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Merge branch 'master' of https://github.com/pommedeterresautee/xgboost

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pommedeterresautee
2015-06-16 21:40:09 +02:00
parent ad2e93f6c5 ab219d3331
commit 37714eb331
42 changed files with 1072 additions and 298 deletions
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78
R-package/R/xgb.cv.R
View File

@@ -54,6 +54,13 @@
#' @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 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 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}.
#'
#' @return
@@ -86,7 +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,...) {
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")
}
@@ -109,7 +117,50 @@ xgb.cv <- function(params=list(), data, nrounds, nfold, label = NULL, missing =
for (mc in metrics) {
params <- append(params, list("eval_metric"=mc))
}
# customized objective and evaluation metric interface
if (!is.null(params$objective) && !is.null(obj))
stop("xgb.cv: cannot assign two different objectives")
if (!is.null(params$objective))
if (class(params$objective)=='function') {
obj = params$objective
params$objective = NULL
}
if (!is.null(params$eval_metric) && !is.null(feval))
stop("xgb.cv: cannot assign two different evaluation metrics")
if (!is.null(params$eval_metric))
if (class(params$eval_metric)=='function') {
feval = params$eval_metric
params$eval_metric = NULL
}
# Early Stopping
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$eval_metric))
stop('Please set maximize to note whether the model is maximizing the evaluation or not.')
if (is.null(maximize))
{
if (params$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(metrics)>1)
warning('Only the first metric is used for early stopping process.')
}
xgb_folds <- xgb.cv.mknfold(dtrain, nfold, params, stratified, folds)
obj_type = params[['objective']]
mat_pred = FALSE
@@ -124,6 +175,7 @@ xgb.cv <- function(params=list(), data, nrounds, nfold, label = NULL, missing =
else
predictValues <- rep(0,xgb.numrow(dtrain))
history <- c()
print.every.n = max(as.integer(print.every.n), 1L)
for (i in 1:nrounds) {
msg <- list()
for (k in 1:nfold) {
@@ -148,7 +200,27 @@ 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)%%print.every.n)
cat(ret, "\n", sep="")
# early_Stopping
if (!is.null(early.stop.round)){
score = strsplit(ret,'\\s+')[[1]][1+length(metrics)+2]
score = strsplit(score,'\\+|:')[[1]][[2]]
score = as.numeric(score)
if ((maximize && score>bestScore) || (!maximize && score<bestScore)) {
bestScore = score
bestInd = i
} else {
if (i-bestInd>=early.stop.round) {
earlyStopflag = TRUE
cat('Stopping. Best iteration:',bestInd)
break
}
}
}
}
colnames <- str_split(string = history[1], pattern = "\t")[[1]] %>% .[2:length(.)] %>% str_extract(".*:") %>% str_replace(":","") %>% str_replace("-", ".")

