[R] CB naming change; cv-prediction as CB; add.cb function to ensure proper CB order; docs; minor fixes + changes
This commit is contained in:
Vadim Khotilovich
parent
4e1269b522
commit
76650c096f
@ -15,18 +15,19 @@
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#' the environment from which they are called from, which is a fairly uncommon thing to do in R.
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#'
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#' To write a custom callback closure, make sure you first understand the main concepts about R envoronments.
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#' Check either the R docs on \code{\link[base]{environment}} or the
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#' \href{http://adv-r.had.co.nz/Environments.html}{Environments chapter} from Hadley Wickham's "Advanced R" book.
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#' Then take a look at the code of \code{cb.reset_learning_rate} for a simple example,
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#' and see the \code{cb.log_evaluation} code for something more involved.
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#' Also, you would need to get familiar with the objects available inside of the \code{xgb.train} internal environment.
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#' Check either R documentation on \code{\link[base]{environment}} or the
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#' \href{http://adv-r.had.co.nz/Environments.html}{Environments chapter} from the "Advanced R"
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#' book by Hadley Wickham. Further, the best option is to read the code of some of the existing callbacks -
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#' choose ones that do something similar to what you want to achieve. Also, you would need to get familiar
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#' with the objects available inside of the \code{xgb.train} and \code{xgb.cv} internal environments.
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#'
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#' @seealso
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#' \code{\link{cb.print_evaluation}},
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#' \code{\link{cb.log_evaluation}},
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#' \code{\link{cb.reset_parameters}},
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#' \code{\link{cb.early_stop}},
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#' \code{\link{cb.save_model}},
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#' \code{\link{cb.print.evaluation}},
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#' \code{\link{cb.evaluation.log}},
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#' \code{\link{cb.reset.parameters}},
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#' \code{\link{cb.early.stop}},
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#' \code{\link{cb.save.model}},
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#' \code{\link{cb.cv.predict}},
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#' \code{\link{xgb.train}},
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#' \code{\link{xgb.cv}}
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#'
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@ -55,7 +56,7 @@ NULL
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#' \code{\link{callbacks}}
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#'
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#' @export
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cb.print_evaluation <- function(period=1) {
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cb.print.evaluation <- function(period=1) {
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callback <- function(env = parent.frame()) {
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if (length(env$bst_evaluation) == 0 ||
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@ -67,12 +68,12 @@ cb.print_evaluation <- function(period=1) {
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if ((i-1) %% period == 0 ||
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i == env$begin_iteration ||
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i == env$end_iteration) {
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msg <- format_eval_string(i, env$bst_evaluation, env$bst_evaluation_err)
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msg <- format.eval.string(i, env$bst_evaluation, env$bst_evaluation_err)
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cat(msg, '\n')
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}
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}
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attr(callback, 'call') <- match.call()
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attr(callback, 'name') <- 'cb.print_evaluation'
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attr(callback, 'name') <- 'cb.print.evaluation'
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callback
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}
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@ -100,7 +101,7 @@ cb.print_evaluation <- function(period=1) {
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#' \code{\link{callbacks}}
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#'
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#' @export
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cb.log_evaluation <- function() {
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cb.evaluation.log <- function() {
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mnames <- NULL
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@ -147,7 +148,7 @@ cb.log_evaluation <- function() {
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list(c(iter = env$iteration, ev)))
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}
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attr(callback, 'call') <- match.call()
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attr(callback, 'name') <- 'cb.log_evaluation'
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attr(callback, 'name') <- 'cb.evaluation.log'
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callback
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}
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@ -178,17 +179,27 @@ cb.log_evaluation <- function() {
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#' \code{\link{callbacks}}
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#'
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#' @export
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cb.reset_parameters <- function(new_params) {
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cb.reset.parameters <- function(new_params) {
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if (typeof(new_params) != "list")
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stop("'new_params' must be a list")
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pnames <- gsub("\\.", "_", names(new_params))
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# TODO: restrict the set of parameters that could be reset?
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nrounds <- NULL
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# run some checks in the begining
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init <- function(env) {
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nrounds <<- env$end_iteration - env$begin_iteration + 1
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if (is.null(env$bst) && is.null(env$bst_folds))
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stop("Parent frame has neither 'bst' nor 'bst_folds'")
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# Some parameters are not allowed to be changed,
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# since changing them would simply wreck some chaos
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not_allowed <- pnames %in%
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c('num_class', 'num_output_group', 'size_leaf_vector', 'updater_seq')
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if (any(not_allowed))
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stop('Parameters ', paste(pnames[not_allowed]), " cannot be changed during boosting.")
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for (n in pnames) {
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p <- new_params[[n]]
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if (is.function(p)) {
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@ -223,7 +234,7 @@ cb.reset_parameters <- function(new_params) {
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}
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attr(callback, 'is_pre_iteration') <- TRUE
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attr(callback, 'call') <- match.call()
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attr(callback, 'name') <- 'cb.reset_parameters'
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attr(callback, 'name') <- 'cb.reset.parameters'
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callback
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}
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@ -246,7 +257,7 @@ cb.reset_parameters <- function(new_params) {
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#' This callback function determines the condition for early stopping
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#' by setting the \code{stop_condition = TRUE} flag in its calling frame.
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#'
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#' The following additional fields are assigned to the model R object:
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#' The following additional fields are assigned to the model's R object:
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#' \itemize{
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#' \item \code{best_score} the evaluation score at the best iteration
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#' \item \code{best_iteration} at which boosting iteration the best score has occurred (1-based index)
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@ -254,7 +265,7 @@ cb.reset_parameters <- function(new_params) {
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#' It differs from \code{best_iteration} in multiclass or random forest settings.
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#' }
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#'
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#' The Same values are also stored as xgb-attributes, however:
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#' The Same values are also stored as xgb-attributes:
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#' \itemize{
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#' \item \code{best_iteration} is stored as a 0-based iteration index (for interoperability of binary models)
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#' \item \code{best_msg} message string is also stored.
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@ -266,22 +277,22 @@ cb.reset_parameters <- function(new_params) {
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#' \code{stop_condition},
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#' \code{bst_evaluation},
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#' \code{rank},
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#' \code{bst} or \code{bst_folds},
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#' \code{bst} (or \code{bst_folds} and \code{basket}),
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#' \code{iteration},
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#' \code{begin_iteration},
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#' \code{end_iteration},
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#' \code{num_parallel_tree},
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#' \code{num_class}.
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#' \code{num_parallel_tree}.
