Alien-XGBoost
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xgboost/R-package/R/callbacks.R view on Meta::CPAN
#' Callback closures for booster training.
#'
#' These are used to perform various service tasks either during boosting iterations or at the end.
#' This approach helps to modularize many of such tasks without bloating the main training methods,
#' and it offers .
#'
#' @details
#' By default, a callback function is run after each boosting iteration.
#' An R-attribute \code{is_pre_iteration} could be set for a callback to define a pre-iteration function.
#'
#' When a callback function has \code{finalize} parameter, its finalizer part will also be run after
#' the boosting is completed.
#'
#' WARNING: side-effects!!! Be aware that these callback functions access and modify things in
#' the environment from which they are called from, which is a fairly uncommon thing to do in R.
#'
#' To write a custom callback closure, make sure you first understand the main concepts about R envoronments.
#' Check either R documentation on \code{\link[base]{environment}} or the
#' \href{http://adv-r.had.co.nz/Environments.html}{Environments chapter} from the "Advanced R"
#' book by Hadley Wickham. Further, the best option is to read the code of some of the existing callbacks -
#' choose ones that do something similar to what you want to achieve. Also, you would need to get familiar
#' with the objects available inside of the \code{xgb.train} and \code{xgb.cv} internal environments.
#'
#' @seealso
#' \code{\link{cb.print.evaluation}},
#' \code{\link{cb.evaluation.log}},
#' \code{\link{cb.reset.parameters}},
#' \code{\link{cb.early.stop}},
#' \code{\link{cb.save.model}},
#' \code{\link{cb.cv.predict}},
#' \code{\link{xgb.train}},
#' \code{\link{xgb.cv}}
#'
#' @name callbacks
NULL
#
# Callbacks -------------------------------------------------------------------
#
#' Callback closure for printing the result of evaluation
#'
#' @param period results would be printed every number of periods
#' @param showsd whether standard deviations should be printed (when available)
#'
#' @details
#' The callback function prints the result of evaluation at every \code{period} iterations.
#' The initial and the last iteration's evaluations are always printed.
#'
#' Callback function expects the following values to be set in its calling frame:
#' \code{bst_evaluation} (also \code{bst_evaluation_err} when available),
#' \code{iteration},
#' \code{begin_iteration},
#' \code{end_iteration}.
#'
#' @seealso
#' \code{\link{callbacks}}
#'
#' @export
cb.print.evaluation <- function(period = 1, showsd = TRUE) {
callback <- function(env = parent.frame()) {
if (length(env$bst_evaluation) == 0 ||
period == 0 ||
NVL(env$rank, 0) != 0 )
return()
i <- env$iteration
if ((i-1) %% period == 0 ||
i == env$begin_iteration ||
i == env$end_iteration) {
stdev <- if (showsd) env$bst_evaluation_err else NULL
msg <- format.eval.string(i, env$bst_evaluation, stdev)
cat(msg, '\n')
}
}
attr(callback, 'call') <- match.call()
attr(callback, 'name') <- 'cb.print.evaluation'
callback
}
xgboost/R-package/R/callbacks.R view on Meta::CPAN
for (n in pnames) {
p <- new_params[[n]]
if (is.function(p)) {
if (length(formals(p)) != 2)
stop("Parameter '", n, "' is a function but not of two arguments")
} else if (is.numeric(p) || is.character(p)) {
if (length(p) != nrounds)
stop("Length of '", n, "' has to be equal to 'nrounds'")
} else {
stop("Parameter '", n, "' is not a function or a vector")
}
}
}
callback <- function(env = parent.frame()) {
if (is.null(nrounds))
init(env)
i <- env$iteration
pars <- lapply(new_params, function(p) {
if (is.function(p))
return(p(i, nrounds))
p[i]
})
if (!is.null(env$bst)) {
xgb.parameters(env$bst$handle) <- pars
} else {
for (fd in env$bst_folds)
xgb.parameters(fd$bst) <- pars
}
}
attr(callback, 'is_pre_iteration') <- TRUE
attr(callback, 'call') <- match.call()
attr(callback, 'name') <- 'cb.reset.parameters'
callback
}
#' Callback closure to activate the early stopping.
