108 lines
3.9 KiB
R
108 lines
3.9 KiB
R
#' eXtreme Gradient Boosting Training
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#'
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#' The training function of xgboost
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#'
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#' @param params the list of parameters. Commonly used ones are:
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#' objective: objective function, common ones are
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#' - reg:linear linear regression
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#' - binary:logistic logistic regression for classification
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#' eta: step size of each boosting step
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#' max_depth: maximum depth of the tree
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#' nthread: number of thread used in training, if not set, all threads are used
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#'
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#' See \url{https://github.com/tqchen/xgboost/wiki/Parameters} for
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#' further details. See also demo/demo.R for walkthrough example in R.
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#' @param dtrain takes an \code{xgb.DMatrix} as the input.
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#' @param nrounds the max number of iterations
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#' @param watchlist what information should be printed when \code{verbose=1} or
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#' \code{verbose=2}. Watchlist is used to specify validation set monitoring
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#' during training. For example user can specify
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#' watchlist=list(validation1=mat1, validation2=mat2) to watch
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#' the performance of each round's model on mat1 and mat2
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#'
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#' @param obj customized objective function. Given prediction and dtrain,
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#' return gradient and second order gradient.
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#' @param feval custimized evaluation function. Given prediction and dtrain,
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#' return a \code{list(metric='metric-name', value='metric-value')}.
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#' @param ... other parameters to pass to \code{params}.
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#'
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#' @details
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#' This is the training function for xgboost.
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#'
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#' Parallelization is automatically enabled if OpenMP is present.
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#' Number of threads can also be manually specified via "nthread" parameter.
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#'
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#' This function only accepts an \code{xgb.DMatrix} object as the input.
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#' It supports advanced features such as watchlist, customized objective function,
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#' therefore it is more flexible than \code{\link{xgboost}}.
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#'
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#'
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#' @examples
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#' data(iris)
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#' iris[,5] <- as.numeric(iris[,5])
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#' dtrain = xgb.DMatrix(as.matrix(iris[,1:4]), label=iris[,5])
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#' dtest = dtrain
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#' watchlist <- list(eval = dtest, train = dtrain)
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#' param <- list(max_depth = 2, eta = 1, silent = 1)
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#' logregobj <- function(preds, dtrain) {
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#' labels <- getinfo(dtrain, "label")
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#' preds <- 1/(1 + exp(-preds))
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#' grad <- preds - labels
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#' hess <- preds * (1 - preds)
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#' return(list(grad = grad, hess = hess))
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#' }
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#' evalerror <- function(preds, dtrain) {
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#' labels <- getinfo(dtrain, "label")
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#' err <- as.numeric(sum(labels != (preds > 0)))/length(labels)
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#' return(list(metric = "error", value = err))
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#' }
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#' bst <- xgb.train(param, dtrain, nround = 2, watchlist, logregobj, evalerror)
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#' @export
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#'
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xgb.train <- function(params=list(), dtrain, nrounds, watchlist = list(),
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obj = NULL, feval = NULL, ...) {
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if (typeof(params) != "list") {
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stop("xgb.train: first argument params must be list")
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}
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if (class(dtrain) != "xgb.DMatrix") {
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stop("xgb.train: second argument dtrain must be xgb.DMatrix")
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}
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params = append(params, list(...))
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bst <- xgb.Booster(params, append(watchlist, dtrain))
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for (i in 1:nrounds) {
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if (is.null(obj)) {
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succ <- xgb.iter.update(bst, dtrain, i - 1)
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} else {
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pred <- xgb.predict(bst, dtrain)
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gpair <- obj(pred, dtrain)
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succ <- xgb.iter.boost(bst, dtrain, gpair)
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}
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if (length(watchlist) != 0) {
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if (is.null(feval)) {
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msg <- xgb.iter.eval(bst, watchlist, i - 1)
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cat(msg)
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cat("\n")
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} else {
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cat("[")
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cat(i)
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cat("]")
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for (j in 1:length(watchlist)) {
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w <- watchlist[j]
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if (length(names(w)) == 0) {
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stop("xgb.eval: name tag must be presented for every elements in watchlist")
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}
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ret <- feval(xgb.predict(bst, w[[1]]), w[[1]])
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cat("\t")
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cat(names(w))
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cat("-")
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cat(ret$metric)
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cat(":")
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cat(ret$value)
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}
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cat("\n")
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}
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}
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}
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return(bst)
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}
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