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#'
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#' @seealso
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#' \code{\link{callbacks}},
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#' \code{\link{xgb.attr}}
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#'
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#' @export
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cb.early_stop <- function(stopping_rounds, maximize=FALSE,
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cb.early.stop <- function(stopping_rounds, maximize=FALSE,
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metric_name=NULL, verbose=TRUE) {
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# state variables
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best_iteration <- -1
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best_ntreelimit <- -1
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best_score <- Inf
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best_msg <- NULL
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metric_idx <- 1
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@ -331,24 +342,23 @@ cb.early_stop <- function(stopping_rounds, maximize=FALSE,
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xgb.attributes(env$bst$handle) <- list(best_iteration = best_iteration - 1,
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best_score = best_score)
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}
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} else if (is.null(env$bst_folds)) {
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stop("Parent frame has neither 'bst' nor 'bst_folds'")
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} else if (is.null(env$bst_folds) || is.null(env$basket)) {
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stop("Parent frame has neither 'bst' nor ('bst_folds' and 'basket')")
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}
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}
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finalizer <- function(env) {
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best_ntreelimit = best_iteration * env$num_parallel_tree * env$num_class
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if (!is.null(env$bst)) {
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attr_best_score = as.numeric(xgb.attr(env$bst$handle, 'best_score'))
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if (best_score != attr_best_score)
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stop("Inconsistent 'best_score' between the state: ", best_score,
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stop("Inconsistent 'best_score' values between the closure state: ", best_score,
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" and the xgb.attr: ", attr_best_score)
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env$bst$best_score = best_score
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env$bst$best_iteration = best_iteration
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env$bst$best_ntreelimit = best_ntreelimit
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env$bst$best_score = best_score
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} else {
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attr(env$bst_folds, 'best_iteration') <- best_iteration
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attr(env$bst_folds, 'best_ntreelimit') <- best_ntreelimit
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env$basket$best_iteration <- best_iteration
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env$basket$best_ntreelimit <- best_ntreelimit
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}
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}
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@ -365,16 +375,17 @@ cb.early_stop <- function(stopping_rounds, maximize=FALSE,
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if (( maximize && score > best_score) ||
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(!maximize && score < best_score)) {
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best_msg <<- format_eval_string(i, env$bst_evaluation, env$bst_evaluation_err)
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best_msg <<- format.eval.string(i, env$bst_evaluation, env$bst_evaluation_err)
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best_score <<- score
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best_iteration <<- i
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best_ntreelimit <<- best_iteration * env$num_parallel_tree
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# save the property to attributes, so they will occur in checkpoint
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if (!is.null(env$bst)) {
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xgb.attributes(env$bst) <- list(
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best_iteration = best_iteration - 1, # convert to 0-based index
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best_score = best_score,
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best_msg = best_msg,
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best_ntreelimit = best_iteration * env$num_parallel_tree * env$num_class)
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best_ntreelimit = best_ntreelimit)
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}
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} else if (i - best_iteration >= stopping_rounds) {
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env$stop_condition <- TRUE
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@ -384,7 +395,7 @@ cb.early_stop <- function(stopping_rounds, maximize=FALSE,
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}
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}
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attr(callback, 'call') <- match.call()
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attr(callback, 'name') <- 'cb.early_stop'
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attr(callback, 'name') <- 'cb.early.stop'
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callback
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}
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@ -412,7 +423,7 @@ cb.early_stop <- function(stopping_rounds, maximize=FALSE,
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#' \code{\link{callbacks}}
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#'
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#' @export
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cb.save_model <- function(save_period = 0, save_name = "xgboost.model") {
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cb.save.model <- function(save_period = 0, save_name = "xgboost.model") {
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if (save_period < 0)
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stop("'save_period' cannot be negative")
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@ -426,7 +437,80 @@ cb.save_model <- function(save_period = 0, save_name = "xgboost.model") {
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xgb.save(env$bst, sprintf(save_name, env$iteration))
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}
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attr(callback, 'call') <- match.call()
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attr(callback, 'name') <- 'cb.save_model'
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attr(callback, 'name') <- 'cb.save.model'
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callback
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}
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#' Callback closure for returning cross-validation based predictions.
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#'
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#' @param save_models a flag for whether to save the folds' models.
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#'
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#' @details
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#' This callback function saves predictions for all of the test folds,
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#' and also allows to save the folds' models.
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#'
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#' It is a "finalizer" callback and it uses early stopping information whenever it is available,
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#' thus it must be run after the early stopping callback if the early stopping is used.
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#'
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#' Callback function expects the following values to be set in its calling frame:
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#' \code{bst_folds},
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#' \code{basket},
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#' \code{data},
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#' \code{end_iteration},
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#' \code{num_parallel_tree},
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#' \code{num_class}.
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#'
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#' @return
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#' Predictions are returned inside of the \code{pred} element, which is either a vector or a matrix,
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#' depending on the number of prediction outputs per data row. The order of predictions corresponds
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#' to the order of rows in the original dataset. Note that when a custom \code{folds} list is
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#' provided in \code{xgb.cv}, the predictions would only be returned properly when this list is a
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#' non-overlapping list of k sets of indices, as in a standard k-fold CV. The predictions would not be
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#' meaningful when user-profided folds have overlapping indices as in, e.g., random sampling splits.
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#' When some of the indices in the training dataset are not included into user-provided \code{folds},
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#' their prediction value would be \code{NA}.
