ParBayesianOptimization | Parallelizable Bayesian Optimization in R | Machine Learning library

library("xgboost")

data(agaricus.train, package = "xgboost")

Folds <- list(
    Fold1 = as.integer(seq(1,nrow(agaricus.train$data),by = 3))
  , Fold2 = as.integer(seq(2,nrow(agaricus.train$data),by = 3))
  , Fold3 = as.integer(seq(3,nrow(agaricu  

simpleFunction <- function(x) dnorm(x,3,2)*1.5 + dnorm(x,7,1) + dnorm(x,10,2)

# Find the x that maximizes our simpleFunction
xmax <- optim(8,simpleFunction,method = "L-BFGS-B",lower = 0, upper = 15,control = list(fnscale = -1))$par

# Get a vi  

library(doParallel)
cl <- makeCluster(2)
registerDoParallel(cl)
clusterExport(cl,c('Folds','agaricus.train'))
clusterEvalQ(cl,expr= {
  library(xgboost)
})

tWithPar <- system.time(
  optObj <- bayesOpt(
      FUN = scoringFunction
    , bou