parallel-gps

Companion code leveraging GPflow for the paper Temporal Gaussian Process Regression in Logarithmic Time.

Please cite the following paper (arXiv preprint) to use the code

@inproceedings{corenflos2022temporal,
  title={Temporal {G}aussian Process Regression in Logarithmic Time},
  author={Corenflos, Adrien and Zhao, Zheng and S{\"a}rkk{\"a}, Simo},
  booktitle={2022 25th International Conference on Information Fusion (FUSION)},
  pages={1--5},
  year={2022},
  organization={IEEE}
}

What is it?

This is an implementation of temporally parallelized and sequential state space Gaussian processes with CPU and GPU support leveraging GPflow as a framework and TensorFlow as a calculation backend.

Supported covariance functions

• Matern 12, 32, 52
• RBF
• Quasi-periodic
• Sum and product of the above

Installation

Clone or download the project Run python setup.py [install|develop] depending on the level of installation you want. Note that in order to use the GPU capabilities you will need to install a tensorflow compatible CUDA version. Note that the requirements.txt file is a superset of what is actually necessary to use the library and also contains packages required for unittesting only.

Example

from pssgp.kernels import RBF
from pssgp.model import StateSpaceGP
from gpflow.model import GPR

data = ...  # Same format as for GPFlow
noise_covariance = 1.
lengthscale = 1.
variance = 0.1

order = 6  # Order of the RBF approximation for (P)SSGP, will not be used if the GP model is GPR
balancing_iter = 5  # Number of balancing steps for the resulting SDE to make it more stable, will not be used if the GP model is GPR

cov_function = RBF(variance=variance, lengthscales=lengthscale, order=order, balancing_iter=balancing_iter)

gp = GPR(data=data, kernel=cov, noise_variance=noise_variance)
ssgp = StateSpaceGP(data=data, kernel=cov, noise_variance=noise_variance, parallel=False)

pssgp = StateSpaceGP(data=data, kernel=cov, noise_variance=noise_variance, parallel=True, max_parallel=1000)  
# max_parallel should be bigger than n_training + n_pred

for model in [gp, ssgp, pssgp]:
    print(model.maximum_log_likelihood_objective())

For more examples, see the notebooks or the runnable scripts in the experiments folder which reproduces the results of our paper.