Contributing

We welcome contributions from the community. Here you will find information to start contributing to hyperbox-brain.

The project is hosted on https://github.com/UTS-CASLab/hyperbox-brain

Our community, our values

We are a community based on openness and friendly, politeness, constructive discussions.

We aspire to treat everybody equally, and value their contributions. We are particularly seeking people from underrepresented backgrounds in Open Source Software and Hyperbox-based Machine Learning in particular to join and contribute their expertise and experience.

Decisions are made based on technical merit and consensus.

Code is not the only way to help the project. Reviewing pull requests, answering questions to help others on mailing lists or issues, organising and running tutorials, working on the website, enhancing the quality of documentation, are all priceless contributions.

We abide by the principles of openness, respect, and consideration of others of the Python Software Foundation: https://www.python.org/psf/codeofconduct/

In case you experience issues using this package, do not hesitate to submit a ticket to the GitHub issue tracker. You are also welcome to post feature requests or pull requests.

Ways to contribute

There are various methods to contribute to hyperbox-brain, with the most common ones being contribution of code or documentation to the project. Enhancing the documentation is no less important than enhancing the library itself. If you find a typo in the documentation, or have made improvements, do not hesitate to send an email to the mailing list or preferably submit a GitHub pull request. Full documentation can be found under the doc/ directory.

But there are many other ways to help. In particular helping to improve, triage, and investigate issues and reviewing other developers’ pull requests are very valuable contributions that decrease the burden on the project maintainers.

Another way to contribute is to report issues you’re facing, and give a “thumbs up” on issues that others reported and that are relevant to you. It also helps us if you spread the word: reference the project from your blog and articles, link to it from your website, or simply star to say “I use it”:

In case a contribution/issue involves changes to the API principles or changes to dependencies or supported versions, it must be essential to submit as a pull-request and send an email to inform the project owner.

Submitting a bug report or a feature request

We use GitHub issues to track all bugs and feature requests; feel free to open an issue if you have found a bug or wish to see a feature implemented.

In case you experience issues using this package, do not hesitate to submit a ticket to the Bug Tracker. You are also welcome to post feature requests or pull requests.

It is recommended to check that your issue complies with the following rules before submitting:

Verify that your issue is not being currently addressed by other issues or pull requests.
If you are submitting an algorithm or feature request, please verify the algorithm carefully and discuss it with the governance board.
If you are submitting a bug report, we strongly encourage you to follow the guidelines in How to make a good bug report.

How to make a good bug report

When you submit an issue to Github, please do your best to follow these guidelines! This will make it a lot easier to provide you with good feedback:

The ideal bug report contains a description of how to reproduce this bug via code snippet. By doing this way, anyone can try to reproduce the bug easily (see this for more details). If your snippet is longer than around 50 lines, please link to a gist or a github repo.
If it is not feasible to include a reproducible snippet, please be specific about what estimators and/or functions are involved and the shape of the data.
If an exception is raised, please provide the full traceback.
Please include your operating system type and version number, as well as your Python, hyperbox-brain, hyperbox-brain, joblib, numpy, matplotlib, plotly, and pandas versions. This information can be found by running the following code snippet:

>>> import hbbrain
>>> hbbrain.show_versions()

Please ensure all code snippets and error messages are formatted in appropriate code blocks. See Creating and highlighting code blocks for more details.

Contributing code

Note

To avoid duplicating work, it is highly recommended that you search through the issue tracker and the PR list. If in doubt about duplicated work, or if you want to work on a non-trivial feature, it’s recommended to first open an issue in the issue tracker to get some feedbacks from core developers.

One easy way to find an issue to work on is by applying the “help wanted” label in your search. This lists all the issues that have been unclaimed so far. In order to claim an issue for yourself, please comment exactly /take on it to assign the issue to you.

How to contribute

The best method to contribute to hyperbox-brain is to fork the main repository on GitHub, then submit a “pull request” (PR).

In the first few steps, we explain how to locally install hyperbox-brain, and how to set up your git repository:

Create an account on GitHub if you do not already have one.
Fork the project repository: click on the ‘Fork’ button near the top of the page. This creates a copy of the code under your account on the GitHub user account. For more details on how to fork a repository see this guide.

