# NOT RUN {
# Example 1 - Optimization of a Linear Function
scoringFunction <- function(x) {
a <- exp(-(2-x)^2)*1.5
b <- exp(-(4-x)^2)*2
c <- exp(-(6-x)^2)*1
return(list(Score = a+b+c))
}
bounds <- list(x = c(0,8))
Results <- BayesianOptimization(
FUN = scoringFunction
, bounds = bounds
, initPoints = 5
, nIters = 8
, gsPoints = 10
)
# }
# NOT RUN {
# Example 2 - Hyperparameter Tuning in xgboost
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(agaricus.train$data),by = 3)))
scoringFunction <- function(max_depth, min_child_weight, subsample) {
dtrain <- xgb.DMatrix(agaricus.train$data,label = agaricus.train$label)
Pars <- list( booster = "gbtree"
, eta = 0.01
, max_depth = max_depth
, min_child_weight = min_child_weight
, subsample = subsample
, objective = "binary:logistic"
, eval_metric = "auc")
xgbcv <- xgb.cv( params = Pars
, data = dtrain
, nround = 100
, folds = Folds
, prediction = TRUE
, showsd = TRUE
, early_stopping_rounds = 5
, maximize = TRUE
, verbose = 0)
return(list( Score = max(xgbcv$evaluation_log$test_auc_mean)
, nrounds = xgbcv$best_iteration
)
)
}
bounds <- list(max_depth = c(2L, 10L)
, min_child_weight = c(1, 100)
, subsample = c(0.25, 1))
kern <- "Matern52"
acq <- "ei"
ScoreResult <- BayesianOptimization(
FUN = scoringFunction
, bounds = bounds
, initPoints = 10
, bulkNew = 1
, nIters = 12
, kern = kern
, acq = acq
, kappa = 2.576
, verbose = 1
, parallel = FALSE
, gsPoints = 50)
# }
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