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#'
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#' @seealso
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#' \code{\link{callbacks}}
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#'
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#' @export
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cb.cv.predict <- function(save_models = FALSE) {
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finalizer <- function(env) {
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if (is.null(env$basket) || is.null(env$bst_folds))
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stop("'cb.cv.predict' callback requires 'basket' and 'bst_folds' lists in its calling frame")
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N <- nrow(env$data)
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pred <- ifelse(env$num_class > 1,
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matrix(NA_real_, N, env$num_class),
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rep(NA_real_, N))
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ntreelimit <- NVL(env$basket$best_ntreelimit,
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env$end_iteration * env$num_parallel_tree)
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for (fd in env$bst_folds) {
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pr <- predict(fd$bst, fd$watchlist[[2]], ntreelimit = ntreelimit, reshape = TRUE)
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if (is.matrix(pred)) {
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pred[fd$index,] <- pr
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} else {
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pred[fd$index] <- pr
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}
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}
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env$basket$pred <- pred
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if (save_models) {
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env$basket$models <- lapply(env$bst_folds, function(fd) {
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xgb.attr(fd$bst, 'niter') <- env$end_iteration - 1
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xgb.Booster.check(xgb.handleToBooster(fd$bst), saveraw = TRUE)
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})
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}
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}
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callback <- function(env = parent.frame(), finalize = FALSE) {
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if (finalize)
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return(finalizer(env))
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}
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attr(callback, 'call') <- match.call()
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attr(callback, 'name') <- 'cb.cv.predict'
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callback
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}
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@ -436,7 +520,7 @@ cb.save_model <- function(save_period = 0, save_name = "xgboost.model") {
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#
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# Format the evaluation metric string
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format_eval_string <- function(iter, eval_res, eval_err=NULL) {
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format.eval.string <- function(iter, eval_res, eval_err=NULL) {
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if (length(eval_res) == 0)
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stop('no evaluation results')
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enames <- names(eval_res)
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@ -454,47 +538,68 @@ format_eval_string <- function(iter, eval_res, eval_err=NULL) {
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}
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# Extract callback names from the list of callbacks
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callback.names <- function(cb.list) {
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unlist(lapply(cb.list, function(x) attr(x, 'name')))
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callback.names <- function(cb_list) {
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unlist(lapply(cb_list, function(x) attr(x, 'name')))
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}
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# Extract callback calls from the list of callbacks
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callback.calls <- function(cb.list) {
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unlist(lapply(cb.list, function(x) attr(x, 'call')))
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callback.calls <- function(cb_list) {
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unlist(lapply(cb_list, function(x) attr(x, 'call')))
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}
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# Add a callback cb to the list and make sure that
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# cb.early.stop and cb.cv.predict are at the end of the list
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# with cb.cv.predict being the last (when present)
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add.cb <- function(cb_list, cb) {
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cb_list <- c(cb_list, cb)
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names(cb_list) <- callback.names(cb_list)
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if ('cb.early.stop' %in% names(cb_list)) {
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cb_list <- c(cb_list, cb_list['cb.early.stop'])
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# this removes only the first one
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cb_list['cb.early.stop'] <- NULL
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}
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if ('cb.cv.predict' %in% names(cb_list)) {
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cb_list <- c(cb_list, cb_list['cb.cv.predict'])
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cb_list['cb.cv.predict'] <- NULL
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}
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cb_list
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}
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# Sort callbacks list into categories
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categorize.callbacks <- function(cb.list) {
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categorize.callbacks <- function(cb_list) {
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list(
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pre_iter = Filter(function(x) {
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pre <- attr(x, 'is_pre_iteration')
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!is.null(pre) && pre
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}, cb.list),
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}, cb_list),
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post_iter = Filter(function(x) {
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pre <- attr(x, 'is_pre_iteration')
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is.null(pre) || !pre
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}, cb.list),
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}, cb_list),
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finalize = Filter(function(x) {
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'finalize' %in% names(formals(x))
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}, cb.list)
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}, cb_list)
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)
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}
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# Check whether all callback functions with names given by 'query.names' are present in the 'cb.list'.
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has.callbacks <- function(cb.list, query.names) {
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if (length(cb.list) < length(query.names))
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# Check whether all callback functions with names given by 'query_names' are present in the 'cb_list'.
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has.callbacks <- function(cb_list, query_names) {
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if (length(cb_list) < length(query_names))
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return(FALSE)
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if (!is.list(cb.list) ||
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!all(sapply(cb.list, class) == 'function'))
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stop('`cb.list`` must be a list of callback functions')
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cb.names <- callback.names(cb.list)
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if (!is.character(cb.names) ||
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length(cb.names) != length(cb.list) ||
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any(cb.names == ""))
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stop('All callbacks in the `cb.list` must have a non-empty `name` attribute')
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if (!is.character(query.names) ||
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length(query.names) == 0 ||
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any(query.names == ""))
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stop('query.names must be a non-empty vector of non-empty character names')
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return(all(query.names %in% cb.names))
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if (!is.list(cb_list) ||
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any(sapply(cb_list, class) != 'function')) {
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stop('`cb_list`` must be a list of callback functions')
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}
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cb_names <- callback.names(cb_list)
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if (!is.character(cb_names) ||
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length(cb_names) != length(cb_list) ||
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any(cb_names == "")) {
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stop('All callbacks in the `cb_list` must have a non-empty `name` attribute')
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}
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if (!is.character(query_names) ||
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length(query_names) == 0 ||
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any(query_names == "")) {
|
||||
stop('query_names must be a non-empty vector of non-empty character names')
|
||||
}
|
||||
return(all(query_names %in% cb_names))
|
||||
}
|
||||
|
||||
@ -10,20 +10,22 @@
|
||||
#' \item \code{binary:logistic} logistic regression for classification
|
||||
#' }
|
||||
#' \item \code{eta} step size of each boosting step
|
||||
#' \item \code{max.depth} maximum depth of the tree
|
||||
#' \item \code{max_depth} maximum depth of the tree
|
||||
#' \item \code{nthread} number of thread used in training, if not set, all threads are used
|
||||
#' }
|
||||
#'
|
||||
#' See \link{xgb.train} for further details.
|
||||
#' See \code{\link{xgb.train}} for further details.
|
||||
#' See also demo/ for walkthrough example in R.
|
||||
#' @param data takes an \code{xgb.DMatrix} or \code{Matrix} as the input.
|
||||
#' @param nrounds the max number of iterations
|
||||
#' @param nfold the original dataset is randomly partitioned into \code{nfold} equal size subsamples.
|
||||
#' @param label option field, when data is \code{Matrix}
|
||||
#' @param missing Missing is only used when input is dense matrix, pick a float
|
||||
#' value that represents missing value. Sometime a data use 0 or other extreme value to represents missing values.
|
||||
#' @param prediction A logical value indicating whether to return the prediction vector.
|
||||
#' @param showsd \code{boolean}, whether show standard deviation of cross validation
|
||||
#' @param label vector of response values. Should be provided only when data is \code{DMatrix}.
|
||||
#' @param missing is only used when input is a dense matrix. By default is set to NA, which means
|
||||
#' that NA values should be considered as 'missing' by the algorithm.
|
||||
#' Sometimes, 0 or other extreme value might be used to represent missing values.
|
||||
#' @param prediction A logical value indicating whether to return the test fold predictions
|
||||
#' from each CV model. This parameter engages the \code{\link{cb.cv.predict}} callback.
|
||||
#' @param showsd \code{boolean}, whether to show standard deviation of cross validation
|
||||
#' @param metrics, list of evaluation metrics to be used in cross validation,
|
||||
#' when it is not specified, the evaluation metric is chosen according to objective function.
