xGPR 0.4.8.5

Efficient approximate Bayesian machine learning

xGPR

xGPR is a library for fitting approximate Gaussian process regression models and approximate kernel classification models to datasets ranging in size from thousands to millions of datapoints. It can also be used for efficient approximate kernel k-means and approximate kernel PCA.

The docs provide a number of examples for how to use xGPR for fitting protein sequences, small molecule structures, and tabular data for regression (classification is also available in v0.4.8).

xGPR uses a fast Hadamard transform-based implementation of the random features approximation (aka random Fourier features). It is designed to run on either CPU or GPU (GPU is better for training, either is fine for inference), to model tabular data, sequence & time series data and graph data, and to fit datasets too large to load into memory in a straightforward way.

Unlike exact Gaussian processes, which exhibit O(N^2) scaling and are impractical for large datasets, xGPR can scale easily; it is straightforward to quickly fit a few million datapoints on a GPU. The approximation we use provides improved accuracy compared with variational or sparse GP approximations. Unlike other libraries for Gaussian processes, which only provide kernels for fixed-vector data, xGPR provides convolution kernels for variable-length time series, sequences and graphs.

What's new in v0.4.8

An approximate kernel classifier is now included. Unlike the xGPRegression object, this does not currently however compute marginal likelihood, so to tune hyperparameters for this you will have to evaluate performance on a validation set. We hope to implement approximate marginal likelihood calculations for this soon.

You can now build custom Datasets (similar to the Dataloader in PyTorch) so that you can use any kind of data (SQLite db, HDF5 etc.) as input when training with minor tweaks.

In most cases, installation should typically be as simple as:

pip install xGPR

See the documentation for important information about installation and requirements.

Documentation

The documentation covers a variety of use cases, including tabular data, sequences and graphs, installation requirements and much more.

Citations

If using xGPR for research intended for publication, please cite either:

Linear-Scaling Kernels for Protein Sequences and Small Molecules Outperform Deep Learning While Providing Uncertainty Quantitation and Improved Interpretability Jonathan Parkinson and Wei Wang Journal of Chemical Information and Modeling 2023 63 (15), 4589-4601 DOI: 10.1021/acs.jcim.3c00601

The preprint is available at:

Jonathan Parkinson, & Wei Wang. (2023). Linear Scaling Kernels for Protein Sequences and Small Molecules Outperform Deep Learning while Providing Uncertainty Quantitation and Improved Interpretability https://arxiv.org/abs/2302.03294