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#' 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 environments.
#' 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$bst_evaluation, stdev)
cat(msg, '\n')
}
}
attr(callback, 'call') <- match.call()
attr(callback, 'name') <- 'cb.print.evaluation'
callback
}
#' Callback closure for logging the evaluation history
#'
#' @details
#' This callback function appends the current iteration evaluation results \code{bst_evaluation}
#' available in the calling parent frame to the \code{evaluation_log} list in a calling frame.
#'
#' The finalizer callback (called with \code{finalize = TURE} in the end) converts
#' the \code{evaluation_log} list into a final data.table.
#'
#' The iteration evaluation result \code{bst_evaluation} must be a named numeric vector.
#'
#' Note: in the column names of the final data.table, the dash '-' character is replaced with
#' the underscore '_' in order to make the column names more like regular R identifiers.
#'
#' Callback function expects the following values to be set in its calling frame:
#' \code{evaluation_log},
#' \code{bst_evaluation},
#' \code{iteration}.
#'
#' @seealso
#' \code{\link{callbacks}}
#'
#' @export
cb.evaluation.log <- function() {
mnames <- NULL
init <- function(env) {
if (!is.list(env$evaluation_log))
stop("'evaluation_log' has to be a list")
mnames <<- names(env$bst_evaluation)
if (is.null(mnames) || any(mnames == ""))
stop("bst_evaluation must have non-empty names")
mnames <<- gsub('-', '_', names(env$bst_evaluation))
if(!is.null(env$bst_evaluation_err))
mnames <<- c(paste0(mnames, '_mean'), paste0(mnames, '_std'))
}
finalizer <- function(env) {
env$evaluation_log <- as.data.table(t(simplify2array(env$evaluation_log)))
setnames(env$evaluation_log, c('iter', mnames))
if(!is.null(env$bst_evaluation_err)) {
# rearrange col order from _mean,_mean,...,_std,_std,...
# to be _mean,_std,_mean,_std,...
len <- length(mnames)
means <- mnames[seq_len(len/2)]
stds <- mnames[(len/2 + 1):len]
cnames <- numeric(len)
cnames[c(TRUE, FALSE)] <- means
cnames[c(FALSE, TRUE)] <- stds
env$evaluation_log <- env$evaluation_log[, c('iter', cnames), with = FALSE]
}
}
callback <- function(env = parent.frame(), finalize = FALSE) {
if (is.null(mnames))
init(env)
if (finalize)
return(finalizer(env))
ev <- env$bst_evaluation
if(!is.null(env$bst_evaluation_err))
ev <- c(ev, env$bst_evaluation_err)
env$evaluation_log <- c(env$evaluation_log,
list(c(iter = env$iteration, ev)))
}
attr(callback, 'call') <- match.call()
attr(callback, 'name') <- 'cb.evaluation.log'
callback
}
#' Callback closure for resetting the booster's parameters at each iteration.
#'
#' @param new_params a list where each element corresponds to a parameter that needs to be reset.
#' Each element's value must be either a vector of values of length \code{nrounds}
#' to be set at each iteration,
#' or a function of two parameters \code{learning_rates(iteration, nrounds)}
#' which returns a new parameter value by using the current iteration number
#' and the total number of boosting rounds.
#'
#' @details
#' This is a "pre-iteration" callback function used to reset booster's parameters
#' at the beginning of each iteration.
#'
#' Note that when training is resumed from some previous model, and a function is used to
#' reset a parameter value, the \code{nrounds} argument in this function would be the
#' the number of boosting rounds in the current training.
#'
#' Callback function expects the following values to be set in its calling frame:
#' \code{bst} or \code{bst_folds},
#' \code{iteration},
#' \code{begin_iteration},
#' \code{end_iteration}.
