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Contributing

This page serves as the contribution guide for the ClusterValidityIndices.jl package. From top to bottom, the ways of contributing are:

  • GitHub Issues: how to raise an issue with the project.
  • Julia Development: how to download and interact with the package.
  • GitFlow: how to directly contribute code to the package in an organized way on GitHub.
  • Development Details: how the internals of the package are currently setup if you would like to directly contribute code.

Please also see the Attribution to learn about the authors and sources of support for the project.

Issues

The main point of contact is the GitHub issues page for the project. This is the easiest way to contribute to the project, as any issue you find or request you have will be addressed there by the authors of the package. Depending on the issue, the authors will collaborate with you, and after making changes they will link a pull request which addresses your concern or implements your proposed changes.

Julia Development

As a Julia package, development follows the usual procedure:

  1. Clone the project from GitHub
  2. Switch to or create the branch that you wish work on (see GitFlow).
  3. Start Julia at your development folder.
  4. Instantiate the package (i.e., download and install the package dependencies).

For example, you can get the package and startup Julia with

git clone git@github.com:AP6YC/ClusterValidityIndices.jl.git
julia --project=.
Note

In Julia, you must activate your project in the current REPL to point to the location/scope of installed packages. The above immediately activates the project when starting up Julia, but you may also separately startup the julia and activate the package with the interactive package manager via the ] syntax:

julia> ]
(@v1.9) pkg> activate .
(ClusterValidityIndices) pkg>

You may run the package's unit tests after the above setup in Julia with

julia> using Pkg
julia> Pkg.instantiate()
julia> Pkg.test()

or interactively though the Julia package manager with

julia> ]
(ClusterValidityIndices) pkg> instantiate
(ClusterValidityIndices) pkg> test

GitFlow

The ClusterValidityIndices.jl package follows the GitFlow git working model. The original post by Vincent Driessen outlines this methodology quite well, while Atlassian has a good tutorial as well. In summary:

  1. Create a feature branch off of the develop branch with the name feature/<my-feature-name>.
  2. Commit your changes and push to this feature branch.
  3. When you are satisfied with your changes, initiate a GitHub pull request (PR) to merge the feature branch with develop.
  4. If the unit tests pass, the feature branch will first be merged with develop and then be deleted.
  5. Releases will be periodically initiated from the develop branch and versioned onto the master branch.
  6. Immediate bug fixes circumvent this process through a hotfix branch off of master.

Development Details

Documentation

These docs are currently hosted as a static site on the GitHub pages platform. They are setup to be built and served in a separate branch gh-pages from the master/development branch of the project.

Package Structure

The ClusterValidityIndices.jl package has the following file structure:

ClusterValidityIndices.jl
├── .github/workflows       // GitHub: workflows for testing and documentation.
├── data                    // Data: CI and example data location.
├── docs                    // Docs: GitHub pages documentation files.
├── paper                   // Docs: JOSS paper and bib files.
├── src                     // Source: scripts and common helper functions.
│   └─── CVI                //      All CVI and ICVI definitions
├── test                    // Test: unit, integration, and environment tests.
├── .appveyor               // CI: Appveyor CI script
├── .gitignore              // Git: .gitignore for the whole project.
├── CODE_OF_CONDUCT         // Doc: the expectations of contributors to the project.
├── CONTRIBUTING            // Doc: a summary of contribution guidelines, pointing to these docs.
├── LICENSE                 // Doc: the license to the project.
├── Manifest.toml           // Julia: the explicit package versions used (ignored).
├── Project.toml            // Julia: the Pkg.jl dependencies of the project.
└── README.md               // Doc: the README.

All CVIs are implemented in separate files in the src/CVI/ directory, and they are imported to src/ClusterValidityIndices.jl through imports in src/CVI/CVI.jl.

Dependencies

Several dependencies directly affect the look and functionality of the project's codebase, so their special considerations are listed below.

NumericalTypeAliases.jl

The ClusterValidityIndices.jl package uses the NumericalTypeAliases.jl package for the definition of convenient aliases for numerical types, such as those restricting real-valued matrices and integer-valued vectors.

DocStringExtensions.jl

The ClusterValidityIndices.jl package makes heavy use of DocStringExtensions.jl for the documentation of its CVIs. This includes the definitions of docstring templates that depend on the type being documented.

Documentation for the project uses the following workflow:

  1. Each type and function requires a description, possible arguments list, and citations (where applicable).
  2. Signatures and methodlists autopopulate, so they should not be explicitly written at the risk of duplication.
  3. Types should have docstrings for every field, which should include boundary values where applicable.

CVI Module Workflow

To write a CVI for this project, it will require the following:

  1. A struct subtyped from CVI that implements the internal parameters in addition to a Float named criterion_value and a LabelMap.
  2. A default constructor that initializes values to zeros and arrays to empties (see existing CVI files such as DB.jl for examples).
  3. An incremental parameter update method param_inc!(cvi::NEW_CVI, sample::RealVector, label::Integer) where NEW_CVI is the name of the new CVI.
  4. A batch parameter update method param_batch!(cvi::NEW_CVI, data::RealMatrix, labels::IntegerVector).
  5. A criterion value evaluation method evaluate(cvi::NEW_CVI) that updates the internal criterion value.
  6. The top-level functions get_icvi and get_cvi will work automatically after writing the above definitions!

Attribution

Authors

This package is developed and maintained by Sasha Petrenko with sponsorship by the Applied Computational Intelligence Laboratory (ACIL). The users @rMassimiliano and @malmaud have graciously contributed their time with reviews and feedback that has greatly improved the project.

If you simply have suggestions for improvement, Sasha Petrenko (<sap625@mst.edu>) is the current developer and maintainer of the ClusterValidityIndices.jl package, so please feel free to reach out with thoughts and questions.

Support

This project is supported by grants from the Night Vision Electronic Sensors Directorate, the DARPA Lifelong Learning Machines (L2M) program, Teledyne Technologies, and the National Science Foundation. The material, findings, and conclusions here do not necessarily reflect the views of these entities.

Research was sponsored by the Army Research Laboratory and was accomplished under Cooperative Agreement Number W911NF-22-2-0209. The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the official policies, either expressed or implied, of the Army Research Laboratory or the U.S. Government. The U.S. Government is authorized to reproduce and distribute reprints for Government purposes notwithstanding any copyright notation herein.