|
||||
#' Possible options are:
|
||||
@ -39,30 +41,29 @@
|
||||
#' @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 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 stratified a \code{boolean} indicating whether sampling of folds should be stratified
|
||||
#' by the values of outcome labels.
|
||||
#' @param folds \code{list} provides a possibility to use a list of pre-defined CV folds
|
||||
#' (each element must be a vector of test fold's indices). When folds are supplied,
|
||||
#' the \code{nfold} and \code{stratified} parameters are 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.
|
||||
#' @param print_every_n Print each n-th iteration evaluation messages when \code{verbose>0}.
|
||||
#' Default is 1 which means all messages are printed. This parameter is passed to the
|
||||
#' \code{\link{cb.print.evaluation}} callback.
|
||||
#' @param early_stopping_rounds 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
|
||||
#' doesn't improve 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.
|
||||
#'
|
||||
#' Setting this parameter engages the \code{\link{cb.early.stop}} callback.
|
||||
#' @param maximize If \code{feval} and \code{early_stopping_rounds} are set,
|
||||
#' then this parameter must be set as well.
|
||||
#' When it is \code{TRUE}, it means the larger the evaluation score the better.
|
||||
#' This parameter is passed to the \code{\link{cb.early.stop}} callback.
|
||||
#' @param callbacks a list of callback functions to perform various task during boosting.
|
||||
#' See \code{\link{callbacks}}. Some of the callbacks are automatically created depending on the
|
||||
#' parameters' values. User can provide either existing or their own callback methods in order
|
||||
#' to customize the training process.
|
||||
#' @param ... other parameters to pass to \code{params}.
|
||||
#'
|
||||
#' @return
|
||||
#' TODO: update this...
|
||||
#'
|
||||
#' If \code{prediction = TRUE}, a list with the following elements is returned:
|
||||
#' \itemize{
|
||||
#' \item \code{dt} a \code{data.table} with each mean and standard deviation stat for training set and test set
|
||||
#' \item \code{pred} an array or matrix (for multiclass classification) with predictions for each CV-fold for the model having been trained on the data in all other folds.
|
||||
#' }
|
||||
#'
|
||||
#' If \code{prediction = FALSE}, just a \code{data.table} with each mean and standard deviation stat for training set and test set is returned.
|
||||
#'
|
||||
#' @details
|
||||
#' The original sample is randomly partitioned into \code{nfold} equal size subsamples.
|
||||
#'
|
||||
@ -74,23 +75,50 @@
|
||||
#'
|
||||
#' Adapted from \url{http://en.wikipedia.org/wiki/Cross-validation_\%28statistics\%29#k-fold_cross-validation}
|
||||
#'
|
||||
#' @return
|
||||
#' An object of class \code{xgb.cv.synchronous} with the following elements:
|
||||
#' \itemize{
|
||||
#' \item \code{call} a function call.
|
||||
#' \item \code{params} parameters that were passed to the xgboost library. Note that it does not
|
||||
#' capture parameters changed by the \code{\link{cb.reset.parameters}} callback.
|
||||
#' \item \code{callbacks} callback functions that were either automatically assigned or
|
||||
#' explicitely passed.
|
||||
#' \item \code{evaluation_log} evaluation history storead as a \code{data.table} with the
|
||||
#' first column corresponding to iteration number and the rest corresponding to the
|
||||
#' CV-based evaluation means and standard deviations for the training and test CV-sets.
|
||||
#' It is created by the \code{\link{cb.evaluation.log}} callback.
|
||||
#' \item \code{niter} number of boosting iterations.
|
||||
#' \item \code{folds} the list of CV folds' indices - either those passed through the \code{folds}
|
||||
#' parameter or randomly generated.
|
||||
#' \item \code{best_iteration} iteration number with the best evaluation metric value
|
||||
#' (only available with early stopping).
|
||||
#' \item \code{best_ntreelimit} the \code{ntreelimit} value corresponding to the best iteration,
|
||||
#' which could further be used in \code{predict} method
|
||||
#' (only available with early stopping).
|
||||
#' \item \code{pred} CV prediction values available when \code{prediction} is set.
|
||||
#' It is either vector or matrix (see \code{\link{cb.cv.predict}}).
|
||||
#' \item \code{models} a liost of the CV folds' models. It is only available with the explicit
|
||||
#' setting of the \code{cb.cv.predict(save_models = TRUE)} callback.
|
||||
#' }
|
||||
#'
|
||||
#' @examples
|
||||
#' data(agaricus.train, package='xgboost')
|
||||
#' dtrain <- xgb.DMatrix(agaricus.train$data, label = agaricus.train$label)
|
||||
#' history <- xgb.cv(data = dtrain, nround=3, nthread = 2, nfold = 5, metrics=list("rmse","auc"),
|
||||
#' max.depth =3, eta = 1, objective = "binary:logistic")
|
||||
#' print(history)
|
||||
#' cv <- xgb.cv(data = dtrain, nrounds = 3, nthread = 2, nfold = 5, metrics = list("rmse","auc"),
|
||||
#' max_depth = 3, eta = 1, objective = "binary:logistic")
|
||||
#' print(cv)
|
||||
#' print(cv, verbose=TRUE)
|
||||
#'
|
||||
#' @export
|
||||
xgb.cv <- function(params=list(), data, nrounds, nfold, label = NULL, missing = NA,
|
||||
prediction = FALSE, showsd = TRUE, metrics=list(),
|
||||
obj = NULL, feval = NULL, stratified = TRUE, folds = NULL,
|
||||
verbose = TRUE, print.every.n=1L,
|
||||
early.stop.round = NULL, maximize = NULL, callbacks = list(), ...) {
|
||||
verbose = TRUE, print_every_n=1L,
|
||||
early_stopping_rounds = NULL, maximize = NULL, callbacks = list(), ...) {
|
||||
|
||||
#strategy <- match.arg(strategy)
|
||||
check.deprecation(...)
|
||||
|
||||
params <- check.params(params, ...)
|
||||
params <- check.booster.params(params, ...)
