generateCOBBS: Generate Benchmark Sets for Continuous Optimization

In martinzaefferer/COBBS: Continuous Optimization Benchmarks By Simulation

Generate Benchmark Sets for Continuous Optimization

Description

Based on provided data and parameters, this function trains a GPR model, then generates the corresponding simulations, and returns them as a set of functions to the user. For the sake of comparison, the predictor (estimation) of the GPR model is also returned

Usage

generateCOBBS(x, y, control = list())

Arguments

x	matrix of sample locations
y	vector of observations at sample locations x
control	(list), specifying the options for model training and simulation: modelControl a list of controls for the GPR model, passed to control argument of gaussianProcessRegression. method a string specifiying the simulation method: "spectral" (default) or "decompose". xsim Locations for simulation, for decompose method only. Defaults to NA. nsim Number of simulation instances to generate. Defaults to 1. Ncos Parameter N, defining the number of cosine functions superimposed with method="spectral". Default is 100. conditionalSimulation Boolean specifying whether conditional simulation is used or not. Default is FALSE. seed Integer specifying a random number generator seed. Defaults to NA (no seed is set).

Value

fit

object/fit produced by gaussianProcessRegression

estimation

a function, GPR predictor

simulation

a list of one or more test functions to be used for benchmarking (based on GPR simulation)

Examples

## generate some data
seed <- 1234
groundtruth <- function(x){
  x=matrix(x,,1)
  apply(x,1,smoof::makeDeflectedCorrugatedSpringFunction(1))
}
lower = 0
upper = 10
set.seed(seed)
x <- matrix(c(runif(8,lower,upper)),,1)
y <- matrix(groundtruth(x),,1)
## specify some model configuration
mc  <- list(useLambda=FALSE,budgetAlgTheta=200,thetaUpper=1e4)
## and some configuration details for the simulation
cntrl <- list(modelControl=mc,
              nsim=1,
              seed=seed,
              method="spectral",
              #method="decompose",xsim=matrix(c(runif(200,-32,32)),,1),
              conditionalSimulation=TRUE
)
## generate model and functions
cobbsResult <- generateCOBBS(x,y,cntrl)
cobbsResult$fit
## plot the functions
require(ggplot2)
xseq <- seq(from=lower,by=0.001,to=upper)
p <- ggplot()
## plot trained model (predictor, estimatation)
df <- data.frame(x=xseq,y=cobbsResult$estimation(xseq))
p <- p + geom_line(aes(x=x,y=y),df,size=1,color="darkgray")
## plot  true function 
df <- data.frame(x=xseq,y=groundtruth(xseq))
p <- p + geom_line(aes(x=x,y=y),df,size=1,linetype=2)
## plot training data
df <- data.frame(x=x,y=y)
p <- p + geom_point(aes(x=x,y=y),df,size=2,fill="white",shape = 21)
## plot simulation
df <- data.frame(x=xseq,y=cobbsResult$simulation[[1]](xseq))
p <- p + geom_line(aes(x=x,y=y),df,size=1,color="red")
## some stuff for good looks
p <- p + theme_classic()
p <- p + xlab("x") + ylab("f(x)") 
print(p)
## prepare an expression that will be run during the experiments
## here: L-BFGS-B with random initial guess
require(nloptr)
expr <- expression(
  res <- lbfgs(x0=matrix(runif(1,lower,upper),1,1),fn = fnlog,
               lower=lower,upper=upper)
)
## run the experiments, with logging
## with each objective function produced by COBBS
resgt <- loggedExperiment(expr, groundtruth, 1:10)
reses <- loggedExperiment(expr, cobbsResult$estimation, 1:10)
ressi <- loggedExperiment(expr, cobbsResult$simulation[[1]], 1:10)
## plot results
print(plotBenchmarkPerformance(list(resgt,reses)))
print(plotBenchmarkPerformance(list(resgt,reses,ressi),c("groundtruth","estimation","simulation")))
## plot error, comparing against groundtruth
print(plotBenchmarkValidation(resgt,list(reses),c("estimation")))
print(plotBenchmarkValidation(resgt,list(reses,ressi),c("estimation","simulation")))

martinzaefferer/COBBS documentation built on July 19, 2023, 4:12 a.m.