Hendrik/xgboost
1
0
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity

Added new train_folds parameter (#5114)

Browse Source 
https://stackoverflow.com/questions/32433458/how-to-specify-train-and-test-indices-for-xgb-cv-in-r-package-xgboost/51412073
This commit is contained in:
Jiaming Yuan 2019-12-19 22:02:19 +08:00 committed by Michaël Benesty
parent 37fdfa03f8
commit e521bb6f83
1 changed files with 9 additions and 2 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

9
R-package/R/xgb.cv.R
View File

@ -47,6 +47,8 @@
#' @param folds \code{list} provides a possibility to use a list of pre-defined CV folds
#' (each element must be a vector of test fold's indices). When folds are supplied,
#' the \code{nfold} and \code{stratified} parameters are ignored.
#' @param train_folds \code{list} list specifying which indicies to use for training. If \code{NULL}
#' (the default) all indices not specified in \code{folds} will be used for training.
#' @param verbose \code{boolean}, print the statistics during the process
#' @param print_every_n Print each n-th iteration evaluation messages when \code{verbose>0}.
#' Default is 1 which means all messages are printed. This parameter is passed to the
@ -114,7 +116,7 @@
#' @export
xgb.cv <- function(params=list(), data, nrounds, nfold, label = NULL, missing = NA,
prediction = FALSE, showsd = TRUE, metrics=list(),
obj = NULL, feval = NULL, stratified = TRUE, folds = NULL,
obj = NULL, feval = NULL, stratified = TRUE, folds = NULL, train_folds = NULL,
verbose = TRUE, print_every_n=1L,
early_stopping_rounds = NULL, maximize = NULL, callbacks = list(), ...) {
@ -186,10 +188,15 @@ xgb.cv <- function(params=list(), data, nrounds, nfold, label = NULL, missing =
# create the booster-folds
# train_folds
dall <- xgb.get.DMatrix(data, label, missing)
bst_folds <- lapply(seq_along(folds), function(k) {
dtest <- slice(dall, folds[[k]])
# code originally contributed by @RolandASc on stackoverflow
if(is.null(train_folds))
dtrain <- slice(dall, unlist(folds[-k]))
else
dtrain <- slice(dall, train_folds[[k]])
handle <- xgb.Booster.handle(params, list(dtrain, dtest))
list(dtrain = dtrain, bst = handle, watchlist = list(train = dtrain, test=dtest), index = folds[[k]])
})