site-policy/github-community-guidelines).

## Development process

Development happens against the `master` branch following the [GitHub flow
model](https://guides.github.com/introduction/flow/). Contributors create
feature branches off of `master`, and their pull requests should target the
`master` branch. Maintainers are responsible for prompt review of pull
requests.

Pull requests against `master` trigger automated tests that are run through
Azure DevOps. 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 `test` directory.

To build the fairlearn dashboard after making changes to it, [install
Yarn](https://yarnpkg.com/lang/en/docs/install), and then run the [widget build
script](scripts/build_widget.py). To validate that you did not make changes
that affect the generated files simply add the `--assert-no-changes` option.

### Investigating automated test failures

For every pull request to `master` 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](dev.azure.com/responsibleai/fairlearn/_build/results), 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](https://github.com/fairlearn/fairlearn/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.

## API conventions

This section relies on the definitions from our [terminology
guide](TERMINOLOGY.md), including the definitions of "estimator", "reduction",
"sensitive features", "moment", and "parity".

### Unfairness mitigation algorithms

Unfairness mitigation algorithms take form of scikit-learn-style estimators.
Any algorithm-specific parameters are passed to the constructor. The resulting
instance of the algorithm should support methods `fit` and `predict` with APIs
resembling those of scikit-learn as much as possible. Any deviations are noted
below.

#### Reductions

Reduction constructors require a parameter corresponding to an estimator that
implements the `fit` method with the `sample_weight` argument. Parity
constraints for reductions are expressed via instances of various subclasses of
the class `fairlearn.reductions.Moment`. Formally, instances of the class
`Moment` implement vector-valued random variables whose sample averages over
the data are required to be bounded (above and/or below).

```python
constraints = Moment()
reduction = Reduction(estimator, constraints)
```

Reductions provide `fit` and `predict` methods with the following signatures:

```python
reduction.fit(X, y, **kwargs)
reduction.predict(X)
```

All of the currently supported parity constraints (subclasses of `Moment`) are
based on sensitive features that need to be provided to `fit` as a keyword
argument `sensitive_features`. In the future, it will also be possible to
provide sensitive features as columns of `X`.

#### Post-processing algorithms

The constructors of post-processing algorithms require either an already
trained predictor or an estimator (which is trained on the data when executing
`fit`). For post-processing algorithms, the `constraints` argument is provided
as a string.

```python
postprocessor = PostProcessing(unconstrained_predictor=predictor, constraints=constraints)
postprocessor = PostProcessing(estimator=estimator, constraints=constraints)
```

Post-processing algorithms (such as the ones under `fairlearn.postprocessing`)
provide the same functions as the reductions above albeit with
`sensitive_features` as a required argument for `predict`. In the future, we
will make `sensitive_features` optional if the sensitive features are already
provided through `X`.

```python
postprocessor.fit(X, y, sensitive_features=sensitive_features)
postprocessor.predict(X, sensitive_features=sensitive_features)
```

## Creating new releases

First add a description of the changes introduced in the package version you
want to release to [CHANGES.md](CHANGES.md).

We have a [Azure DevOps
Pipeline](https://dev.azure.com/responsibleai/fairlearn/_build?definitionId=48&_a=summary)
which takes care of building wheels and pushing to PyPI. Validations are also
performed prior to any deployments, and also following the uploads to Test-PyPI
and PyPI. To use it:
1. Ensure that `_base_version` in `fairlearn/__init__.py` is set correctly for
   PyPI.
1. Put down a tag corresponding to this `_base_version` but preprended with
   `v`. For example, version `0.5.0` should be tagged wtih `v0.5.0`
1. Queue the pipeline at this tag, with a variable `DEV_VERSION` set to zero.
   When the package is uploaded to Test-PyPI, this number will be appended to
   the version as a `dev[n]` suffix

The pipeline requires sign offs immediately prior to the deployments to
Test-PyPI and PyPI. If there is an issue found, then after applying the fix,
update the location of the tag, and queue a new release pipeline with the value
of `DEV_VERSION` increased by one.

The `DEV_VERSION` variable is to work around the PyPI behaviour where uploads
are immutable and published immediately. This means that each upload to PyPI
'uses up' that particular version (on that particular PyPI instance). Since we
wish to deploy exactly the same bits to both Test-PyPI and PyPI, without this
workaround the version released to PyPI would depend on the number of issues
discovered when uploading to Test-PyPI. If PyPI updates their release process
to separate the 'upload' and 'publish' steps (i.e. until a package is
published, it remains hidden and mutable) then the code associated with
`DEV_VERSION` should be removed.

As part of the release process, the `build_wheels.py` script uses
`process_readme.py` to turn all the relative links in the ReadMe file into
absolute ones (this is the reason why the applied tag has be of the form
`v[_base_version]`). The `process_readme.py` script is slightly fragile with
respect to the contents of the ReadMe, so after significant changes its output
should be verified.