Gaussian Process (GP) and Revised BBMM methods for GP

Installation

python setup.py install

Dependences

• cupy pip install cupy

How to use

Create data and kernel

import numpy as np
import BBMM
X = np.random.random((100, 10))
Y = np.sum(np.sin(X), axis=1)[:, None]
noise = 1e-4
k = BBMM.kern.RBF()
k.set_lengthscale(1.0)
k.set_variance(10.0)

Train a full GP model and then save

gp = BBMM.GP(X, Y, k, noise, GPU=False)
gp.optimize(messages=False)
# predict
Y_pred = gp.predict(X)
# save
gp.save("model.npz")

Train a BBMM model and then save

bbmm = BBMM.BBMM(k, nGPU=1)
bbmm.initialize(X, noise)
bbmm.set_preconditioner(50, nGPU=0)
bbmm.solve_iter(Y)
# predict
Y_pred = bbmm.predict(X)
# save
bbmm.save("model.npz")

The kernel calculations are cached by default. If you want to play with them by yourself you may want YOUR_KERNEL.clear_cache or disable the cacheing by YOUR_KERNEL.set_cache_state(False).

The usage of more advanced kernels is shown in the test examples.

References

1. Wang, Ke Alexander, Geoff Pleiss, Jacob R. Gardner, Stephen Tyree, Kilian Q. Weinberger, and Andrew Gordon Wilson. “Exact Gaussian processes on a million data points.” arXiv preprint arXiv:1903.08114 (2019). Accepted by NeurIPS 2019 [Link]
2. Gardner, J. R., Pleiss, G., Bindel, D., Weinberger, K. Q., & Wilson, A. G. (2018). Gpytorch: Blackbox matrix-matrix gaussian process inference with gpu acceleration. arXiv preprint arXiv:1809.11165. Accepted by NeurIPS 2018 [Link]
3. Sun, J., Cheng, L., & Miller III, T. F. (2021). Molecular Energy Learning Using Alternative Blackbox Matrix-Matrix Multiplication Algorithm for Exact Gaussian Process. arXiv preprint arXiv:2109.09817. [Link]
4. Sun, J., Cheng, L., & Miller III, T. F. (2022). Molecular Dipole Moment Learning via Rotationally Equivariant Gaussian Process Regression with Derivatives in Molecular-orbital-based Machine Learning. arXiv preprint arXiv:2205.15510. [Link]