#'
#' @seealso
#' \code{\link{callbacks}}
#'
#' @export
cb.reset.parameters <- function(new_params) {
if (typeof(new_params) != "list")
stop("'new_params' must be a list")
pnames <- gsub("\\.", "_", names(new_params))
nrounds <- NULL
# run some checks in the begining
init <- function(env) {
nrounds <<- env$end_iteration - env$begin_iteration + 1
if (is.null(env$bst) && is.null(env$bst_folds))
stop("Parent frame has neither 'bst' nor 'bst_folds'")
# Some parameters are not allowed to be changed,
# since changing them would simply wreck some chaos
not_allowed <- pnames %in%
c('num_class', 'num_output_group', 'size_leaf_vector', 'updater_seq')
if (any(not_allowed))
stop('Parameters ', paste(pnames[not_allowed]), " cannot be changed during boosting.")
for (n in pnames) {
p <- new_params[[n]]
if (is.function(p)) {
if (length(formals(p)) != 2)
stop("Parameter '", n, "' is a function but not of two arguments")
} else if (is.numeric(p) || is.character(p)) {
if (length(p) != nrounds)
stop("Length of '", n, "' has to be equal to 'nrounds'")
} else {
stop("Parameter '", n, "' is not a function or a vector")
}
}
}
callback <- function(env = parent.frame()) {
if (is.null(nrounds))
init(env)
i <- env$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-provided 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$bst), saveraw = TRUE)
})
}
}
callback <- function(env = parent.frame(), finalize = FALSE) {
if (finalize)
return(finalizer(env))
}
attr(callback, 'call') <- match.call()
attr(callback, 'name') <- 'cb.cv.predict'
callback
}
#' Callback closure for collecting the model coefficients history of a gblinear booster
#' during its training.
#'
#' @param sparse when set to FALSE/TURE, a dense/sparse matrix is used to store the result.
#' Sparse format is useful when one expects only a subset of coefficients to be non-zero,
#' when using the "thrifty" feature selector with fairly small number of top features
#' selected per iteration.
#'
#' @details
#' To keep things fast and simple, gblinear booster does not internally store the history of linear
#' model coefficients at each boosting iteration. This callback provides a workaround for storing
#' the coefficients' path, by extracting them after each training iteration.
#'
#' Callback function expects the following values to be set in its calling frame:
#' \code{bst} (or \code{bst_folds}).
#'
#' @return
#' Results are stored in the \code{coefs} element of the closure.
#' The \code{\link{xgb.gblinear.history}} convenience function provides an easy way to access it.
#' With \code{xgb.train}, it is either a dense of a sparse matrix.
#' While with \code{xgb.cv}, it is a list (an element per each fold) of such matrices.
#'
#' @seealso
#' \code{\link{callbacks}}, \code{\link{xgb.gblinear.history}}.
#'
#' @examples
#' #### Binary classification:
#' #
#' # In the iris dataset, it is hard to linearly separate Versicolor class from the rest
#' # without considering the 2nd order interactions:
#' require(magrittr)
#' x <- model.matrix(Species ~ .^2, iris)[,-1]
#' colnames(x)
#' dtrain <- xgb.DMatrix(scale(x), label = 1*(iris$Species == "versicolor"))
#' param <- list(booster = "gblinear", objective = "reg:logistic", eval_metric = "auc",
#' lambda = 0.0003, alpha = 0.0003, nthread = 2)
#' # For 'shotgun', which is a default linear updater, using high eta values may result in
#' # unstable behaviour in some datasets. With this simple dataset, however, the high learning
#' # rate does not break the convergence, but allows us to illustrate the typical pattern of
#' # "stochastic explosion" behaviour of this lock-free algorithm at early boosting iterations.
#' bst <- xgb.train(param, dtrain, list(tr=dtrain), nrounds = 200, eta = 1.,
#' callbacks = list(cb.gblinear.history()))
#' # Extract the coefficients' path and plot them vs boosting iteration number:
#' coef_path <- xgb.gblinear.history(bst)
#' matplot(coef_path, type = 'l')
#'
#' # With the deterministic coordinate descent updater, it is safer to use higher learning rates.
#' # Will try the classical componentwise boosting which selects a single best feature per round:
#' bst <- xgb.train(param, dtrain, list(tr=dtrain), nrounds = 200, eta = 0.8,
#' updater = 'coord_descent', feature_selector = 'thrifty', top_k = 1,
#' callbacks = list(cb.gblinear.history()))
#' xgb.gblinear.history(bst) %>% matplot(type = 'l')
#' # Componentwise boosting is known to have similar effect to Lasso regularization.
#' # Try experimenting with various values of top_k, eta, nrounds,
#' # as well as different feature_selectors.
