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make it possible to use a list of pre-defined CV folds in xgb.cv

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Vadim Khotilovich
2015-04-03 13:24:04 -05:00
parent c03b42054f
commit 31b0e53cd4
3 changed files with 42 additions and 28 deletions
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16
R-package/R/xgb.cv.R
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@@ -50,7 +50,9 @@
#' @param feval custimized evaluation function. Returns
#' \code{list(metric='metric-name', value='metric-value')} with given
#' prediction and dtrain.
#' @param stratified \code{boolean}, whether sampling of folds should be stratified by the values of labels in \code{data}
#' @param stratified \code{boolean} whether sampling of folds should be stratified by the values of labels in \code{data}
#' @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 ... other parameters to pass to \code{params}.
#'
@@ -84,10 +86,16 @@
#'
xgb.cv <- function(params=list(), data, nrounds, nfold, label = NULL, missing = NULL,
prediction = FALSE, showsd = TRUE, metrics=list(),
obj = NULL, feval = NULL, stratified = TRUE, verbose = T,...) {
obj = NULL, feval = NULL, stratified = TRUE, folds = NULL, verbose = T,...) {
if (typeof(params) != "list") {
stop("xgb.cv: first argument params must be list")
}
if(!is.null(folds)) {
if(class(folds)!="list" | length(folds) < 2) {
stop("folds must be a list with 2 or more elements that are vectors of indices for each CV-fold")
}
nfold <- length(folds)
}
if (nfold <= 1) {
stop("nfold must be bigger than 1")
}
@@ -102,7 +110,7 @@ xgb.cv <- function(params=list(), data, nrounds, nfold, label = NULL, missing =
params <- append(params, list("eval_metric"=mc))
}
folds <- xgb.cv.mknfold(dtrain, nfold, params, stratified)
xgb_folds <- xgb.cv.mknfold(dtrain, nfold, params, stratified, folds)
obj_type = params[['objective']]
mat_pred = FALSE
if (!is.null(obj_type) && obj_type=='multi:softprob')
@@ -119,7 +127,7 @@ xgb.cv <- function(params=list(), data, nrounds, nfold, label = NULL, missing =
for (i in 1:nrounds) {
msg <- list()
for (k in 1:nfold) {
fd <- folds[[k]]
fd <- xgb_folds[[k]]
succ <- xgb.iter.update(fd$booster, fd$dtrain, i - 1, obj)
if (i<nrounds) {
msg[[k]] <- xgb.iter.eval(fd$booster, fd$watchlist, i - 1, feval) %>% str_split("\t") %>% .[[1]]