Static pruning of a DGP emulator

This function implements static pruning for a DGP emulator.

Usage

prune(object, control = list(), verb = TRUE)

Arguments

object

an instance of the dgp class that is generated by dgp().

control

a list that can supply the following two components to control static pruning of the DGP emulator:

• min_size, the minimum number of design points required to trigger pruning. Defaults to 10 times of the input dimensions.

• threshold, the \(R^2\) value above which a GP node is considered redundant and removable. Defaults to 0.97.

verb

a bool indicating if trace information will be printed during the function execution. Defaults to TRUE.

Value

An updated object that could be an instance of gp, dgp, or bundle (of GP emulators) class.

Details

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

Note

• The function requires a DGP emulator that has been trained with a dataset comprising a minimum size equal to min_size in control. If the training dataset size is smaller than this, it is recommended that the design of the DGP emulator is enriched and its structure pruned dynamically using the design() function. Depending on the design of the DGP emulator, static pruning may not be accurate. It is thus recommended that dynamic pruning is implemented as a part of a sequential design via design().

• The following slots:

  • loo and oos created by validate(); and

  • results created by predict();

  in object will be removed and not contained in the returned object.

Examples

if (FALSE) { # \dontrun{

# load the package and the Python env
library(dgpsi)

# construct the borehole function over a hypercube
f <- function(x){
  x[,1] <- (0.15 - 0.5) * x[,1] + 0.5
  x[,2] <- exp((log(50000) - log(100)) * x[,2] + log(100))
  x[,3] <- (115600 - 63070) *x[,3] + 63070
  x[,4] <- (1110 - 990) * x[,4] + 990
  x[,5] <- (116 - 63.1) * x[,5] + 63.1
  x[,6] <- (820 - 700) * x[,6] + 700
  x[,7] <- (1680 - 1120) * x[,7] + 1120
  x[,8] <- (12045 - 9855) * x[,8] + 9855
  y <- apply(x, 1, RobustGaSP::borehole)
}

# set a random seed
set_seed(999)

# generate training data
X <- maximinLHS(80, 8)
Y <- f(X)

# generate validation data
validate_x <- maximinLHS(500, 8)
validate_y <- f(validate_x)

# training a DGP emulator with anisotropic squared exponential kernels
m <- dgp(X, Y, share = F)

# OOS validation of the DGP emulator
plot(m, validate_x, validate_y)

# prune the emulator until no more GP nodes are removable
m <- prune(m)

# OOS validation of the resulting emulator
plot(m, validate_x, validate_y)
} # }