b4d97d3cb8
* [R] better argument check in xgb.DMatrix; fixes #1480 * [R] showsd was a dummy; fixes #2044 * [R] better categorical encoding explanation in vignette; fixes #1989 * [R] new roxygen version docs update
615 lines
22 KiB
R
615 lines
22 KiB
R
#' Callback closures for booster training.
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#'
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#' These are used to perform various service tasks either during boosting iterations or at the end.
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#' This approach helps to modularize many of such tasks without bloating the main training methods,
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#' and it offers .
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#'
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#' @details
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#' By default, a callback function is run after each boosting iteration.
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#' An R-attribute \code{is_pre_iteration} could be set for a callback to define a pre-iteration function.
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#'
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#' When a callback function has \code{finalize} parameter, its finalizer part will also be run after
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#' the boosting is completed.
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#'
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#' WARNING: side-effects!!! Be aware that these callback functions access and modify things in
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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 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.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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#' @name callbacks
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NULL
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#
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# Callbacks -------------------------------------------------------------------
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#
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#' Callback closure for printing the result of evaluation
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#'
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#' @param period results would be printed every number of periods
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#' @param showsd whether standard deviations should be printed (when available)
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#'
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#' @details
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#' The callback function prints the result of evaluation at every \code{period} iterations.
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#' The initial and the last iteration's evaluations are always printed.
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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_evaluation} (also \code{bst_evaluation_err} when available),
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#' \code{iteration},
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#' \code{begin_iteration},
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#' \code{end_iteration}.
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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.print.evaluation <- function(period=1, showsd=TRUE) {
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callback <- function(env = parent.frame()) {
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if (length(env$bst_evaluation) == 0 ||
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period == 0 ||
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NVL(env$rank, 0) != 0 )
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return()
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i <- env$iteration
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if ((i-1) %% period == 0 ||
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i == env$begin_iteration ||
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i == env$end_iteration) {
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stdev <- if (showsd) env$bst_evaluation_err else NULL
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msg <- format.eval.string(i, env$bst_evaluation, stdev)
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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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callback
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}
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#' Callback closure for logging the evaluation history
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#'
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#' @details
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#' This callback function appends the current iteration evaluation results \code{bst_evaluation}
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#' available in the calling parent frame to the \code{evaluation_log} list in a calling frame.
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#'
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#' The finalizer callback (called with \code{finalize = TURE} in the end) converts
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#' the \code{evaluation_log} list into a final data.table.
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#'
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#' The iteration evaluation result \code{bst_evaluation} must be a named numeric vector.
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#'
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#' Note: in the column names of the final data.table, the dash '-' character is replaced with
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#' the underscore '_' in order to make the column names more like regular R identifiers.
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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{evaluation_log},
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#' \code{bst_evaluation},
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#' \code{iteration}.
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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.evaluation.log <- function() {
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mnames <- NULL
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init <- function(env) {
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if (!is.list(env$evaluation_log))
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stop("'evaluation_log' has to be a list")
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mnames <<- names(env$bst_evaluation)
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if (is.null(mnames) || any(mnames == ""))
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stop("bst_evaluation must have non-empty names")
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mnames <<- gsub('-', '_', names(env$bst_evaluation))
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if(!is.null(env$bst_evaluation_err))
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mnames <<- c(paste0(mnames, '_mean'), paste0(mnames, '_std'))
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}
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finalizer <- function(env) {
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env$evaluation_log <- as.data.table(t(simplify2array(env$evaluation_log)))
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setnames(env$evaluation_log, c('iter', mnames))
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if(!is.null(env$bst_evaluation_err)) {
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# rearrange col order from _mean,_mean,...,_std,_std,...
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# to be _mean,_std,_mean,_std,...
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len <- length(mnames)
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means <- mnames[1:(len/2)]
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stds <- mnames[(len/2 + 1):len]
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cnames <- numeric(len)
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cnames[c(TRUE, FALSE)] <- means
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cnames[c(FALSE, TRUE)] <- stds
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env$evaluation_log <- env$evaluation_log[, c('iter', cnames), with=FALSE]
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}
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}
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callback <- function(env = parent.frame(), finalize = FALSE) {
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if (is.null(mnames))
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init(env)
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if (finalize)
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return(finalizer(env))
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ev <- env$bst_evaluation
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if(!is.null(env$bst_evaluation_err))
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ev <- c(ev, env$bst_evaluation_err)
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env$evaluation_log <- c(env$evaluation_log,
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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.evaluation.log'
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callback
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}
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#' Callback closure for restetting the booster's parameters at each iteration.
