Vanguard: Advanced GPs

Vanguard is a high-level wrapper around GPyTorch and aims to provide a user-friendly interface for training and using Gaussian process models. Vanguard's main objective is to make a variety of more advanced GP techniques in the machine learning literature available for easy use by a non-specialists and specialists alike. Vanguard is designed for modularity to facilitate straightforward combinations of different techniques.

Vanguard implements many advanced Gaussian process techniques, as showcased in our examples folder. These techniques and others implemented within the Vanguard paradigm can be combined straightforwardly with minimal extra code, and without requiring specialist GP knowledge.

Installation

To install Vanguard:

$ pip install vanguard-gp

Note that it is vanguard-gp and not vanguard. However, to import the package, use from vanguard import ....

If the code is not running properly, recreate the environment with pip install -r requirements.txt --no-deps.

Documentation

Vanguard's documentation can be found online at [TODO: LINK GOES HERE].

Alternatively, you can build the documentation from source - instructions for doing so can be found in CONTRIBUTING.md.

Tests

Vanguard's tests are contained in the tests/ directory, and can be run with pytest. The tests are arranged as follows:

• tests/units contains unit tests. These should be fairly quick to run.
• tests/integration contains integration tests, which may take longer to run.
• tests/test_doctests.py finds and runs all doctests. This should be fairly quick to run.
• tests/test_examples.py runs all notebooks under examples/ as tests. These require nbconvert and nbformat to run, and can take a significant amount of time to complete, so consider excluding test_examples.py from your test discovery.

$ pytest # run all tests (slow)
$ pytest tests/units # run unit tests
$ pytest tests/integration # run integration tests (slow)
$ pytest tests/test_doctests.py # run doctests
$ pytest tests/test_examples.py # run example tests (slow)

Our PR workflows run our tests with the pytest-beartype plugin. This is a runtime type checker that ensures all our type hints are correct. In order to run with these checks locally, add --beartype-packages="vanguard" -m "not no_beartype" to your pytest invocation. You should then separately run pytest with -m no_beartype to ensure that all tests are run. The reason for this separation is that some of our tests check that our handling of inputs of invalid type are correct, but beartype catches these errors before we get a chance to look at them, causing the tests to fail; thus, these tests need to be run separately without beartype.

Since different Python versions have different versions of standard library and third-party modules, we can't guarantee that type hints are 100% correct on all Python versions. Type hints are only tested for correctness on the latest version of Python (3.12).

For example, to run the unit tests with type checking:

$ pytest tests/units --beartype-packages="vanguard" -m "not no_beartype"  # run unit tests with type checking
$ pytest tests/units -m no_beartype  # run unit tests that are incompatible with beartype