#'
#' # For xgb.cv:
#' bst <- xgb.cv(param, dtrain, nfold = 5, nrounds = 100, eta = 0.8,
#' callbacks = list(cb.gblinear.history()))
#' # coefficients in the CV fold #3
#' xgb.gblinear.history(bst)[[3]] %>% matplot(type = 'l')
#'
#'
#' #### Multiclass classification:
#' #
#' dtrain <- xgb.DMatrix(scale(x), label = as.numeric(iris$Species) - 1)
#' param <- list(booster = "gblinear", objective = "multi:softprob", num_class = 3,
#' lambda = 0.0003, alpha = 0.0003, nthread = 2)
#' # For the default linear updater 'shotgun' it sometimes is helpful
#' # to use smaller eta to reduce instability
#' bst <- xgb.train(param, dtrain, list(tr=dtrain), nrounds = 70, eta = 0.5,
#' callbacks = list(cb.gblinear.history()))
#' # Will plot the coefficient paths separately for each class:
#' xgb.gblinear.history(bst, class_index = 0) %>% matplot(type = 'l')
#' xgb.gblinear.history(bst, class_index = 1) %>% matplot(type = 'l')
#' xgb.gblinear.history(bst, class_index = 2) %>% matplot(type = 'l')
#'
#' # CV:
#' bst <- xgb.cv(param, dtrain, nfold = 5, nrounds = 70, eta = 0.5,
#' callbacks = list(cb.gblinear.history(FALSE)))
#' # 1st forld of 1st class
#' xgb.gblinear.history(bst, class_index = 0)[[1]] %>% matplot(type = 'l')
#'
#' @export
cb.gblinear.history <- function(sparse=FALSE) {
coefs <- NULL
init <- function(env) {
if (!is.null(env$bst)) { # xgb.train:
coef_path <- list()
} else if (!is.null(env$bst_folds)) { # xgb.cv:
coef_path <- rep(list(), length(env$bst_folds))
} else stop("Parent frame has neither 'bst' nor 'bst_folds'")
}
# convert from list to (sparse) matrix
list2mat <- function(coef_list) {
if (sparse) {
coef_mat <- sparseMatrix(x = unlist(lapply(coef_list, slot, "x")),
i = unlist(lapply(coef_list, slot, "i")),
p = c(0, cumsum(sapply(coef_list, function(x) length(x@x)))),
dims = c(length(coef_list[[1]]), length(coef_list)))
return(t(coef_mat))
} else {
return(do.call(rbind, coef_list))
}
}
finalizer <- function(env) {
if (length(coefs) == 0)
return()
if (!is.null(env$bst)) { # # xgb.train:
coefs <<- list2mat(coefs)
} else { # xgb.cv:
# first lapply transposes the list
coefs <<- lapply(seq_along(coefs[[1]]), function(i) lapply(coefs, "[[", i)) %>%
lapply(function(x) list2mat(x))
}
}
extract.coef <- function(env) {
if (!is.null(env$bst)) { # # xgb.train:
cf <- as.numeric(grep('(booster|bias|weigh)', xgb.dump(env$bst), invert = TRUE, value = TRUE))
if (sparse) cf <- as(cf, "sparseVector")
} else { # xgb.cv:
cf <- vector("list", length(env$bst_folds))
for (i in seq_along(env$bst_folds)) {
dmp <- xgb.dump(xgb.handleToBooster(env$bst_folds[[i]]$bst))
cf[[i]] <- as.numeric(grep('(booster|bias|weigh)', dmp, invert = TRUE, value = TRUE))
if (sparse) cf[[i]] <- as(cf[[i]], "sparseVector")
}
}
cf
}
callback <- function(env = parent.frame(), finalize = FALSE) {
if (is.null(coefs)) init(env)
if (finalize) return(finalizer(env))
cf <- extract.coef(env)
coefs <<- c(coefs, list(cf))
}
attr(callback, 'call') <- match.call()
attr(callback, 'name') <- 'cb.gblinear.history'
callback
}
#' Extract gblinear coefficients history.
#'
#' A helper function to extract the matrix of linear coefficients' history
#' from a gblinear model created while using the \code{cb.gblinear.history()}
#' callback.
#'
#' @param model either an \code{xgb.Booster} or a result of \code{xgb.cv()}, trained
#' using the \code{cb.gblinear.history()} callback.
#' @param class_index zero-based class index to extract the coefficients for only that
#' specific class in a multinomial multiclass model. When it is NULL, all the
#' coefficients are returned. Has no effect in non-multiclass models.