|
||||
# TODO: should we deprecate the redundant 'metrics' parameter?
|
||||
for (m in metrics)
|
||||
params <- c(params, list("eval_metric" = m))
|
||||
@ -124,23 +152,28 @@ xgb.cv <- function(params=list(), data, nrounds, nfold, label = NULL, missing =
|
||||
|
||||
# verbosity & evaluation printing callback:
|
||||
params <- c(params, list(silent = 1))
|
||||
print.every.n <- max( as.integer(print.every.n), 1L)
|
||||
if (!has.callbacks(callbacks, 'cb.print_evaluation') && verbose)
|
||||
callbacks <- c(callbacks, cb.print_evaluation(print.every.n))
|
||||
|
||||
print_every_n <- max( as.integer(print_every_n), 1L)
|
||||
if (!has.callbacks(callbacks, 'cb.print.evaluation') && verbose) {
|
||||
callbacks <- add.cb(callbacks, cb.print.evaluation(print_every_n))
|
||||
}
|
||||
# evaluation log callback: always is on in CV
|
||||
evaluation_log <- list()
|
||||
if (!has.callbacks(callbacks, 'cb.log_evaluation'))
|
||||
callbacks <- c(callbacks, cb.log_evaluation())
|
||||
|
||||
if (!has.callbacks(callbacks, 'cb.evaluation.log')) {
|
||||
callbacks <- add.cb(callbacks, cb.evaluation.log())
|
||||
}
|
||||
# Early stopping callback
|
||||
stop_condition <- FALSE
|
||||
if (!is.null(early.stop.round) &&
|
||||
!has.callbacks(callbacks, 'cb.early_stop'))
|
||||
callbacks <- c(callbacks, cb.early_stop(early.stop.round, maximize=maximize, verbose=verbose))
|
||||
|
||||
if (!is.null(early_stopping_rounds) &&
|
||||
!has.callbacks(callbacks, 'cb.early.stop')) {
|
||||
callbacks <- add.cb(callbacks, cb.early.stop(early_stopping_rounds,
|
||||
maximize=maximize, verbose=verbose))
|
||||
}
|
||||
# CV-predictions callback
|
||||
if (prediction &&
|
||||
!has.callbacks(callbacks, 'cb.cv.predict')) {
|
||||
callbacks <- add.cb(callbacks, cb.cv.predict(save_model=FALSE))
|
||||
}
|
||||
# Sort the callbacks into categories
|
||||
names(callbacks) <- callback.names(callbacks)
|
||||
cb <- categorize.callbacks(callbacks)
|
||||
|
||||
|
||||
@ -152,10 +185,14 @@ xgb.cv <- function(params=list(), data, nrounds, nfold, label = NULL, missing =
|
||||
bst <- xgb.Booster(params, list(dtrain, dtest))
|
||||
list(dtrain=dtrain, bst=bst, watchlist=list(train=dtrain, test=dtest), index=folds[[k]])
|
||||
})
|
||||
# a "basket" to collect some results from callbacks
|
||||
basket <- list()
|
||||
|
||||
# extract parameters that can affect the relationship b/w #trees and #iterations
|
||||
num_class <- max(as.numeric(NVL(params[['num_class']], 1)), 1)
|
||||
num_parallel_tree <- max(as.numeric(NVL(params[['num_parallel_tree']], 1)), 1)
|
||||
|
||||
# those are fixed for CV (no training continuation)
|
||||
begin_iteration <- 1
|
||||
end_iteration <- nrounds
|
||||
|
||||
@ -184,34 +221,10 @@ xgb.cv <- function(params=list(), data, nrounds, nfold, label = NULL, missing =
|
||||
params = params,
|
||||
callbacks = callbacks,
|
||||
evaluation_log = evaluation_log,
|
||||
nboost = end_iteration,
|
||||
ntree = end_iteration * num_parallel_tree * num_class
|
||||
niter = end_iteration,
|
||||
folds = folds
|
||||
)
|
||||
if (!is.null(attr(bst_folds, 'best_iteration'))) {
|
||||
ret$best_iteration <- attr(bst_folds, 'best_iteration')
|
||||
ret$best_ntreelimit <- attr(bst_folds, 'best_ntreelimit')
|
||||
}
|
||||
ret$folds <- folds
|
||||
|
||||
# TODO: should making prediction go
|
||||
# a. into a callback?
|
||||
# b. return folds' models, and have a separate method for predictions?
|
||||
if (prediction) {
|
||||
ret$pred <- ifelse(num_class > 1,
|
||||
matrix(0, nrow(data), num_class),
|
||||
rep(0, nrow(data)))
|
||||
ntreelimit <- NVL(ret$best_ntreelimit, ret$ntree)
|
||||
for (fd in bst_folds) {
|
||||
pred <- predict(fd$bst, fd$watchlist[[2]], ntreelimit = ntreelimit)
|
||||
if (is.matrix(ret$pred))
|
||||
ret$pred[fd$index,] <- t(matrix(pred, num_class, length(fd$index)))
|
||||
else
|
||||
ret$pred[fd$index] <- pred
|
||||
}
|
||||
ret$bst <- lapply(bst_folds, function(x) {
|
||||
xgb.Booster.check(xgb.handleToBooster(x$bst), saveraw = TRUE)
|
||||
})
|
||||
}
|
||||
ret <- c(ret, basket)
|
||||
|
||||
class(ret) <- 'xgb.cv.synchronous'
|
||||
invisible(ret)
|
||||
@ -234,8 +247,8 @@ xgb.cv <- function(params=list(), data, nrounds, nfold, label = NULL, missing =
|
||||
#' @examples
|
||||
#' data(agaricus.train, package='xgboost')
|
||||
#' train <- agaricus.train
|
||||
#' cv <- xgb.cv(data = train$data, label = train$label, nfold = 5, max.depth = 2,
|
||||
#' eta = 1, nthread = 2, nround = 2, objective = "binary:logistic")
|
||||
#' cv <- xgb.cv(data = train$data, label = train$label, nfold = 5, max_depth = 2,
|
||||
#' eta = 1, nthread = 2, nrounds = 2, objective = "binary:logistic")
|
||||
#' print(cv)
|
||||
#' print(cv, verbose=TRUE)
|
||||
#'
|
||||
@ -264,7 +277,7 @@ print.xgb.cv.synchronous <- function(x, verbose=FALSE, ...) {
|
||||
})
|
||||
}
|
||||
|
||||
for (n in c('nboost', 'ntree', 'best_iteration', 'best_ntreelimit')) {
|
||||
for (n in c('niter', 'best_iteration', 'best_ntreelimit')) {
|
||||
if (is.null(x[[n]]))
|
||||
next
|
||||
cat(n, ': ', x[[n]], '\n', sep='')
|
||||
@ -279,6 +292,7 @@ print.xgb.cv.synchronous <- function(x, verbose=FALSE, ...) {
|
||||
if (verbose)
|
||||
cat('evaluation_log:\n')
|
||||
print(x$evaluation_log, row.names = FALSE, ...)