#'
#' @param stopping_rounds The number of rounds with no improvement in
#' the evaluation metric in order to stop the training.
#' @param maximize whether to maximize the evaluation metric
#' @param metric_name the name of an evaluation column to use as a criteria for early
#' stopping. If not set, the last column would be used.
#' Let's say the test data in \code{watchlist} was labelled as \code{dtest},
#' and one wants to use the AUC in test data for early stopping regardless of where
#' it is in the \code{watchlist}, then one of the following would need to be set:
#' \code{metric_name='dtest-auc'} or \code{metric_name='dtest_auc'}.
#' All dash '-' characters in metric names are considered equivalent to '_'.
#' @param verbose whether to print the early stopping information.
#'
#' @details
#' This callback function determines the condition for early stopping
#' by setting the \code{stop_condition = TRUE} flag in its calling frame.
#'
#' The following additional fields are assigned to the model's R object:
#' \itemize{
#' \item \code{best_score} the evaluation score at the best iteration
#' \item \code{best_iteration} at which boosting iteration the best score has occurred (1-based index)
#' \item \code{best_ntreelimit} to use with the \code{ntreelimit} parameter in \code{predict}.
#' It differs from \code{best_iteration} in multiclass or random forest settings.
#' }
#'
#' The Same values are also stored as xgb-attributes:
#' \itemize{
#' \item \code{best_iteration} is stored as a 0-based iteration index (for interoperability of binary models)
#' \item \code{best_msg} message string is also stored.
#' }
#'
#' At least one data element is required in the evaluation watchlist for early stopping to work.
#'
#' Callback function expects the following values to be set in its calling frame:
#' \code{stop_condition},
#' \code{bst_evaluation},
#' \code{rank},
#' \code{bst} (or \code{bst_folds} and \code{basket}),
#' \code{iteration},
#' \code{begin_iteration},
#' \code{end_iteration},
#' \code{num_parallel_tree}.
#'
#' @seealso
#' \code{\link{callbacks}},
#' \code{\link{xgb.attr}}
#'
#' @export
cb.early.stop <- function(stopping_rounds, maximize = FALSE,
metric_name = NULL, verbose = TRUE) {
# state variables
best_iteration <- -1
best_ntreelimit <- -1
best_score <- Inf
best_msg <- NULL
metric_idx <- 1
init <- function(env) {
if (length(env$bst_evaluation) == 0)
stop("For early stopping, watchlist must have at least one element")
eval_names <- gsub('-', '_', names(env$bst_evaluation))
if (!is.null(metric_name)) {
metric_idx <<- which(gsub('-', '_', metric_name) == eval_names)
if (length(metric_idx) == 0)
stop("'metric_name' for early stopping is not one of the following:\n",
paste(eval_names, collapse = ' '), '\n')
}
if (is.null(metric_name) &&
length(env$bst_evaluation) > 1) {
metric_idx <<- length(eval_names)
if (verbose)
cat('Multiple eval metrics are present. Will use ',
eval_names[metric_idx], ' for early stopping.\n', sep = '')
}
metric_name <<- eval_names[metric_idx]
# maximize is usually NULL when not set in xgb.train and built-in metrics
if (is.null(maximize))
maximize <<- grepl('(_auc|_map|_ndcg)', metric_name)
if (verbose && NVL(env$rank, 0) == 0)
cat("Will train until ", metric_name, " hasn't improved in ",
stopping_rounds, " rounds.\n\n", sep = '')
best_iteration <<- 1
if (maximize) best_score <<- -Inf
env$stop_condition <- FALSE
if (!is.null(env$bst)) {
if (!inherits(env$bst, 'xgb.Booster'))
stop("'bst' in the parent frame must be an 'xgb.Booster'")
