MOGPTK: The Multi-Output Gaussian Process Toolkit
This toolkit addresses the need for accessible multi-output GP modeling for researchers, data scientists, and practitioners, but it is incremental as it packages existing methods into a user-friendly tool.
The authors introduced MOGPTK, a Python toolkit for modeling multi-channel data with Gaussian processes, making multi-output GP models accessible and enabling GPU-accelerated training through integration with GPflow and TensorFlow.
We present MOGPTK, a Python package for multi-channel data modelling using Gaussian processes (GP). The aim of this toolkit is to make multi-output GP (MOGP) models accessible to researchers, data scientists, and practitioners alike. MOGPTK uses a Python front-end, relies on the GPflow suite and is built on a TensorFlow back-end, thus enabling GPU-accelerated training. The toolkit facilitates implementing the entire pipeline of GP modelling, including data loading, parameter initialization, model learning, parameter interpretation, up to data imputation and extrapolation. MOGPTK implements the main multi-output covariance kernels from literature, as well as spectral-based parameter initialization strategies. The source code, tutorials and examples in the form of Jupyter notebooks, together with the API documentation, can be found at http://github.com/GAMES-UChile/mogptk