GPyS 0.1.0

Gaussian Process Subspace Prediction

This is a prototypical implementation of GPS in the Python programming language. For the original research article documenting the method, see the Citation section.

Citation

• Ruda Zhang, Simon Mak, and David Dunson. Gaussian Process Subspace Prediction for Model Reduction. SIAM Journal on Scientific Computing, 2022. https://epubs.siam.org/doi/10.1137/21M1432739

Installation

Install the package via pip using the following command:

• pip install GPyS==0.0.31

Example Use

After installing the package you can load it via:

For GPS Preprocessor:

• from GPyS_preprocessor import Preprocessor
• Note that only Preprocessor.setup(X) takes in argument X and this must be called first before any other functions
• The remaining functions merely return preprocessing quantities of interests

For GPS Hyperparameter training:

• from GPyS_LOOCV_error import LOOCV
• Utilize hSSDist(length) method for the objective function computation at a given (default) length scale
• Please take a look at the LOOCV_script.py to see an example computation of optimal lengthscale for GPS.

For GPS Prediction:

• from GPyS_prediction import Prediction
• All the functions can be independently called here.
• Also, the user can directly call Prediction.GPS_Prediction() to immediately obtain prediction results