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add demo for early_stopping in R

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This commit is contained in:
hetong007 2015-05-06 15:14:29 -07:00
parent 0f182b0b66
commit 419e4dbda6
2 changed files with 29 additions and 50 deletions
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56
R-package/R/xgb.train.R
View File

@ -139,31 +139,34 @@ xgb.train <- function(params=list(), data, nrounds, watchlist = list(),
params = append(params, list(...))
# Early stopping
if (!is.null(feval) && is.null(maximize) && !is.null(earlyStopRound))
stop('Please set maximize to note whether the model is maximizing the evaluation or not.')
if (length(watchlist) == 0 && !is.null(earlyStopRound))
stop('For early stopping you need at least one set in watchlist.')
if (is.null(maximize) && is.null(params$eval_metric) && !is.null(earlyStopRound))
stop('Please set maximize to note whether the model is maximizing the evaluation or not.')
if (is.null(maximize))
{
if (params$eval_metric %in% c('rmse','logloss','error','merror','mlogloss')) {
maximize = FALSE
} else {
maximize = TRUE
if (!is.null(earlyStopRound)){
if (!is.null(feval) && is.null(maximize))
stop('Please set maximize to note whether the model is maximizing the evaluation or not.')
if (length(watchlist) == 0)
stop('For early stopping you need at least one set in watchlist.')
if (is.null(maximize) && is.null(params$eval_metric))
stop('Please set maximize to note whether the model is maximizing the evaluation or not.')
if (is.null(maximize))
{
if (params$eval_metric %in% c('rmse','logloss','error','merror','mlogloss')) {
maximize = FALSE
} else {
maximize = TRUE
}
}
if (maximize) {
bestScore = 0
} else {
bestScore = Inf
}
bestInd = 0
earlyStopflag = FALSE
if (length(watchlist)>1)
warning('Only the first data set in watchlist is used for early stopping process.')
}
if (maximize) {
bestScore = 0
} else {
bestScore = Inf
}
bestInd = 0
earlyStopflag = FALSE
if (length(watchlist)>1 && !is.null(earlyStopRound))
warning('Only the first data set in watchlist is used for early stopping process.')
handle <- xgb.Booster(params, append(watchlist, dtrain))
bst <- xgb.handleToBooster(handle)
@ -174,8 +177,7 @@ xgb.train <- function(params=list(), data, nrounds, watchlist = list(),
cat(paste(msg, "\n", sep=""))
if (!is.null(earlyStopRound))
{
score = strsplit(msg,'\\s+')[[1]][1]
score = strsplit(score,':')[[1]][2]
score = strsplit(msg,':|\\s+')[[1]][3]
score = as.numeric(score)
if ((maximize && score>bestScore) || (!maximize && score<bestScore)) {
bestScore = score
@ -183,14 +185,12 @@ xgb.train <- function(params=list(), data, nrounds, watchlist = list(),
} else {
if (i-bestInd>earlyStopRound) {
earlyStopflag = TRUE
cat('Stopping. Best iteration:',bestInd)
break
}
}
}
}
if (earlyStopflag) {
cat('Stopping. Best iteration:',bestInd)
break
}
}
bst <- xgb.Booster.check(bst)
if (!is.null(earlyStopRound)) {

23
R-package/demo/early_Stopping.R
View File

@ -30,29 +30,8 @@ evalerror <- function(preds, dtrain) {
err <- as.numeric(sum(labels != (preds > 0)))/length(labels)
return(list(metric = "error", value = err))
}
print ('start training with user customized objective')
print ('start training with early Stopping setting')
# training with customized objective, we can also do step by step training
# simply look at xgboost.py's implementation of train
bst <- xgb.train(param, dtrain, num_round, watchlist, logregobj, evalerror, maximize = FALSE,
earlyStopRound = 3)
#
# there can be cases where you want additional information
# being considered besides the property of DMatrix you can get by getinfo
# you can set additional information as attributes if DMatrix
# set label attribute of dtrain to be label, we use label as an example, it can be anything
attr(dtrain, 'label') <- getinfo(dtrain, 'label')
# this is new customized objective, where you can access things you set
# same thing applies to customized evaluation function
logregobjattr <- function(preds, dtrain) {
# now you can access the attribute in customized function
labels <- attr(dtrain, 'label')
preds <- 1/(1 + exp(-preds))
grad <- preds - labels
hess <- preds * (1 - preds)
return(list(grad = grad, hess = hess))
}
print ('start training with user customized objective, with additional attributes in DMatrix')
# training with customized objective, we can also do step by step training
# simply look at xgboost.py's implementation of train
bst <- xgb.train(param, dtrain, num_round, watchlist, logregobjattr, evalerror, maximize = FALSE,
earlyStopRound = 3)