|
||||
|
||||
if (!is.null(x$best_iteration)) {
|
||||
cat('Best iteration:\n')
|
||||
print(x$evaluation_log[x$best_iteration], row.names = FALSE, ...)
|
||||
|
||||
@ -45,8 +45,8 @@
|
||||
#' \item \code{binary:logistic} logistic regression for binary classification. Output probability.
|
||||
#' \item \code{binary:logitraw} logistic regression for binary classification, output score before logistic transformation.
|
||||
#' \item \code{num_class} set the number of classes. To use only with multiclass objectives.
|
||||
#' \item \code{multi:softmax} set xgboost to do multiclass classification using the softmax objective. Class is represented by a number and should be from 0 to \code{num_class}.
|
||||
#' \item \code{multi:softprob} same as softmax, but output a vector of ndata * nclass, which can be further reshaped to ndata, nclass matrix. The result contains predicted probabilities of each data point belonging to each class.
|
||||
#' \item \code{multi:softmax} set xgboost to do multiclass classification using the softmax objective. Class is represented by a number and should be from 0 to \code{num_class - 1}.
|
||||
#' \item \code{multi:softprob} same as softmax, but prediction outputs a vector of ndata * nclass elements, which can be further reshaped to ndata, nclass matrix. The result contains predicted probabilities of each data point belonging to each class.
|
||||
#' \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
|
||||
@ -63,34 +63,41 @@
|
||||
#' the performance of each round's model on mat1 and mat2
|
||||
#'
|
||||
#' @param obj customized objective function. Returns gradient and second order
|
||||
#' gradient with given prediction and dtrain,
|
||||
#' gradient with given prediction and dtrain.
|
||||
#' @param feval custimized evaluation function. Returns
|
||||
#' \code{list(metric='metric-name', value='metric-value')} with given
|
||||
#' prediction and dtrain,
|
||||
#' 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.
|
||||
#' information of performance. If 2, xgboost will print some additional information.
|
||||
#' Setting \code{verbose > 0} automatically engages the \code{\link{cb.evaluation.log}} and
|
||||
#' \code{\link{cb.print.evaluation}} callback functions.
|
||||
#' @param print_every_n Print each n-th iteration evaluation messages when \code{verbose>0}.
|
||||
#' Default is 1 which means all messages are printed. This parameter is passed to the
|
||||
#' \code{\link{cb.print.evaluation}} callback.
|
||||
#' @param early_stopping_rounds 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 save_period save the model to the disk after every \code{save_period} rounds, 0 means save at the end.
|
||||
#' doesn't improve for \code{k} rounds.
|
||||
#' Setting this parameter engages the \code{\link{cb.early.stop}} callback.
|
||||
#' @param maximize If \code{feval} and \code{early_stopping_rounds} are set,
|
||||
#' then this parameter must be set as well.
|
||||
#' When it is \code{TRUE}, it means the larger the evaluation score the better.
|
||||
#' This parameter is passed to the \code{\link{cb.early.stop}} callback.
|
||||
#' @param save_period when it is non-NULL, model is saved to disk after every \code{save_period} rounds,
|
||||
#' 0 means save at the end. The saving is handled by the \code{\link{cb.save.model}} callback.
|
||||
#' @param save_name the name or path for periodically saved model file.
|
||||
#' @param xgb_model the previously built model to continue the trainig from.
|
||||
#' @param xgb_model a previously built model to continue the trainig from.
|
||||
#' Could be either an object of class \code{xgb.Booster}, or its raw data, or the name of a
|
||||
#' file with a previously saved model.
|
||||
#' @param callbacks a list of callback functions to perform various task during boosting.
|
||||
#' See \code{\link{callbacks}}. Some of the callbacks are currently automatically
|
||||
#' created when specific parameters are set.
|
||||
#' See \code{\link{callbacks}}. Some of the callbacks are automatically created depending on the
|
||||
#' parameters' values. User can provide either existing or their own callback methods in order
|
||||
#' to customize the training process.
|
||||
#' @param ... other parameters to pass to \code{params}.
|
||||
#' @param label the response variable. User should not set this field,
|
||||
#' if data is local data file or \code{xgb.DMatrix}.
|
||||
#' @param label vector of response values. Should not be provided when data is
|
||||
#' a local data file name or an \code{xgb.DMatrix}.
|
||||
#' @param missing by default is set to NA, which means that NA values should be considered as 'missing'
|
||||
#' by the algorithm. Sometimes, 0 or other extreme value might be used to represent missing values.
|
||||
#' This parameter is only used when input is dense matrix,
|
||||
#' This parameter is only used when input is a dense matrix.
|
||||
#' @param weight a vector indicating the weight for each row of the input.
|
||||
#'
|
||||
#' @details
|
||||
@ -112,25 +119,50 @@
|
||||
#' \item \code{rmse} root mean square error. \url{http://en.wikipedia.org/wiki/Root_mean_square_error}
|
||||
#' \item \code{logloss} negative log-likelihood. \url{http://en.wikipedia.org/wiki/Log-likelihood}
|
||||
#' \item \code{mlogloss} multiclass logloss. \url{https://www.kaggle.com/wiki/MultiClassLogLoss}
|
||||
#' \item \code{error} Binary classification error rate. It is calculated as \code{(wrong cases) / (all cases)}.
|
||||
#' \item \code{error} Binary classification error rate. It is calculated as \code{(# wrong cases) / (# all cases)}.
|
||||
#' By default, it uses the 0.5 threshold for predicted values to define negative and positive instances.
|
||||
#' Different threshold (e.g., 0.) could be specified as "error@0."
|
||||
#' \item \code{merror} Multiclass classification error rate. It is calculated as \code{(wrong cases) / (all cases)}.
|
||||
#' \item \code{merror} Multiclass classification error rate. It is calculated as \code{(# wrong cases) / (# all cases)}.
|
||||
#' \item \code{auc} Area under the curve. \url{http://en.wikipedia.org/wiki/Receiver_operating_characteristic#'Area_under_curve} for ranking evaluation.
|
||||
#' \item \code{ndcg} Normalized Discounted Cumulative Gain (for ranking task). \url{http://en.wikipedia.org/wiki/NDCG}
|
||||
#' }
|
||||
#'
|
||||
#' The following callbacks are automatically created when certain parameters are set:
|
||||
#' \itemize{
|
||||
#' \item \code{cb.print_evaluation} is turned on when \code{verbose > 0};
|
||||
#' and the \code{print.every.n} parameter is passed to it.
