I was working on the implementation of Swift wrapper for XGBoost for some time. The goal of the wrapper was to provide convenient usage for those who are familiar with Python, but also take advantage of great compatibility of Swift with C to provide even more powerful features in the near future.
Currently, all core stuff required to train, test, and use your model should be available. In addition, support for Python Numpy and Tensorflow Tensor allows the use of well-known libraries along with it.
If you would like to try this out, code with documentation is hosted at https://github.com/kongzii/SwiftXGBoost.
If you find anything is missing or not working properly, please fill an issue. Functionality is currently tested against expected behaviour or directly against Python implementation.
Thanks for reading this and have a nice day!