inst/doc/tuningHyperparameters.R

In ParBayesianOptimization: Parallel Bayesian Optimization of Hyperparameters

## ----setup, include = FALSE-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)
backup_options <- options()
options(width = 1000)
set.seed(1991)
xgbAvail <- requireNamespace('xgboost', quietly = TRUE)

## ----eval = xgbAvail, echo=TRUE, results = 'hide'-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
library("xgboost")
library("ParBayesianOptimization")

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))
)

## ----eval = xgbAvail------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
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
    )
  )
}

## ----eval = xgbAvail------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
bounds <- list( 
    max_depth = c(2L, 10L)
  , min_child_weight = c(1, 25)
  , subsample = c(0.25, 1)
)

## ----eval = xgbAvail------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
set.seed(1234)
optObj <- bayesOpt(
    FUN = scoringFunction
  , bounds = bounds
  , initPoints = 4
  , iters.n = 3
)


## ----eval = xgbAvail------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
optObj$scoreSummary

## ----eval = xgbAvail------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
getBestPars(optObj)

## ----revert_options, include=FALSE--------------------------------------------
options(backup_options)