Package: dgpsi
dgpsiFor R usersTheRinterface to the package is available atdgpsi-R.A Python package for deep and linked Gaussian process emulations using stochastic imputation (SI)dgpsicurrently implements:Deep Gaussian process emulation with flexible architecture construction:multiple layers;multiple GP nodes;separable or non-separable squared exponential and Matérn2.5 kernels;global input connections;non-Gaussian likelihoods including Poisson, Negative-Binomial, and heteroskedastic Gaussian;Linked emulation of feed-forward systems of computer models:linking GP emulators of deterministic individual computer models;linking GP and DGP emulators of deterministic individual computer models;Multi-core predictions from GP, DGP, and Linked (D)GP emulators;Fast Leave-One-Out (LOO) cross validations for GP and DGP emulators.Calculations of ALM, MICE, PEI, and VIGF sequential design criterions.Installationdgpsicurrently requires Python version 3.7, 3.8, or 3.9. The package can be installed viapip:pipinstalldgpsiorconda:condainstall-cconda-forgedgpsiHowever, to gain the best performance of the package or you are using an Apple Silicon computer, we recommend the following steps for the installation:Download and installMiniforge3that is compatible to your system fromhere.Run the following command in your terminal app to create a virtual environment calleddgp_si:condacreate-ndgp_sipython=3.9.13Activate and enter the virtual environment:condaactivatedgp_siInstalldgpsi:for Apple Silicon users, you could gain speed-up by switching to Apple's Accelerate framework:condainstalldgpsi"libblas=*=*accelerate"for Intel users, you could gain speed-up by switching to MKL:condainstalldgpsi"libblas=*=*mkl"otherwise, simply run:condainstalldgpsiDemo and documentationPlease seedemofor some illustrative examples of the method. The API reference
of the package can be accessed fromhttps://dgpsi.readthedocs.io, and some tutorials will be soon added there.TipsSince SI is a stochastic inference, in case of unsatisfactory results, you may want to try to restart the training multiple times even with initial values of hyperparameters unchanged;The recommended DGP structure is a two-layered one with the number of GP nodes in the first layer equal to the number of input dimensions (i.e., number of input columns) and the number of GP nodes in the second layer equal to the number of output dimensions (i.e., number of output columns) or the number of parameters in the specified likelihood. Thedgpclass in the package is default to this structure.ContactPlease feel free to email me with any questions and feedbacks:Deyu Ming <deyu.ming.16@ucl.ac.uk>.Research NoticeThis package is part of an ongoing research initiative. For detailed information about the research aspects and guidelines for use, please refer to ourResearch Notice.ReferencesMing, D. and Williamson, D. (2023) Linked deep Gaussian process emulation for model networks. arXiv:2306.01212.Ming, D., Williamson, D., and Guillas, S. (2023) Deep Gaussian process emulation using stochastic imputation.Technometrics. 65(2), 150-161.Ming, D. and Guillas, S. (2021) Linked Gaussian process emulation for systems of computer models using Matérn kernels and adaptive design,SIAM/ASA Journal on Uncertainty Quantification. 9(4), 1615-1642.