88
R-package/R/xgb.train.R
View File

@@ -36,7 +36,7 @@
#' 3. Task Parameters
#'
#' \itemize{
#' \item \code{objective} specify the learning task and the corresponding learning objective, and the objective options are below:
#' \item \code{objective} specify the learning task and the corresponding learning objective, users can pass a self-defined function to it. The default objective options are below:
#' \itemize{
#' \item \code{reg:linear} linear regression (Default).
#' \item \code{reg:logistic} logistic regression.
@@ -48,7 +48,7 @@
#' \item \code{rank:pairwise} set xgboost to do ranking task by minimizing the pairwise loss.
#' }
#' \item \code{base_score} the initial prediction score of all instances, global bias. Default: 0.5
#' \item \code{eval_metric} evaluation metrics for validation data. Default: metric will be assigned according to objective(rmse for regression, and error for classification, mean average precision for ranking). List is provided in detail section.
#' \item \code{eval_metric} evaluation metrics for validation data. Users can pass a self-defined function to it. Default: metric will be assigned according to objective(rmse for regression, and error for classification, mean average precision for ranking). List is provided in detail section.
#' }
#'
#' @param data takes an \code{xgb.DMatrix} as the input.
@@ -66,7 +66,12 @@
#' prediction and dtrain,
#' @param verbose If 0, xgboost will stay silent. If 1, xgboost will print
#' information of performance. If 2, xgboost will print information of both
#'
#' @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 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}.
#'
#' @details
@@ -98,7 +103,6 @@
#' dtrain <- xgb.DMatrix(agaricus.train$data, label = agaricus.train$label)
#' dtest <- dtrain
#' watchlist <- list(eval = dtest, train = dtrain)
#' param <- list(max.depth = 2, eta = 1, silent = 1)
#' logregobj <- function(preds, dtrain) {
#' labels <- getinfo(dtrain, "label")
#' preds <- 1/(1 + exp(-preds))
@@ -111,11 +115,13 @@
#' err <- as.numeric(sum(labels != (preds > 0)))/length(labels)
#' return(list(metric = "error", value = err))
#' }
#' bst <- xgb.train(param, dtrain, nthread = 2, nround = 2, watchlist, logregobj, evalerror)
#' param <- list(max.depth = 2, eta = 1, silent = 1, objective=logregobj,eval_metric=evalerror)
#' bst <- xgb.train(param, dtrain, nthread = 2, nround = 2, watchlist)
#' @export
#'
xgb.train <- function(params=list(), data, nrounds, watchlist = list(),
obj = NULL, feval = NULL, verbose = 1, ...) {
obj = NULL, feval = NULL, verbose = 1, print.every.n=1L,
early.stop.round = NULL, maximize = NULL, ...) {
dtrain <- data
if (typeof(params) != "list") {
stop("xgb.train: first argument params must be list")
@@ -130,19 +136,85 @@ xgb.train <- function(params=list(), data, nrounds, watchlist = list(),
}
if (length(watchlist) != 0 && verbose == 0) {
warning('watchlist is provided but verbose=0, no evaluation information will be printed')
watchlist <- list()
}
params = append(params, list(...))
# customized objective and evaluation metric interface
if (!is.null(params$objective) && !is.null(obj))
stop("xgb.train: cannot assign two different objectives")
if (!is.null(params$objective))
if (class(params$objective)=='function') {
obj = params$objective
params$objective = NULL
}
if (!is.null(params$eval_metric) && !is.null(feval))
stop("xgb.train: cannot assign two different evaluation metrics")
if (!is.null(params$eval_metric))
if (class(params$eval_metric)=='function') {
feval = params$eval_metric
params$eval_metric = NULL
}
# Early stopping
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 (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.')
}
handle <- xgb.Booster(params, append(watchlist, dtrain))
bst <- xgb.handleToBooster(handle)
print.every.n=max( as.integer(print.every.n), 1L)
for (i in 1:nrounds) {
succ <- xgb.iter.update(bst$handle, dtrain, i - 1, obj)
if (length(watchlist) != 0) {
msg <- xgb.iter.eval(bst$handle, watchlist, i - 1, feval)
cat(paste(msg, "\n", sep=""))
if (0== ( (i-1) %% print.every.n))
cat(paste(msg, "\n", sep=""))
if (!is.null(early.stop.round))
{
score = strsplit(msg,':|\\s+')[[1]][3]
score = as.numeric(score)
if ((maximize && score>bestScore) || (!maximize && score<bestScore)) {
bestScore = score
bestInd = i
} else {
if (i-bestInd>=early.stop.round) {
earlyStopflag = TRUE
cat('Stopping. Best iteration:',bestInd)
break
}
}
}
}
}
bst <- xgb.Booster.check(bst)
if (!is.null(early.stop.round)) {
bst$bestScore = bestScore
bst$bestInd = bestInd
}
return(bst)
}

12
R-package/R/xgboost.R
View File

@@ -28,8 +28,14 @@
#' @param verbose If 0, xgboost will stay silent. If 1, xgboost will print
#' information of performance. If 2, xgboost will print information of both
#' performance and construction progress information
#' @param print.every.n Print every N progress messages when \code{verbose>0}. Default is 1 which means all messages are printed.
#' @param missing Missing is only used when input is dense matrix, pick a float
#' value that represents missing value. Sometimes a data use 0 or other extreme value to represents missing values.
#' @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 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}.
#'
#' @details
@@ -51,7 +57,8 @@
#' @export
#'
xgboost <- function(data = NULL, label = NULL, missing = NULL, params = list(), nrounds,
verbose = 1, ...) {
verbose = 1, print.every.n = 1L, early.stop.round = NULL,
maximize = NULL, ...) {
if (is.null(missing)) {
dtrain <- xgb.get.DMatrix(data, label)
} else {
@@ -66,7 +73,8 @@ xgboost <- function(data = NULL, label = NULL, missing = NULL, params = list(),
watchlist <- list()
}
bst <- xgb.train(params, dtrain, nrounds, watchlist, verbose=verbose)
bst <- xgb.train(params, dtrain, nrounds, watchlist, verbose = verbose, print.every.n=print.every.n,
early.stop.round = early.stop.round)
return(bst)
}