Clone your fork of the hyperbox-brain repo from your GitHub account to your local disk:

git clone git@github.com:YourLogin/hyperbox-brain.git  # add --depth 1 if your connection is slow
cd hyperbox-brain

Follow the steps in the installation from source to build hyperbox-brain in development mode and return to this document.

Install the development dependencies:

pip install pytest pytest-cov flake8 mypy numpydoc black==22.3.0

Add the upstream remote. This saves a reference to the main hyperbox-brain repository, which you can use to keep your repository synchronized with the latest changes:
```
git remote add upstream git@github.com:UTS-CASLab/hyperbox-brain.git
```
Check that the upstream and origin remote aliases are configured correctly by running git remote -v which should display:
```
origin  git@github.com:YourLogin/hyperbox-brain.git (fetch)
origin  git@github.com:YourLogin/hyperbox-brain.git (push)
upstream    git@github.com:UTS-CASLab/hyperbox-brain.git (fetch)
upstream    git@github.com:UTS-CASLab/hyperbox-brain.git (push)
```
You should now have a working installation of hyperbox-brain, and your git repository properly configured. The next steps now describe the process of modifying code and submitting a PR.
Synchronize your main branch with the upstream/main branch, more details on GitHub Docs:
```
git checkout main
git fetch upstream
git merge upstream/main
```
Create a feature branch to hold your development changes:
```
git checkout -b my_feature
```
and start making changes. Always use a feature branch. It’s good practice to never work on the main branch!
(Optional) Install pre-commit to run code style checks before each commit:
```
pip install pre-commit
pre-commit install
```
pre-commit checks can be disabled for a particular commit with git commit -n.
Develop the feature on your feature branch on your computer, using Git to do the version control. When you’re done editing, add changed files using git add and then git commit:
```
git add modified_files
git commit
```
to record your changes in Git, then push the changes to your GitHub account with:
```
git push -u origin my_feature
```
Follow these instructions to create a pull request from your fork. This will send an email to the committers. You may want to consider sending an email to the mailing list for more visibility.

It is often helpful to keep your local feature branch synchronized with the latest changes of the main hyperbox-brain repository:
```
git fetch upstream
git merge upstream/main
```
Subsequently, you might need to solve the conflicts. You can refer to the Git documentation related to resolving merge conflict using the command line.

Pull request checklist

Before a PR can be merged, it needs to be approved by two core developers. Please prefix the title of your pull request with [MRG] if the contribution is complete and should be subjected to a detailed review. An incomplete contribution – where you expect to do more work before receiving a full review – should be prefixed [WIP] (to indicate a work in progress) and changed to [MRG] when it matures. WIPs may be useful to: indicate you are working on something to avoid duplicated work, request broad review of functionality or API, or seek collaborators. WIPs often benefit from the inclusion of a task list in the PR description.

In order to ease the reviewing process, we recommend that your contribution complies with the following rules before marking a PR as [MRG]. The bolded ones are especially important:

