dgpsi (version 2.4.0)

pack: Pack GP and DGP emulators into a bundle

Description

This function packs GP emulators and DGP emulators into a bundle class for sequential designs if each emulator emulates one output dimension of the underlying simulator.

Usage

pack(..., id = NULL)

Value

An S3 class named bundle to be used by design() for sequential designs. It has:

• a slot called id that is assigned through the id argument.

• N slots named emulator1,...,emulatorN, each of which contains a GP or DGP emulator, where N is the number of emulators that are provided to the function.

• a slot called data which contains two elements X and Y. X contains N matrices named emulator1,...,emulatorN that are training input data for different emulators. Y contains N single-column matrices named emulator1,...,emulatorN that are training output data for different emulators.

Arguments

...

a sequence or a list of emulators produced by gp() or dgp().

id

an ID to be assigned to the bundle emulator. If an ID is not provided (i.e., id = NULL), a UUID (Universally Unique Identifier) will be automatically generated and assigned to the emulator. Default to NULL.

Details

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

Examples

if (FALSE) {

# load packages and the Python env
library(lhs)
library(dgpsi)

# construct a function with a two-dimensional output
f <- function(x) {
 y1 = sin(30*((2*x-1)/2-0.4)^5)*cos(20*((2*x-1)/2-0.4))
 y2 = 1/3*sin(2*(2*x - 1))+2/3*exp(-30*(2*(2*x-1))^2)+1/3
 return(cbind(y1,y2))
}

# generate the initial design
X <- maximinLHS(10,1)
Y <- f(X)

# generate the validation data
validate_x <- maximinLHS(30,1)
validate_y <- f(validate_x)

# training a 2-layered DGP emulator with respect to each output with the global connection off
m1 <- dgp(X, Y[,1], connect=F)
m2 <- dgp(X, Y[,2], connect=F)

# specify the range of the input dimension
lim <- c(0, 1)

# pack emulators to form an emulator bundle
m <- pack(m1, m2)

# 1st wave of the sequential design with 10 steps with target RMSE 0.01
m <- design(m, N=10, limits = lim, f = f, x_test = validate_x, y_test = validate_y, target = 0.01)

# 2rd wave of the sequential design with 10 steps, the same target, and the aggregation
# function that takes the average of the criterion scores across the two outputs
g <- function(x){
  return(rowMeans(x))
}
m <- design(m, N=10, limits = lim, f = f, x_test = validate_x,
                    y_test = validate_y, aggregate = g, target = 0.01)

# draw sequential designs of the two packed emulators
draw(m, emulator = 1, type = 'design')
draw(m, emulator = 2, type = 'design')

# inspect the traces of RMSEs of the two packed emulators
draw(m, emulator = 1, type = 'rmse')
draw(m, emulator = 2, type = 'rmse')

# write and read the constructed emulator bundle
write(m, 'bundle_dgp')
m <- read('bundle_dgp')

# unpack the bundle into individual emulators
m_unpacked <- unpack(m)

# plot OOS validations of individual emulators
plot(m_unpacked[[1]], x_test = validate_x, y_test = validate_y[,1])
plot(m_unpacked[[2]], x_test = validate_x, y_test = validate_y[,2])
}