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CONTRIBUTING.md

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Contributing

Please follow these guidelines if you wish to contribute to Spektral.

Bugs

If you found a bug in the latest version of Spektral, you can open an issue to report it.

Before opening the issue, make sure to follow these steps:

  1. Update to the current master branch and see if the problem is already solved. Sometimes a change does not get released immediately on PyPi, so it might be a good idea to install from source.
  2. Check old issues to see if the problem was already solved.
  3. Make sure that your configuration checks all requirements, including:
    • Operating system
    • Python version
    • Version of Spektral
    • Version of Tensorflow and Keras (note that since version 0.3 Spektral only supports tf.keras)
    • Version of CUDA and cuDNN
  4. Provide a minimal script to reproduce the issue. The script should be runnable as-is, with no modification or external data.
  5. Include any stack trace/errors that you get.

If you want to try to fix the bug yourself, feel free to open a pull request.

Bug fixes should be added to the master branch.

Feature requests

If you want to request a feature, open an issue on GitHub and clearly mark it as a feature request.

  1. Give a detailed description of the feature, including why it is important and why it fits in the scope of the project. Spektral is primarily a library for creating graph neural networks, so new features should gravitate around this subject.
  2. Provide an example of the use case that you have in mind for your feature.

The quickest way to see your feature implemented is to code it yourself and then open a pull request.

Contributing a feature

New features should be added to the develop branch. Contribution to the documentation go the master branch, instead.

There are no hard rules for contributing to Spektral, but you should try to follow these guidelines.

General guidelines:

  • Format your code according to PEP8;
  • Make sure that the code you contribute is clearly identifiable in a PR (e.g., watch out for your IDE automatically reformatting the whole project);
  • New features should support:
    • Python >= 3.5
    • TensorFlow >= 2.1.0
    • Linux (at least Ubuntu >= 16.04) and MacOS >= 10.14
  • Write tests for the new feature and then run: 
    cd spektral
pytest tests/
  • Write docstrings for the new feature (copy the format of existing docstrings);

Guidelines for adding new layers:

  • Message-passing/convolutional layers go in their own file in layers/convolutional/;
  • Pooling layers go in their own file in layers/pooling/;
  • Global pooling layers go in layers/pooling/globalpool.py;
  • Layers should extend tensorflow.keras.layers.Layer and implement the following methods:
    • build()
    • call()
    • compute_output_shape()
    • get_config()
  • Convolutional layers should also implement a preprocess(A) staticmethod to manipulate/normalize the adjacency matrix before giving it as input to the GNN (e.g., the preoprocess() method of GCN adds self-loops and then re-scales each row by the degree);
  • Make sure that you understand data modes and that you know the modes supported by your layer. Layers should support at least single or batch mode;
  • Many layers in Spektral inherit their base functionality from GraphConv, check if this is the case for yours as well;
  • There is also a MessagePassing layer that offers a quick API to implement convolutional layers in single/disjoint mode;

Guidelines for testing:

  • Tests are found in tests/;
  • It's especially important to add tests for any new layer. See tests/test_layers/ and the files contained there;
  • See the comments in each test .py for more information;

Guidelines for the documentation:

  • See the documentation in the other layers for how to format docstrings (it's important that the format is the same so that the docs can be built automatically);
  • Docs are automatically generated using docs/autogen.py. Make sure to include any new Layer as an entry in the PAGES dictionary. It is not necessary to add utils and other minor functions to autogen.py (although you should still write the docstrings).