Give your pull request a helpful title that summarizes what your contribution does. This title will often become the commit message once merged so it should summarize your contribution for posterity. In some cases “Fix <ISSUE TITLE>” is enough. “Fix #<ISSUE NUMBER>” is never a good title.
Make sure your code passes the tests. The whole test suite can be run with pytest, but it is usually not recommended since it takes a long time. It is often enough to only run the test related to your changes: for example, if you changed something in hbbrain/mixed_data/eiol_gfmm.py, running the following commands will usually be enough:
- pytest hbbrain/mixed_data/eiol_gfmm.py to make sure the doctest examples are correct.
- pytest hbbrain/mixed_data/tests/test_eiol_gfmm.py to run the tests specific to the file.
- pytest hbbrain/mixed_data to test the whole mixed_data module
- pytest docs/api/mixed_data.rst and pytest docs/tutorials/mixed_data_learner.rst to make sure the user guide examples are correct.
For guidelines on how to use pytest efficiently, see the document.
Make sure your code is properly commented and documented, and make sure the documentation renders properly. To build the documentation, please refer to our Documentation guidelines.
Tests are necessary for enhancements to be accepted. Bug-fixes or new features should be provided with non-regression tests. These tests verify the correct behavior of the fix or feature. In this manner, further modifications on the code base are granted to be consistent with the desired behavior. In the case of bug fixes, at the time of the PR, the non-regression tests should fail for the code base in the main branch and pass for the PR code.
Run black to auto-format your code.
```
black .
```
See black’s editor integration documentation to configure your editor to run black.
Make sure that your PR does not add PEP8 violations. To check the code that you changed, you can run the following command:
```
git diff upstream/main -u -- "*.py" | flake8 --diff
```
or make flake8-diff which should work on unix-like system.
Follow the Coding guidelines.
When applicable, use the validation tools and scripts in the hbbrain.utils submodule. You can add any functions to this submodule if necessary for your implementation.
Often pull requests resolve one or more other issues (or pull requests). If merging your pull request means that some other issues/PRs should be closed, you should use keywords to create link to them (e.g., Fixes #1234; multiple issues/PRs are allowed as long as each one is preceded by a keyword). Upon merging, those issues/PRs will automatically be closed by GitHub. If your pull request is simply related to some other issues/PRs, create a link to them without using the keywords (e.g., See also #1234).
PRs should often substantiate the change, through benchmarks of performance and efficiency or through examples of usage. Examples also illustrate the features and intricacies of the library to users. Have a look at other examples in the examples directory for reference. Examples should demonstrate why the new functionality is useful in practice and, if possible, compare it to other methods available in hyperbox-brain.
New features have some maintenance overhead. We expect PR authors to take part in the maintenance for the code they submit, at least initially. New features need to be illustrated with narrative documentation in the user guide, with small code snippets. If relevant, please also add references in the literature, with PDF links when possible.
The user guide should also include expected time and space complexity of the algorithm and scalability, e.g. “this algorithm can scale to a large number of samples > 1000000, but does not scale in dimensionality: n_features is expected to be lower than 100”.

You can check for common programming errors with the following tools:

Code with a good unittest coverage (at least 80%, better 100%), check with:

pip install pytest pytest-cov
pytest --cov hbbrain path/to/tests_for_package

Run static analysis with mypy:
```
mypy hbbrain
```
must not produce new errors in your pull request. Using # type: ignore annotation can be a workaround for a few cases that are not supported by mypy, in particular, when importing C or Cython modules on properties with decorators.

Coding guidelines

The following are some guidelines on how new code should be written for inclusion in hyperbox-brain, and which may be appropriate to adopt in external projects. Certainly, there are special cases and there will be exceptions to these rules. However, following these rules when submitting new code makes the review easier so new code can be integrated in less time.

Uniformly formatted code makes it easier to share code ownership. The hyperbox-brain project tries to closely follow the official Python guidelines detailed in PEP8 that detail how code should be formatted and indented. Please read it and follow it.

In addition, we add the following guidelines:

Use underscores to separate words in non class names: n_samples rather than nsamples.
Avoid multiple statements on one line. Prefer a line return after a control flow statement (if/for).
Unit tests should use absolute imports, exactly as client code would.
Please don’t use import * in any case. It is considered harmful by the official Python recommendations. It makes the code harder to read as the origin of symbols is no longer explicitly referenced, but most important, it prevents using a static analysis tool like pyflakes to automatically find bugs in hyperbox-brain.
Use the numpy docstring standard in all your docstrings.

A good example of code that we like can be found here.

Documentation

We are happy to accept any sort of documentation: function docstrings, reStructuredText documents (like this one), tutorials, etc. reStructuredText documents live in the source code repository under the docs/ directory.

You can edit the documentation using any text editor, and then generate the HTML output by typing make from the docs/ directory. Alternatively, make html may be used to generate the documentation with the example gallery (which takes quite some time). The resulting HTML files will be placed in _build/html and are viewable in a web browser.

Building the documentation

First, make sure you have properly installed the development version.

Building the documentation requires installing some additional packages:

pip install sphinx sphinx-rtd-theme readthedocs-sphinx-search numpydoc \
            sphinx-gallery hyperbox-brain nbsphinx sphinx-autodocgen \
            pandas IPython

To build the documentation, you need to be in the docs folder:

cd docs

In the vast majority of cases, you only need to generate the full web site, without the example gallery:

make

The documentation will be generated in the _build/html directory. To also generate the example gallery you can use:

make html

This will run all the examples, which takes a while. If you only want to generate a few examples, you can use:

EXAMPLES_PATTERN=your_regex_goes_here make html

This is particularly useful if you are modifying a few examples.