|
||||
#' \item \code{cb.log_evaluation} is on when \code{verbose > 0} and \code{watchlist} is present.
|
||||
#' \item \code{cb.early_stop}: when \code{early.stop.round} is set.
|
||||
#' \item \code{cb.save_model}: when \code{save_period > 0} is set.
|
||||
#' \item \code{cb.print.evaluation} is turned on when \code{verbose > 0};
|
||||
#' and the \code{print_every_n} parameter is passed to it.
|
||||
#' \item \code{cb.evaluation.log} is on when \code{verbose > 0} and \code{watchlist} is present.
|
||||
#' \item \code{cb.early.stop}: when \code{early_stopping_rounds} is set.
|
||||
#' \item \code{cb.save.model}: when \code{save_period > 0} is set.
|
||||
#' }
|
||||
#'
|
||||
#' @return
|
||||
#' TODO
|
||||
#' An object of class \code{xgb.Booster} with the following elements:
|
||||
#' \itemize{
|
||||
#' \item \code{handle} a handle (pointer) to the xgboost model in memory.
|
||||
#' \item \code{raw} a cached memory dump of the xgboost model saved as R's \code{raw} type.
|
||||
#' \item \code{niter} number of boosting iterations.
|
||||
#' \item \code{evaluation_log} evaluation history storead as a \code{data.table} with the
|
||||
#' first column corresponding to iteration number and the rest corresponding to evaluation
|
||||
#' metrics' values. It is created by the \code{\link{cb.evaluation.log}} callback.
|
||||
#' \item \code{call} a function call.
|
||||
#' \item \code{params} parameters that were passed to the xgboost library. Note that it does not
|
||||
#' capture parameters changed by the \code{\link{cb.reset.parameters}} callback.
|
||||
#' \item \code{callbacks} callback functions that were either automatically assigned or
|
||||
#' explicitely passed.
|
||||
#' \item \code{best_iteration} iteration number with the best evaluation metric value
|
||||
#' (only available with early stopping).
|
||||
#' \item \code{best_ntreelimit} the \code{ntreelimit} value corresponding to the best iteration,
|
||||
#' which could further be used in \code{predict} method
|
||||
#' (only available with early stopping).
|
||||
#' \item \code{best_score} the best evaluation metric value during early stopping.
|
||||
#' (only available with early stopping).
|
||||
#' }
|
||||
#'
|
||||
#' @seealso
|
||||
#' \code{\link{callbacks}},
|
||||
#' \code{\link{predict.xgb.Booster}},
|
||||
#' \code{\link{xgb.cv}}
|
||||
#'
|
||||
#' @examples
|
||||
#' data(agaricus.train, package='xgboost')
|
||||
@ -141,8 +173,9 @@
|
||||
#' watchlist <- list(eval = dtest, train = dtrain)
|
||||
#'
|
||||
#' ## A simple xgb.train example:
|
||||
#' param <- list(max.depth = 2, eta = 1, silent = 1, objective="binary:logistic", eval_metric="auc")
|
||||
#' bst <- xgb.train(param, dtrain, nthread = 2, nround = 2, watchlist)
|
||||
#' param <- list(max_depth = 2, eta = 1, silent = 1,
|
||||
#' objective = "binary:logistic", eval_metric = "auc")
|
||||
#' bst <- xgb.train(param, dtrain, nthread = 2, nrounds = 2, watchlist)
|
||||
#'
|
||||
#' ## An xgb.train example where custom objective and evaluation metric are used:
|
||||
#' logregobj <- function(preds, dtrain) {
|
||||
@ -157,33 +190,36 @@
|
||||
#' 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)
|
||||
#' bst <- xgb.train(param, dtrain, nthread = 2, nrounds = 2, watchlist)
|
||||
#'
|
||||
#' ## An xgb.train example of using variable learning rates at each iteration:
|
||||
#' my_etas <- list(eta = c(0.5, 0.1))
|
||||
#' bst <- xgb.train(param, dtrain, nthread = 2, nround = 2, watchlist,
|
||||
#' callbacks = list(cb.reset_parameters(my_etas)))
|
||||
#' bst <- xgb.train(param, dtrain, nthread = 2, nrounds = 2, watchlist,
|
||||
#' callbacks = list(cb.reset.parameters(my_etas)))
|
||||
#'
|
||||
#' ## Explicit use of the cb.log_evaluation callback allows to run
|
||||
#' ## Explicit use of the cb.evaluation.log callback allows to run
|
||||
#' ## xgb.train silently but still store the evaluation results:
|
||||
#' bst <- xgb.train(param, dtrain, nthread = 2, nround = 2, watchlist,
|
||||
#' verbose = 0, callbacks = list(cb.log_evaluation()))
|
||||
#' bst <- xgb.train(param, dtrain, nthread = 2, nrounds = 2, watchlist,
|
||||
#' verbose = 0, callbacks = list(cb.evaluation.log()))
|
||||
#' print(bst$evaluation_log)
|
||||
#'
|
||||
#' ## An 'xgboost' interface example:
|
||||
#' bst <- xgboost(data = agaricus.train$data, label = agaricus.train$label, max.depth = 2,
|
||||
#' eta = 1, nthread = 2, nround = 2, objective = "binary:logistic")
|
||||
#' bst <- xgboost(data = agaricus.train$data, label = agaricus.train$label,
|
||||
#' max_depth = 2, eta = 1, nthread = 2, nrounds = 2,
|
||||
#' objective = "binary:logistic")
|
||||
#' pred <- predict(bst, agaricus.test$data)
|
||||
#'
|
||||
#' @rdname xgb.train
|
||||
#' @export
|
||||
xgb.train <- function(params = list(), data, nrounds, watchlist = list(),
|
||||
obj = NULL, feval = NULL, verbose = 1, print.every.n=1L,
|
||||
early.stop.round = NULL, maximize = NULL,
|
||||
obj = NULL, feval = NULL, verbose = 1, print_every_n=1L,
|
||||
early_stopping_rounds = NULL, maximize = NULL,
|
||||
save_period = NULL, save_name = "xgboost.model",
|
||||
xgb_model = NULL, callbacks = list(), ...) {
|
||||
|
||||
params <- check.params(params, ...)
|
||||
check.deprecation(...)
|
||||
|
||||
params <- check.booster.params(params, ...)
