Made xgboost tests, examples, and vignettes conditional on xgboost being installed

Author: AnotherSamWilson

R/bayesOpt.R

@@ -114,67 +114,69 @@
 #'
 #' \dontrun{
 #' # Example 2 - Hyperparameter Tuning in xgboost
-#' library("xgboost")
+#' if (requireNamespace('xgboost', quietly = TRUE)) {
+#'   library("xgboost")
 #'
-#' data(agaricus.train, package = "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$data),by = 3))
-#' )
+#'   Folds <- list(
+#'       Fold1 = as.integer(seq(1,nrow(agaricus.train$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) {
+#'   scoringFunction <- function(max_depth, min_child_weight, subsample) {
 #'
-#'   dtrain <- xgb.DMatrix(agaricus.train$data,label = agaricus.train$label)
+#'     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"
-#'   )
+#'     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
-#'   )
+#'     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$best_iteration
+#'     return(
+#'       list(
+#'           Score = max(xgbcv$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)
-#' )
+#'   bounds <- list(
+#'       max_depth = c(2L, 10L)
+#'     , min_child_weight = c(1, 100)
+#'     , subsample = c(0.25, 1)
+#'   )
 #'
-#' ScoreResult <- bayesOpt(
-#'     FUN = scoringFunction
-#'   , bounds = bounds
-#'   , initPoints = 3
-#'   , iters.n = 2
-#'   , iters.k = 1
-#'   , acq = "ei"
-#'   , gsPoints = 10
-#'   , parallel = FALSE
-#'   , verbose = 1
-#' )
+#'   ScoreResult <- bayesOpt(
+#'       FUN = scoringFunction
+#'     , bounds = bounds
+#'     , initPoints = 3
+#'     , iters.n = 2
+#'     , iters.k = 1
+#'     , acq = "ei"
+#'     , gsPoints = 10
+#'     , parallel = FALSE
+#'     , verbose = 1
+#'   )
+#' }
 #' }
 #' @importFrom data.table data.table setDT setcolorder := as.data.table copy .I setnames is.data.table rbindlist
 #' @importFrom utils head tail

man/bayesOpt.Rd

@@ -16,6 +16,7 @@ knitr::opts_chunk$set(
 )
 options(width = 1000)
 set.seed(1991)
+xgbAvail <- requireNamespace('xgboost', quietly = TRUE)
 ```
 
 ********
@@ -35,7 +36,7 @@ Idealy, we would use the information from prior model evaluations to guide us in
 ## Practical Example
 
 In this example, we will be using the agaricus.train dataset provided in the XGBoost package. Here, we load the packages, data, and create a folds object to be used in the scoring function.
-```{r eval = TRUE, echo=TRUE, results = 'hide'}
+```{r eval = xgbAvail, echo=TRUE, results = 'hide'}
 library("xgboost")
 library("ParBayesianOptimization")
 
@@ -50,7 +51,7 @@ Folds <- list(
 
 Now we need to define the scoring function. This function should, at a minimum, return a list with a ```Score``` element, which is the model evaluation metric we want to maximize. We can also retain other pieces of information created by the scoring function by including them as named elements of the returned list. In this case, we want to retain the optimal number of rounds determined by the ```xgb.cv```:
 
-```{r eval = TRUE}
+```{r eval = xgbAvail}
 scoringFunction <- function(max_depth, min_child_weight, subsample) {
 
   dtrain <- xgb.DMatrix(agaricus.train$data,label = agaricus.train$label)
@@ -92,31 +93,32 @@ Some other objects we need to define are the bounds, GP kernel and acquisition f
   + The kernel is passed to the ```GauPro``` function ```GauPro_kernel_model``` and defines the covariance function.
   + The acquisition function defines the utility we get from using a certain parameter set.
 
-```{r eval = TRUE}
+```{r eval = xgbAvail}
 bounds <- list( 
     max_depth = c(2L, 10L)
   , min_child_weight = c(1, 25)
   , subsample = c(0.25, 1)
 )
 ```
 
-We are now ready to put this all into the ```BayesianOptimization``` function. 
+We are now ready to put this all into the ```bayesOpt``` function. 
 
-```{r eval = TRUE}
+```{r eval = xgbAvail}
 set.seed(1234)
 optObj <- bayesOpt(
     FUN = scoringFunction
   , bounds = bounds
   , initPoints = 4
   , iters.n = 3
 )
+
 ```
 
 The console informs us that the process initialized by running ```scoringFunction``` 4 times. It then fit a Gaussian process to the parameter-score pairs, found the global optimum of the acquisition function, and ran ```scoringFunction``` again. This process continued until we had 7 parameter-score pairs. You can interrogate the ```bayesOpt``` object to see the results:
 
-```{r eval = TRUE}
+```{r eval = xgbAvail}
 optObj$scoreSummary
 ```
-```{r eval = TRUE}
+```{r eval = xgbAvail}
 getBestPars(optObj)
 ```