Set the environment variable NO_MATHJAX=1 if you intend to view the documentation in an offline setting.

To build the PDF manual, run:

make latexpdf

Warning

Sphinx version

While we do our best to have the documentation build under as many versions of Sphinx as possible, the different versions tend to behave slightly differently.

Guidelines for writing documentation

It is essential to keep a good compromise between mathematical and algorithmic details, and give intuition to the reader on what the algorithm does.

Basically, to elaborate on the above, it is best to always start with a small paragraph with a hand-waving explanation of what the method does to the data. Then, it is very helpful to point out why the feature is useful and when it should be used - the latter also including “big O” (\(O\left(g\left(n\right)\right)\)) complexities of the algorithm, as opposed to just rules of thumb, as the latter can be very machine-dependent. If those complexities are not available, then rules of thumb may be provided instead.

Secondly, a generated figure from an example should then be included to further provide some intuition.

Next, one or two small code examples to show its use can be added.

Next, any math and equations, followed by references, can be added to further the documentation. Not starting the documentation with the maths makes it more friendly towards users that are just interested in what the feature will do, as opposed to how it works “under the hood”.

Finally, follow the formatting rules below to make it consistently good:

Add “See Also” in docstrings for related classes/functions.

“See Also” in docstrings should be one line per reference, with a colon and an explanation, for example:

See Also
--------
SelectKBest : Select features based on the k highest scores.
SelectNSamples : Select samples based on a false negative rate test.

When documenting the parameters and attributes, here is a list of some well-formatted examples:
```
n_hyperboxes : int, default=10
    The number of hyperboxes generated by the algorithm.

some_param : {'hello', 'goodbye'}, bool or int, default=True
    The parameter description goes here, which can be either a string
    literal (either `hello` or `goodbye`), a bool, or an int. The default
    value is True.

array_parameter : {array-like, sparse matrix} of shape (n_samples, n_features) or (n_samples,)
    This parameter accepts data in either of the mentioned forms, with one
    of the mentioned shapes. The default value is
    `np.ones(shape=(n_samples,))`.

list_param : list of int

typed_ndarray : ndarray of shape (n_samples,), dtype=np.int32

sample_weight : array-like of shape (n_samples,), default=None

multioutput_array : ndarray of shape (n_samples, n_classes) or list of such arrays
```
In general have the following in mind:
1. Use Python basic types.
2. Use parenthesis for defining shapes: array-like of shape (n_samples,) or array-like of shape (n_samples, n_features)
3. For strings with multiple options, use brackets: input: {'log', 'squared', 'multinomial'}
4. 1D or 2D data can be a subset of {array-like, ndarray, sparse matrix, dataframe}. Note that array-like can also be a list, while ndarray is explicitly only a numpy.ndarray.
5. Specify dataframe when “frame-like” features are being used, such as the column names.
6. When specifying the data type of a list, use of as a delimiter: list of int. When the parameter supports arrays giving details about the shape and/or data type and a list of such arrays, you can use one of array-like of shape (n_samples,) or list of such arrays.
7. When specifying the dtype of an ndarray, use e.g. dtype=np.int32 after defining the shape: ndarray of shape (n_samples,), dtype=np.int32. You can specify multiple dtype as a set: array-like of shape (n_samples,), dtype={np.float64, np.float32}. If one wants to mention arbitrary precision, use integral and floating rather than the Python dtype int and float. When both int and floating are supported, there is no need to specify the dtype.
8. When the default is None, None only needs to be specified at the end with default=None. Be sure to include in the docstring, what it means for the parameter or attribute to be None.
For unwritten formatting rules, try to follow existing good works:
- When bibliographic references are available with arxiv or Digital Object Identifier identification numbers, use the sphinx directives :arxiv: or :doi:.
- For “References” in docstrings, see this document.
When editing reStructuredText (.rst) files, try to keep line length under 80 characters when possible (exceptions include links and tables).
Do not modify sphinx labels as this would break existing cross references and external links pointing to specific sections in the hyperbox-brain documentation.
Before submitting your pull request check if your modifications have introduced new sphinx warnings and try to fix them.