if (!is.null(best_score <- xgb.attr(env$bst$handle, 'best_score'))) {
best_score <<- as.numeric(best_score)
best_iteration <<- as.numeric(xgb.attr(env$bst$handle, 'best_iteration')) + 1
best_msg <<- as.numeric(xgb.attr(env$bst$handle, 'best_msg'))
} else {
xgb.attributes(env$bst$handle) <- list(best_iteration = best_iteration - 1,
best_score = best_score)
}
} else if (is.null(env$bst_folds) || is.null(env$basket)) {
stop("Parent frame has neither 'bst' nor ('bst_folds' and 'basket')")
}
}
finalizer <- function(env) {
if (!is.null(env$bst)) {
attr_best_score = as.numeric(xgb.attr(env$bst$handle, 'best_score'))
if (best_score != attr_best_score)
stop("Inconsistent 'best_score' values between the closure state: ", best_score,
" and the xgb.attr: ", attr_best_score)
env$bst$best_iteration = best_iteration
env$bst$best_ntreelimit = best_ntreelimit
env$bst$best_score = best_score
} else {
env$basket$best_iteration <- best_iteration
env$basket$best_ntreelimit <- best_ntreelimit
}
}
callback <- function(env = parent.frame(), finalize = FALSE) {
if (best_iteration < 0)
init(env)
if (finalize)
return(finalizer(env))
i <- env$iteration
score = env$bst_evaluation[metric_idx]
if (( maximize && score > best_score) ||
(!maximize && score < best_score)) {
best_msg <<- format.eval.string(i, env$bst_evaluation, env$bst_evaluation_err)
best_score <<- score
best_iteration <<- i
best_ntreelimit <<- best_iteration * env$num_parallel_tree
# save the property to attributes, so they will occur in checkpoint
if (!is.null(env$bst)) {
xgb.attributes(env$bst) <- list(
best_iteration = best_iteration - 1, # convert to 0-based index
best_score = best_score,
best_msg = best_msg,
best_ntreelimit = best_ntreelimit)
}
} else if (i - best_iteration >= stopping_rounds) {
env$stop_condition <- TRUE
env$end_iteration <- i
if (verbose && NVL(env$rank, 0) == 0)
cat("Stopping. Best iteration:\n", best_msg, "\n\n", sep = '')
}
}
attr(callback, 'call') <- match.call()
attr(callback, 'name') <- 'cb.early.stop'
callback
}
#' Callback closure for saving a model file.
#'
#' @param save_period save the model to disk after every
#' \code{save_period} iterations; 0 means save the model at the end.
#' @param save_name the name or path for the saved model file.
#' It can contain a \code{\link[base]{sprintf}} formatting specifier
#' to include the integer iteration number in the file name.
#' E.g., with \code{save_name} = 'xgboost_%04d.model',
#' the file saved at iteration 50 would be named "xgboost_0050.model".
#'
#' @details
#' This callback function allows to save an xgb-model file, either periodically after each \code{save_period}'s or at the end.
#'
#' Callback function expects the following values to be set in its calling frame:
#' \code{bst},
#' \code{iteration},
#' \code{begin_iteration},
#' \code{end_iteration}.
#'
#' @seealso
#' \code{\link{callbacks}}
#'
#' @export
cb.save.model <- function(save_period = 0, save_name = "xgboost.model") {
if (save_period < 0)
stop("'save_period' cannot be negative")
callback <- function(env = parent.frame()) {
if (is.null(env$bst))
stop("'save_model' callback requires the 'bst' booster object in its calling frame")
if ((save_period > 0 && (env$iteration - env$begin_iteration) %% save_period == 0) ||
(save_period == 0 && env$iteration == env$end_iteration))
xgb.save(env$bst, sprintf(save_name, env$iteration))
}
attr(callback, 'call') <- match.call()
attr(callback, 'name') <- 'cb.save.model'
callback
}
#' Callback closure for returning cross-validation based predictions.