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#'
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#' @param new_params a list where each element corresponds to a parameter that needs to be reset.
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#' Each element's value must be either a vector of values of length \code{nrounds}
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#' to be set at each iteration,
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#' or a function of two parameters \code{learning_rates(iteration, nrounds)}
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#' which returns a new parameter value by using the current iteration number
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#' and the total number of boosting rounds.
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#'
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#' @details
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#' This is a "pre-iteration" callback function used to reset booster's parameters
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#' at the beginning of each iteration.
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#'
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#' Note that when training is resumed from some previous model, and a function is used to
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#' reset a parameter value, the \code{nround} argument in this function would be the
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#' the number of boosting rounds in the current training.
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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} or \code{bst_folds},
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#' \code{iteration},
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#' \code{begin_iteration},
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#' \code{end_iteration}.
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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.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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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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if (length(formals(p)) != 2)
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stop("Parameter '", n, "' is a function but not of two arguments")
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} else if (is.numeric(p) || is.character(p)) {
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if (length(p) != nrounds)
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stop("Length of '", n, "' has to be equal to 'nrounds'")
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} else {
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stop("Parameter '", n, "' is not a function or a vector")
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}
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}
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}
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callback <- function(env = parent.frame()) {
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if (is.null(nrounds))
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init(env)
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i <- env$iteration
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pars <- lapply(new_params, function(p) {
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if (is.function(p))
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return(p(i, nrounds))
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p[i]
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})
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if (!is.null(env$bst)) {
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xgb.parameters(env$bst$handle) <- pars
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} else {
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for (fd in env$bst_folds)
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xgb.parameters(fd$bst) <- pars
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}
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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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callback
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}
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#' Callback closure to activate the early stopping.
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#'
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#' @param stopping_rounds The number of rounds with no improvement in
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#' the evaluation metric in order to stop the training.
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#' @param maximize whether to maximize the evaluation metric
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#' @param metric_name the name of an evaluation column to use as a criteria for early
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#' stopping. If not set, the last column would be used.
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#' Let's say the test data in \code{watchlist} was labelled as \code{dtest},
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#' and one wants to use the AUC in test data for early stopping regardless of where
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#' it is in the \code{watchlist}, then one of the following would need to be set:
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#' \code{metric_name='dtest-auc'} or \code{metric_name='dtest_auc'}.
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#' All dash '-' characters in metric names are considered equivalent to '_'.
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#' @param verbose whether to print the early stopping information.
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#'
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#' @details
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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'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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#' \item \code{best_ntreelimit} to use with the \code{ntreelimit} parameter in \code{predict}.
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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:
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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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#' }
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#'
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#' At least one data element is required in the evaluation watchlist for early stopping to work.
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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{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} 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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#'
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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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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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init <- function(env) {
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if (length(env$bst_evaluation) == 0)
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stop("For early stopping, watchlist must have at least one element")
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eval_names <- gsub('-', '_', names(env$bst_evaluation))
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if (!is.null(metric_name)) {
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metric_idx <<- which(gsub('-', '_', metric_name) == eval_names)
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if (length(metric_idx) == 0)
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stop("'metric_name' for early stopping is not one of the following:\n",
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paste(eval_names, collapse=' '), '\n')
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}
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if (is.null(metric_name) &&
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length(env$bst_evaluation) > 1) {
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metric_idx <<- length(eval_names)
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if (verbose)
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cat('Multiple eval metrics are present. Will use ',
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eval_names[metric_idx], ' for early stopping.\n', sep = '')
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}
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metric_name <<- eval_names[metric_idx]
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# maximixe is usually NULL when not set in xgb.train and built-in metrics
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if (is.null(maximize))
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maximize <<- ifelse(grepl('(_auc|_map|_ndcg)', metric_name), TRUE, FALSE)
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if (verbose && NVL(env$rank, 0) == 0)
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cat("Will train until ", metric_name, " hasn't improved in ",
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stopping_rounds, " rounds.\n\n", sep = '')
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best_iteration <<- 1
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if (maximize) best_score <<- -Inf
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env$stop_condition <- FALSE
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if (!is.null(env$bst)) {
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if (class(env$bst) != 'xgb.Booster')
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stop("'bst' in the parent frame must be an 'xgb.Booster'")
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if (!is.null(best_score <- xgb.attr(env$bst$handle, 'best_score'))) {
