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xgb.csv(printEveryN) parameter to print every n-th progress message

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by321 2015-05-13 21:51:05 -07:00
parent b8b0243d95
commit a4341f22a2
2 changed files with 9 additions and 3 deletions
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8
R-package/R/xgb.cv.R
View File

@ -54,6 +54,7 @@
#' @param folds \code{list} provides a possibility of using a list of pre-defined CV folds (each element must be a vector of fold's indices).
#' If folds are supplied, the nfold and stratified parameters would be ignored.
#' @param verbose \code{boolean}, print the statistics during the process
#' @param printEveryN Print every N progress messages when \code{verbose>0}. Default is 1 which means all messages are printed.
#' @param early_stop_round If \code{NULL}, the early stopping function is not triggered.
#' If set to an integer \code{k}, training with a validation set will stop if the performance
#' keeps getting worse consecutively for \code{k} rounds.
@ -93,7 +94,7 @@
#'
xgb.cv <- function(params=list(), data, nrounds, nfold, label = NULL, missing = NULL,
prediction = FALSE, showsd = TRUE, metrics=list(),
obj = NULL, feval = NULL, stratified = TRUE, folds = NULL, verbose = T,
obj = NULL, feval = NULL, stratified = TRUE, folds = NULL, verbose = T, printEveryN=1L,
early_stop_round = NULL, early.stop.round = NULL, maximize = NULL, ...) {
if (typeof(params) != "list") {
stop("xgb.cv: first argument params must be list")
@ -161,6 +162,7 @@ xgb.cv <- function(params=list(), data, nrounds, nfold, label = NULL, missing =
else
predictValues <- rep(0,xgb.numrow(dtrain))
history <- c()
printEveryN = max(as.integer(printEveryN), 1L)
for (i in 1:nrounds) {
msg <- list()
for (k in 1:nfold) {
@ -185,7 +187,9 @@ xgb.cv <- function(params=list(), data, nrounds, nfold, label = NULL, missing =
}
ret <- xgb.cv.aggcv(msg, showsd)
history <- c(history, ret)
if(verbose) paste(ret, "\n", sep="") %>% cat
if(verbose)
if (0==(i-1L)%%printEveryN)
cat(ret, "\n", sep="")
# early_Stopping
if (!is.null(early_stop_round)){

4
R-package/man/xgb.cv.Rd
View File

@ -7,7 +7,7 @@
xgb.cv(params = list(), data, nrounds, nfold, label = NULL,
missing = NULL, prediction = FALSE, showsd = TRUE, metrics = list(),
obj = NULL, feval = NULL, stratified = TRUE, folds = NULL,
verbose = T, early_stop_round = NULL, early.stop.round = NULL,
verbose = T, printEveryN=1L, early_stop_round = NULL, early.stop.round = NULL,
maximize = NULL, ...)
}
\arguments{
@ -66,6 +66,8 @@ If folds are supplied, the nfold and stratified parameters would be ignored.}
\item{verbose}{\code{boolean}, print the statistics during the process}
\item{printEveryN}{Print every N progress messages when \code{verbose>0}. Default is 1 which means all messages are printed.}
\item{early_stop_round}{If \code{NULL}, the early stopping function is not triggered.
If set to an integer \code{k}, training with a validation set will stop if the performance
keeps getting worse consecutively for \code{k} rounds.}