#'
#' @return
#' For an \code{xgb.train} result, a matrix (either dense or sparse) with the columns
#' corresponding to iteration's coefficients (in the order as \code{xgb.dump()} would
#' return) and the rows corresponding to boosting iterations.
#'
#' For an \code{xgb.cv} result, a list of such matrices is returned with the elements
#' corresponding to CV folds.
#'
#' @export
xgb.gblinear.history <- function(model, class_index = NULL) {
if (!(inherits(model, "xgb.Booster") ||
inherits(model, "xgb.cv.synchronous")))
stop("model must be an object of either xgb.Booster or xgb.cv.synchronous class")
is_cv <- inherits(model, "xgb.cv.synchronous")
if (is.null(model[["callbacks"]]) || is.null(model$callbacks[["cb.gblinear.history"]]))
stop("model must be trained while using the cb.gblinear.history() callback")
if (!is_cv) {
# extract num_class & num_feat from the internal model
dmp <- xgb.dump(model)
if(length(dmp) < 2 || dmp[2] != "bias:")
stop("It does not appear to be a gblinear model")
dmp <- dmp[-c(1,2)]
n <- which(dmp == 'weight:')
if(length(n) != 1)
stop("It does not appear to be a gblinear model")
num_class <- n - 1
num_feat <- (length(dmp) - 4) / num_class
} else {
# in case of CV, the object is expected to have this info
if (model$params$booster != "gblinear")
stop("It does not appear to be a gblinear model")
num_class <- NVL(model$params$num_class, 1)
num_feat <- model$nfeatures
if (is.null(num_feat))
stop("This xgb.cv result does not have nfeatures info")
}
if (!is.null(class_index) &&
num_class > 1 &&
(class_index[1] < 0 || class_index[1] >= num_class))
stop("class_index has to be within [0,", num_class - 1, "]")
coef_path <- environment(model$callbacks$cb.gblinear.history)[["coefs"]]
if (!is.null(class_index) && num_class > 1) {
coef_path <- if (is.list(coef_path)) {
lapply(coef_path,
function(x) x[, seq(1 + class_index, by=num_class, length.out=num_feat)])
} else {
coef_path <- coef_path[, seq(1 + class_index, by=num_class, length.out=num_feat)]
}
}
coef_path
}
#
# 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) {
unlist(lapply(cb_list, function(x) attr(x, 'call')))
}
# Add a callback cb to the list and make sure that
# cb.early.stop and cb.cv.predict are at the end of the list
# with cb.cv.predict being the last (when present)
add.cb <- function(cb_list, cb) {
cb_list <- c(cb_list, cb)
names(cb_list) <- callback.names(cb_list)
if ('cb.early.stop' %in% names(cb_list)) {
cb_list <- c(cb_list, cb_list['cb.early.stop'])
# this removes only the first one
cb_list['cb.early.stop'] <- NULL
}
if ('cb.cv.predict' %in% names(cb_list)) {
cb_list <- c(cb_list, cb_list['cb.cv.predict'])
cb_list['cb.cv.predict'] <- NULL
}
cb_list
}
# Sort callbacks list into categories
categorize.callbacks <- function(cb_list) {
list(
pre_iter = Filter(function(x) {
pre <- attr(x, 'is_pre_iteration')
!is.null(pre) && pre
}, cb_list),
post_iter = Filter(function(x) {
pre <- attr(x, 'is_pre_iteration')
is.null(pre) || !pre
}, cb_list),
finalize = Filter(function(x) {
'finalize' %in% names(formals(x))
}, cb_list)
)
}
# Check whether all callback functions with names given by 'query_names' are present in the 'cb_list'.
has.callbacks <- function(cb_list, query_names) {
if (length(cb_list) < length(query_names))
return(FALSE)
if (!is.list(cb_list) ||
any(sapply(cb_list, class) != 'function')) {
stop('`cb_list` must be a list of callback functions')
}
cb_names <- callback.names(cb_list)
if (!is.character(cb_names) ||
length(cb_names) != length(cb_list) ||
any(cb_names == "")) {
stop('All callbacks in the `cb_list` must have a non-empty `name` attribute')
}
if (!is.character(query_names) ||
length(query_names) == 0 ||
any(query_names == "")) {
stop('query_names must be a non-empty vector of non-empty character names')
}
return(all(query_names %in% cb_names))
}
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