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best_score <<- as.numeric(best_score)
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best_iteration <<- as.numeric(xgb.attr(env$bst$handle, 'best_iteration')) + 1
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best_msg <<- as.numeric(xgb.attr(env$bst$handle, 'best_msg'))
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} else {
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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$bst_folds) || is.null(env$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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if (!is.null(env$bst)) {
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attr_best_score = as.numeric(xgb.attr(env$bst$handle, 'best_score'))
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if (best_score != attr_best_score)
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stop("Inconsistent 'best_score' values between the closure state: ", best_score,
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" and the xgb.attr: ", attr_best_score)
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env$bst$best_iteration = best_iteration
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env$bst$best_ntreelimit = best_ntreelimit
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env$bst$best_score = best_score
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} else {
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env$basket$best_iteration <- best_iteration
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env$basket$best_ntreelimit <- best_ntreelimit
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}
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}
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callback <- function(env = parent.frame(), finalize = FALSE) {
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if (best_iteration < 0)
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init(env)
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if (finalize)
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return(finalizer(env))
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i <- env$iteration
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score = env$bst_evaluation[metric_idx]
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if (( maximize && score > best_score) ||
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(!maximize && score < best_score)) {
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best_msg <<- format.eval.string(i, env$bst_evaluation, env$bst_evaluation_err)
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best_score <<- score
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best_iteration <<- i
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best_ntreelimit <<- best_iteration * env$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_ntreelimit)
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}
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} else if (i - best_iteration >= stopping_rounds) {
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env$stop_condition <- TRUE
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env$end_iteration <- i
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if (verbose && NVL(env$rank, 0) == 0)
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cat("Stopping. Best iteration:\n", best_msg, "\n\n", sep = '')
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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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callback
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}
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#' Callback closure for saving a model file.
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#'
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#' @param save_period save the model to disk after every
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#' \code{save_period} iterations; 0 means save the model at the end.
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#' @param save_name the name or path for the saved model file.
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#' It can contain a \code{\link[base]{sprintf}} formatting specifier
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#' to include the integer iteration number in the file name.
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#' E.g., with \code{save_name} = 'xgboost_%04d.model',
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#' the file saved at iteration 50 would be named "xgboost_0050.model".
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#'
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#' @details
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#' This callback function allows to save an xgb-model file, either periodically after each \code{save_period}'s or at the end.
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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},
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#' \code{iteration},
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#' \code{begin_iteration},
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#' \code{end_iteration}.
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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.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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callback <- function(env = parent.frame()) {
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if (is.null(env$bst))
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stop("'save_model' callback requires the 'bst' booster object in its calling frame")
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if ((save_period > 0 && (env$iteration - env$begin_iteration) %% save_period == 0) ||
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(save_period == 0 && env$iteration == env$end_iteration))
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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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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{params},
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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$data)
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pred <-
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if (env$num_class > 1) {
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matrix(NA_real_, N, env$num_class)
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} else {
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rep(NA_real_, N)
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}
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ntreelimit <- NVL(env$basket$best_ntreelimit,
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env$end_iteration * env$num_parallel_tree)
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if (NVL(env$params[['booster']], '') == 'gblinear') {
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ntreelimit <- 0 # must be 0 for gblinear
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}
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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.complete(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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#
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# Internal utility functions for callbacks ------------------------------------
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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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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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if (is.null(enames))
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stop('evaluation results must have names')
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iter <- sprintf('[%d]\t', iter)
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if (!is.null(eval_err)) {
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if (length(eval_res) != length(eval_err))
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stop('eval_res & eval_err lengths mismatch')
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res <- paste0(sprintf("%s:%f+%f", enames, eval_res, eval_err), collapse='\t')
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} else {
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res <- paste0(sprintf("%s:%f", enames, eval_res), collapse='\t')
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}
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return(paste0(iter, res))
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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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}
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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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}
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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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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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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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finalize = Filter(function(x) {
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'finalize' %in% names(formals(x))
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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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return(FALSE)
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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 == "")) {
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stop('query_names must be a non-empty vector of non-empty character names')
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}
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return(all(query_names %in% cb_names))
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}
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