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rename arguments to be dot-seperated

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hetong007
2015-05-25 11:51:01 -07:00
parent 8d3a7e1688
commit 733d23aef8
6 changed files with 40 additions and 55 deletions
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23
R-package/R/xgb.cv.R
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@@ -54,12 +54,11 @@
#' @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.
#' @param print.every.n 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.
#' @param early.stop.round An alternative of \code{early_stop_round}.
#' @param maximize If \code{feval} and \code{early_stop_round} are set, then \code{maximize} must be set as well.
#' @param maximize If \code{feval} and \code{early.stop.round} are set, then \code{maximize} must be set as well.
#' \code{maximize=TRUE} means the larger the evaluation score the better.
#'
#' @param ... other parameters to pass to \code{params}.
@@ -94,8 +93,8 @@
#'
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, printEveryN=1L,
early_stop_round = NULL, early.stop.round = NULL, maximize = NULL, ...) {
obj = NULL, feval = NULL, stratified = TRUE, folds = NULL, verbose = T, print.every.n=1L,
early.stop.round = NULL, maximize = NULL, ...) {
if (typeof(params) != "list") {
stop("xgb.cv: first argument params must be list")
}
@@ -136,9 +135,7 @@ xgb.cv <- function(params=list(), data, nrounds, nfold, label = NULL, missing =
}
# Early Stopping
if (is.null(early_stop_round) && !is.null(early.stop.round))
early_stop_round = early.stop.round
if (!is.null(early_stop_round)){
if (!is.null(early.stop.round)){
if (!is.null(feval) && is.null(maximize))
stop('Please set maximize to note whether the model is maximizing the evaluation or not.')
if (is.null(maximize) && is.null(params$eval_metric))
@@ -178,7 +175,7 @@ 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)
print.every.n = max(as.integer(print.every.n), 1L)
for (i in 1:nrounds) {
msg <- list()
for (k in 1:nfold) {
@@ -204,11 +201,11 @@ xgb.cv <- function(params=list(), data, nrounds, nfold, label = NULL, missing =
ret <- xgb.cv.aggcv(msg, showsd)
history <- c(history, ret)
if(verbose)
if (0==(i-1L)%%printEveryN)
if (0==(i-1L)%%print.every.n)
cat(ret, "\n", sep="")
# early_Stopping
if (!is.null(early_stop_round)){
if (!is.null(early.stop.round)){
score = strsplit(ret,'\\s+')[[1]][1+length(metrics)+1]
score = strsplit(score,'\\+|:')[[1]][[2]]
score = as.numeric(score)
@@ -216,7 +213,7 @@ xgb.cv <- function(params=list(), data, nrounds, nfold, label = NULL, missing =
bestScore = score
bestInd = i
} else {
if (i-bestInd>=early_stop_round) {
if (i-bestInd>=early.stop.round) {
earlyStopflag = TRUE
cat('Stopping. Best iteration:',bestInd)
break