Thank you for considering a contribution to Rubix ML. We believe that our contributors play the most important role in bringing powerful machine learning tools to the PHP language. Please read over the following guidelines so that we can continue to provide high quality machine learning tools that our users love.
Here are a few things to check off before sending in a pull request ...
- Your changes pass static analysis
- All unit tests pass
- Your changes are consistent with our coding style
- Do your changes require updates to the documentation?
- Does an entry to the CHANGELOG need to be added?
- Have you read and agreed to our CLA?
New to pull requests? Github has a great howto to get you started.
We use pull requests as an opportunity to communicate with our contributors. Oftentimes, we can improve code readability, find bugs, and make optimizations during the code review process. Every pull request must have the approval from at least one core engineer before merging into the main codebase.
To ensure that project maintainers have the legal rights to license and distribute your code contributions, we ask that every contributor sign our contributor license agreement (CLA). If you are a first-time contributor, you will automatically receive instructions on how to sign the agreement from our CLA bot with your first pull request.
Static code analysis is an integral part of our overall testing and quality assurance strategy. Static analysis allows us to catch bugs before they make it into the codebase. Therefore, it is important that your updates pass static analysis at the level set by the project lead.
To run static analysis:
$ composer analyzeNew code will usually require an accompanying unit test. What to test depends on the type of change you are making. See the individual unit testing guidelines below.
To run the unit tests:
$ composer testLimiting tests to public methods is usually sufficient. It is also important to test for edge cases such as mistakes that the user might make to ensure they are handled properly.
Bugs usually indicate an area of the code that has not been properly tested yet. When submitting a bug fix, please include a passing test that would have reproduced the bug prior to your changes.
We use a unique end-to-end testing schema for all learners that involves generating a controlled training and testing set, training the learner, and then validating its predictions using an industry-standard scoring metric. The reason for this type of test is to be able to confirm that the learner offers the ability to generalize its training to new data. Since not all learners are the same, choose a dataset and minimum validation score that is appropriate for a real world use case.
Note: Be sure to seed the random number generator with a known constant in your tests to make them deterministic.
Rubix ML follows the PSR-2 coding style with additional rules to keep the codebase clean and reduce cognitive load for our developers. A consistent codebase allows for quicker debugging and generally a more pleasant developer experience.
To run the style checker:
$ composer checkTo run the automatic style fixer:
$ composer fixUse accurate, descriptive, consistent, and concise nomenclature. A variable name should only describe the data that the variable contains. Prefer verbs for function and method names unless in the case of an accessor/getter function where the 'get' prefix may be dropped. Prioritize full names over abbreviations unless in the case where the abbreviation is the more common usage.
We employ the Domain Driven Design (DDD) methodology in our naming and design. The goal is to allow developers and domain experts to be able to use the same language when referring to concepts in our codebase. Therefore, it is crucial that your naming reflects the domain that your abstraction operates within.
Objects implemented in Rubix ML have a mutability policy of generally immutable which means properties are private or protected and state must be mutated only through a well-defined public API.
Please provide a docblock for every class, property, method, constant, and function that includes a brief description of what the thing does. Inline comments are strongly discouraged - instead use expressive syntax and abstractions to articulate your intent in code.
Due to a limitation in PHP that requires objects and functions to be named in order to be deserialized and since the library relies on serialization for persistence, we do not use anonymous classes or functions in our codebase. Instead, create a named class or function.
Performance can be critical for some machine learning projects. To ensure that our users have the best experience, we benchmark every learner and use the information as a baseline to make optimizations. When contributing a new learner or transformer please include a benchmark.
To run the benchmarking suite:
$ composer benchmarkWe use Mkdocs and Mike to compile the markdown documents in the docs folder to a versioned static document site.
Make sure to have the following Python dependencies installed.
$ pip install mike mkdocs mkdocs-material mkdocs-git-revision-date-pluginTo serve the documentation site locally for development you can run the following commands from the terminal. Then, you'll be able to view the docs by navigating to http://127.0.0.1:8000 in your browser.
$ mike deploy 'VERSION'
$ mike serve