serialize: Serialize the constructed emulator

In dgpsi: Interface to 'dgpsi' for Deep and Linked Gaussian Process Emulations

Serialize the constructed emulator

Description

This function serializes the constructed emulator.

Usage

serialize(object, light = TRUE)

Arguments

object	an instance of the S3 class gp, dgp, lgp, or bundle.
light	a bool indicating if a light version of the constructed emulator (that requires a small storage) will be serialized. Defaults to TRUE.

Details

See further examples and tutorials at https://mingdeyu.github.io/dgpsi-R/.

Value

A serialized version of object.

Note

Since the constructed emulators are 'python' objects, they cannot be directly exported to other R processes for parallel processing in multi-session workers created through spawning. This function provides a solution by converting the emulators into serialized objects, which can be restored using deserialize() for multi-session processing. Note that in forking, serialization is generally not required.

Examples

## Not run: 

library(future)
library(future.apply)
library(dgpsi)

# model
f <- function(x) {
 (sin(7.5*x)+1)/2
}

# training data
X <- seq(0, 1, length = 10)
Y <- sapply(X, f)

# train a DGP emulator
m <- dgp(X, Y, name = "matern2.5")

# testing input data
X_dgp <- seq(0, 1, length = 100)

# serialize the DGP emulator
m_serialized <- serialize(m)

# start a multi-session with three cores for parallel predictions
plan(multisession, workers = 3)

# perform parallel predictions
results <- future_lapply(1:length(X_dgp), function(i) {
  m_deserialized <- deserialize(m_serialized)
  mean_i <- predict(m_deserialized, X_dgp[i])$results$mean
}, future.seed = TRUE)

# reset the future plan to sequential
plan(sequential)

# combine mean predictions
pred_mean <- do.call(rbind, results)

## End(Not run)

dgpsi documentation built on April 3, 2025, 9:26 p.m.