#'
#' @param save_models a flag for whether to save the folds' models.
#'
#' @details
#' This callback function saves predictions for all of the test folds,
#' and also allows to save the folds' models.
#'
#' It is a "finalizer" callback and it uses early stopping information whenever it is available,
#' thus it must be run after the early stopping callback if the early stopping is used.
#'
#' Callback function expects the following values to be set in its calling frame:
#' \code{bst_folds},
#' \code{basket},
#' \code{data},
#' \code{end_iteration},
#' \code{params},
#' \code{num_parallel_tree},
#' \code{num_class}.
#'
#' @return
#' Predictions are returned inside of the \code{pred} element, which is either a vector or a matrix,
#' depending on the number of prediction outputs per data row. The order of predictions corresponds
#' to the order of rows in the original dataset. Note that when a custom \code{folds} list is
#' provided in \code{xgb.cv}, the predictions would only be returned properly when this list is a
#' non-overlapping list of k sets of indices, as in a standard k-fold CV. The predictions would not be
#' meaningful when user-profided folds have overlapping indices as in, e.g., random sampling splits.
#' When some of the indices in the training dataset are not included into user-provided \code{folds},
#' their prediction value would be \code{NA}.
#'
#' @seealso
#' \code{\link{callbacks}}
#'
#' @export
cb.cv.predict <- function(save_models = FALSE) {
finalizer <- function(env) {
if (is.null(env$basket) || is.null(env$bst_folds))
stop("'cb.cv.predict' callback requires 'basket' and 'bst_folds' lists in its calling frame")
N <- nrow(env$data)
pred <-
if (env$num_class > 1) {
matrix(NA_real_, N, env$num_class)
} else {
rep(NA_real_, N)
}
ntreelimit <- NVL(env$basket$best_ntreelimit,
env$end_iteration * env$num_parallel_tree)
if (NVL(env$params[['booster']], '') == 'gblinear') {
ntreelimit <- 0 # must be 0 for gblinear
}
for (fd in env$bst_folds) {
pr <- predict(fd$bst, fd$watchlist[[2]], ntreelimit = ntreelimit, reshape = TRUE)
if (is.matrix(pred)) {
pred[fd$index,] <- pr
} else {
pred[fd$index] <- pr
}
}
env$basket$pred <- pred
if (save_models) {
env$basket$models <- lapply(env$bst_folds, function(fd) {
xgb.attr(fd$bst, 'niter') <- env$end_iteration - 1
xgb.Booster.complete(xgb.handleToBooster(fd$bst), saveraw = TRUE)
})
}
}
callback <- function(env = parent.frame(), finalize = FALSE) {
if (finalize)
return(finalizer(env))
}
attr(callback, 'call') <- match.call()
attr(callback, 'name') <- 'cb.cv.predict'
callback
}
#
# Internal utility functions for callbacks ------------------------------------
#
# Format the evaluation metric string
format.eval.string <- function(iter, eval_res, eval_err = NULL) {
if (length(eval_res) == 0)
stop('no evaluation results')
enames <- names(eval_res)
if (is.null(enames))
stop('evaluation results must have names')
iter <- sprintf('[%d]\t', iter)
if (!is.null(eval_err)) {
if (length(eval_res) != length(eval_err))
stop('eval_res & eval_err lengths mismatch')
res <- paste0(sprintf("%s:%f+%f", enames, eval_res, eval_err), collapse = '\t')
} else {
res <- paste0(sprintf("%s:%f", enames, eval_res), collapse = '\t')
}
return(paste0(iter, res))
}
# Extract callback names from the list of callbacks
callback.names <- function(cb_list) {
unlist(lapply(cb_list, function(x) attr(x, 'name')))
}
# Extract callback calls from the list of callbacks
callback.calls <- function(cb_list) {
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