|
||||
|
||||
check.custom.obj()
|
||||
check.custom.eval()
|
||||
@ -203,31 +239,30 @@ xgb.train <- function(params = list(), data, nrounds, watchlist = list(),
|
||||
|
||||
# evaluation printing callback
|
||||
params <- c(params, list(silent = ifelse(verbose > 1, 0, 1)))
|
||||
print.every.n <- max( as.integer(print.every.n), 1L)
|
||||
if (!has.callbacks(callbacks, 'cb.print_evaluation') && verbose)
|
||||
callbacks <- c(callbacks, cb.print_evaluation(print.every.n))
|
||||
|
||||
print_every_n <- max( as.integer(print_every_n), 1L)
|
||||
if (!has.callbacks(callbacks, 'cb.print.evaluation') && verbose) {
|
||||
callbacks <- add.cb(callbacks, cb.print.evaluation(print_every_n))
|
||||
}
|
||||
# evaluation log callback: it is automatically enabled only when verbose > 0
|
||||
evaluation_log <- list()
|
||||
if (verbose > 0 &&
|
||||
!has.callbacks(callbacks, 'cb.log_evaluation') &&
|
||||
length(watchlist) > 0)
|
||||
callbacks <- c(callbacks, cb.log_evaluation())
|
||||
|
||||
!has.callbacks(callbacks, 'cb.evaluation.log') &&
|
||||
length(watchlist) > 0) {
|
||||
callbacks <- add.cb(callbacks, cb.evaluation.log())
|
||||
}
|
||||
# Model saving callback
|
||||
if (!is.null(save_period) &&
|
||||
!has.callbacks(callbacks, 'cb.save_model'))
|
||||
callbacks <- c(callbacks, cb.save_model(save_period, save_name))
|
||||
|
||||
!has.callbacks(callbacks, 'cb.save.model')) {
|
||||
callbacks <- add.cb(callbacks, cb.save.model(save_period, save_name))
|
||||
}
|
||||
# Early stopping callback
|
||||
stop_condition <- FALSE
|
||||
if (!is.null(early.stop.round) &&
|
||||
!has.callbacks(callbacks, 'cb.early_stop'))
|
||||
callbacks <- c(callbacks,
|
||||
cb.early_stop(early.stop.round, maximize=maximize, verbose=verbose))
|
||||
|
||||
if (!is.null(early_stopping_rounds) &&
|
||||
!has.callbacks(callbacks, 'cb.early.stop')) {
|
||||
callbacks <- add.cb(callbacks, cb.early.stop(early_stopping_rounds,
|
||||
maximize=maximize, verbose=verbose))
|
||||
}
|
||||
# Sort the callbacks into categories
|
||||
names(callbacks) <- callback.names(callbacks)
|
||||
cb <- categorize.callbacks(callbacks)
|
||||
|
||||
|
||||
@ -235,23 +270,24 @@ xgb.train <- function(params = list(), data, nrounds, watchlist = list(),
|
||||
handle <- xgb.Booster(params, append(watchlist, dtrain), xgb_model)
|
||||
bst <- xgb.handleToBooster(handle)
|
||||
|
||||
|
||||
# When the 'xgb_model' was set, find out how many boosting rounds it has
|
||||
# by adjusting its number of trees for num_parallel_tree and multiclass
|
||||
ntree <- 0
|
||||
if (!is.null(xgb_model)) {
|
||||
ntree <- if ('ntree' %in% names(xgb_model)) xgb_model$ntree
|
||||
else length(grep('^booster', xgb.dump(bst)))
|
||||
}
|
||||
# extract parameters that can affect the relationship b/w #trees and #iterations
|
||||
num_class <- max(as.numeric(NVL(params[['num_class']], 1)), 1)
|
||||
num_parallel_tree <- max(as.numeric(NVL(params[['num_parallel_tree']], 1)), 1)
|
||||
nboost <- ntree %/% (num_parallel_tree * num_class)
|
||||
|
||||
# When the 'xgb_model' was set, find out how many boosting iterations it has
|
||||
niter_skip <- 0
|
||||
if (!is.null(xgb_model)) {
|
||||
niter_skip <- as.numeric(xgb.attr(bst, 'niter')) + 1
|
||||
if (length(niter_skip) == 0) {
|
||||
niter_skip <- xgb.ntree(bst) %/% (num_parallel_tree * num_class)
|
||||
}
|
||||
}
|
||||
|
||||
# TODO: distributed code
|
||||
rank <- 0
|
||||
|
||||
begin_iteration <- nboost + 1
|
||||
end_iteration <- nboost + nrounds
|
||||
begin_iteration <- niter_skip + 1
|
||||
end_iteration <- niter_skip + nrounds
|
||||
|
||||
# the main loop for boosting iterations
|
||||
for (iteration in begin_iteration:end_iteration) {
|
||||
@ -264,17 +300,18 @@ xgb.train <- function(params = list(), data, nrounds, watchlist = list(),
|
||||
if (length(watchlist) > 0)
|
||||
bst_evaluation <- xgb.iter.eval(bst$handle, watchlist, iteration - 1, feval)
|
||||
|
||||
xgb.attr(bst$handle, 'niter') <- iteration - 1
|
||||
|
||||
for (f in cb$post_iter) f()
|
||||
|
||||
if (stop_condition) break
|
||||
}
|
||||
for (f in cb$finalize) f(finalize=TRUE)
|
||||
|
||||
bst <- xgb.Booster.check(bst)
|
||||
bst <- xgb.Booster.check(bst, saveraw = TRUE)
|
||||
|
||||
# store the total number of boosting iterations and the number of trees
|
||||
bst$nboost = end_iteration
|
||||
bst$ntree = end_iteration * num_parallel_tree * num_class
|
||||
# store the total number of boosting iterations
|
||||
bst$niter = end_iteration
|
||||
|
||||
# store the evaluation results
|
||||
if (length(evaluation_log) > 0 &&
|
||||
@ -282,8 +319,10 @@ xgb.train <- function(params = list(), data, nrounds, watchlist = list(),
|
||||
# include the previous compatible history when available
|
||||
if (class(xgb_model) == 'xgb.Booster' &&
|
||||
!is.null(xgb_model$evaluation_log) &&
|
||||
all(colnames(evaluation_log) == colnames(xgb_model$evaluation_log)))
|
||||
all.equal(colnames(evaluation_log),
|
||||
colnames(xgb_model$evaluation_log))) {
|
||||
evaluation_log <- rbindlist(list(xgb_model$evaluation_log, evaluation_log))
|
||||
}
|
||||
bst$evaluation_log <- evaluation_log
|
||||
}
|
||||
|
||||
|
||||
Loading…
x
Reference in New Issue
Block a user