Issue Tracker Tags

All issues and pull requests on the GitHub issue tracker should have (at least) one of the following tags:

Bug / Crash:: Something is happening that clearly shouldn’t happen. Wrong results as well as unexpected errors from estimators go here.
Cleanup / Enhancement:: Improving performance, usability, consistency.
Documentation:: Missing, incorrect or sub-standard documentations and examples.
New Feature:: Feature requests and pull requests implementing a new feature.

There are four other tags to help new contributors:

good first issue:: This issue is ideal for a first contribution to hyperbox-brain. Ask for help if the formulation is unclear. If you have already contributed to hyperbox-brain, look at Easy issues instead.
Easy:: This issue can be tackled without much prior experience.
Moderate:: Might need some knowledge of machine learning or the package, but is still approachable for someone new to the project.
help wanted:: This tag marks an issue which currently lacks a contributor or a PR that needs another contributor to take over the work. These issues can range in difficulty, and may not be approachable for new contributors. Note that not all issues which need contributors will have this tag.

Code Review Guidelines

Reviewing code contributed to the project as PRs is a crucial component of hyperbox-brain development. We encourage anyone to start reviewing code of other developers. The code review process is often highly educational for everybody involved. This is particularly appropriate if it is a feature you would like to use, and so can respond critically about whether the PR meets your needs. While each pull request needs to be signed off by two core developers, you can speed up this process by providing your feedback.

Note

The difference between an objective improvement and a subjective one isn’t always clear. Reviewers should recall that code review is primarily about reducing risk in the project. When reviewing code, one should aim at preventing situations which may require a bug fix, a deprecation, or a retraction. Regarding docs: typos, grammar issues and disambiguations are better addressed immediately.

Here are a few important aspects that need to be covered in any code review, from high-level questions to a more detailed check-list.

Do we want this in the library? Is it likely to be used? Do you, as a hyperbox-brain user, like the change and intend to use it? Is it in the scope of hyperbox-brain? Will the cost of maintaining a new feature be worth its benefits?
Is the code consistent with the API of hyperbox-brain? Are public functions/classes/parameters well named and intuitively designed?
Are all public functions/classes and their parameters, return types, and stored attributes named according to hyperbox-brain conventions and documented clearly?
Is any new functionality described in the user-guide and illustrated with examples?
Is every public function/class tested? Are a reasonable set of parameters, their values, value types, and combinations tested? Do the tests validate that the code is correct, i.e. doing what the documentation says it does? If the change is a bug-fix, is a non-regression test included? Look at this document to get started with testing in Python.
Do the tests pass in the continuous integration build? If appropriate, help the contributor understand why tests failed.
Do the tests cover every line of code (see the coverage report in the build log)? If not, are the lines missing coverage good exceptions?
Is the code easy to read and low on redundancy? Should variable names be improved for clarity or consistency? Should comments be added? Should comments be removed as unhelpful or extraneous?
Could the code easily be rewritten to run much more efficiently for relevant settings?
Is the code backwards compatible with previous versions? (or is a deprecation cycle necessary?)
Will the new code add any dependencies on other libraries? (this is unlikely to be accepted)
Does the documentation render properly (see the Documentation section for more details), and are the plots instructive?

Communication Guidelines

Reviewing open pull requests (PRs) helps move the project forward. It is a great way to get familiar with the codebase and should motivate the contributor to keep involved in the project. [1]

Every PR, good or bad, is an act of generosity. Opening with a positive comment will help the author feel rewarded, and your subsequent remarks may be heard more clearly. You may feel good also.
Begin if possible with the large issues, so the author knows they’ve been understood. Resist the temptation to immediately go line by line, or to open with small pervasive issues.
Do not let perfect be the enemy of the good. If you find yourself making many small suggestions that don’t fall into the Code Review Guidelines, consider the following approaches:
- refrain from submitting these;
- prefix them as “Nit” so that the contributor knows it’s OK not to address;
- follow up in a subsequent PR, out of courtesy, you may want to let the original contributor know.
Do not rush, take the time to make your comments clear and justify your suggestions.
You are the face of the project. Bad days occur to everyone, in that occasion you deserve a break: try to take your time and stay offline.

Important

This guide line is adapted from scikit-learn guidelines under the MIT licence.