289 lines
11 KiB
R
289 lines
11 KiB
R
#' Cross Validation
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#'
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#' The cross valudation function of xgboost
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#'
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#' @param params the list of parameters. Commonly used ones are:
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#' \itemize{
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#' \item \code{objective} objective function, common ones are
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#' \itemize{
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#' \item \code{reg:linear} linear regression
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#' \item \code{binary:logistic} logistic regression for classification
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#' }
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#' \item \code{eta} step size of each boosting step
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#' \item \code{max.depth} maximum depth of the tree
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#' \item \code{nthread} number of thread used in training, if not set, all threads are used
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#' }
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#'
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#' See \link{xgb.train} for further details.
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#' See also demo/ for walkthrough example in R.
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#' @param data takes an \code{xgb.DMatrix} or \code{Matrix} as the input.
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#' @param nrounds the max number of iterations
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#' @param nfold the original dataset is randomly partitioned into \code{nfold} equal size subsamples.
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#' @param label option field, when data is \code{Matrix}
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#' @param missing Missing is only used when input is dense matrix, pick a float
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#' value that represents missing value. Sometime a data use 0 or other extreme value to represents missing values.
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#' @param prediction A logical value indicating whether to return the prediction vector.
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#' @param showsd \code{boolean}, whether show standard deviation of cross validation
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#' @param metrics, list of evaluation metrics to be used in cross validation,
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#' when it is not specified, the evaluation metric is chosen according to objective function.
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#' Possible options are:
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#' \itemize{
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#' \item \code{error} binary classification error rate
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#' \item \code{rmse} Rooted mean square error
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#' \item \code{logloss} negative log-likelihood function
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#' \item \code{auc} Area under curve
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#' \item \code{merror} Exact matching error, used to evaluate multi-class classification
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#' }
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#' @param obj customized objective function. Returns gradient and second order
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#' gradient with given prediction and dtrain.
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#' @param feval custimized evaluation function. Returns
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#' \code{list(metric='metric-name', value='metric-value')} with given
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#' prediction and dtrain.
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#' @param stratified \code{boolean} whether sampling of folds should be stratified by the values of labels in \code{data}
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#' @param folds \code{list} provides a possibility of using a list of pre-defined CV folds (each element must be a vector of fold's indices).
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#' If folds are supplied, the nfold and stratified parameters would be ignored.
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#' @param verbose \code{boolean}, print the statistics during the process
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#' @param print.every.n Print every N progress messages when \code{verbose>0}. Default is 1 which means all messages are printed.
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#' @param early.stop.round If \code{NULL}, the early stopping function is not triggered.
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#' If set to an integer \code{k}, training with a validation set will stop if the performance
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#' doesn't improve for \code{k} rounds.
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#' @param maximize If \code{feval} and \code{early.stop.round} are set, then \code{maximize} must be set as well.
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#' \code{maximize=TRUE} means the larger the evaluation score the better.
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#'
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#' @param ... other parameters to pass to \code{params}.
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#'
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#' @return
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#' TODO: update this...
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#'
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#' If \code{prediction = TRUE}, a list with the following elements is returned:
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#' \itemize{
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#' \item \code{dt} a \code{data.table} with each mean and standard deviation stat for training set and test set
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#' \item \code{pred} an array or matrix (for multiclass classification) with predictions for each CV-fold for the model having been trained on the data in all other folds.
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#' }
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#'
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#' If \code{prediction = FALSE}, just a \code{data.table} with each mean and standard deviation stat for training set and test set is returned.
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#'
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#' @details
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#' The original sample is randomly partitioned into \code{nfold} equal size subsamples.
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#'
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#' Of the \code{nfold} subsamples, a single subsample is retained as the validation data for testing the model, and the remaining \code{nfold - 1} subsamples are used as training data.
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#'
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#' The cross-validation process is then repeated \code{nrounds} times, with each of the \code{nfold} subsamples used exactly once as the validation data.
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#'
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#' All observations are used for both training and validation.
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#'
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#' Adapted from \url{http://en.wikipedia.org/wiki/Cross-validation_\%28statistics\%29#k-fold_cross-validation}
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#'
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#' @examples
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#' data(agaricus.train, package='xgboost')
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#' dtrain <- xgb.DMatrix(agaricus.train$data, label = agaricus.train$label)
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#' history <- xgb.cv(data = dtrain, nround=3, nthread = 2, nfold = 5, metrics=list("rmse","auc"),
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#' max.depth =3, eta = 1, objective = "binary:logistic")
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#' print(history)
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#'
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#' @export
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xgb.cv <- function(params=list(), data, nrounds, nfold, label = NULL, missing = NA,
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prediction = FALSE, showsd = TRUE, metrics=list(),
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obj = NULL, feval = NULL, stratified = TRUE, folds = NULL,
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verbose = TRUE, print.every.n=1L,
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early.stop.round = NULL, maximize = NULL, callbacks = list(), ...) {
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#strategy <- match.arg(strategy)
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params <- check.params(params, ...)
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# TODO: should we deprecate the redundant 'metrics' parameter?
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for (m in metrics)
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params <- c(params, list("eval_metric" = m))
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check.custom.obj()
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check.custom.eval()
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#if (is.null(params[['eval_metric']]) && is.null(feval))
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# stop("Either 'eval_metric' or 'feval' must be provided for CV")
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# Labels
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if (class(data) == 'xgb.DMatrix')
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labels <- getinfo(data, 'label')
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if (is.null(labels))
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stop("Labels must be provided for CV either through xgb.DMatrix, or through 'label=' when 'data' is matrix")
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# CV folds
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if(!is.null(folds)) {
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if(class(folds) != "list" || length(folds) < 2)
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stop("'folds' must be a list with 2 or more elements that are vectors of indices for each CV-fold")
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nfold <- length(folds)
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} else {
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if (nfold <= 1)
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stop("'nfold' must be > 1")
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folds <- generate.cv.folds(nfold, nrow(data), stratified, label, params)
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}
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# Potential TODO: sequential CV
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#if (strategy == 'sequential')
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# stop('Sequential CV strategy is not yet implemented')
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# verbosity & evaluation printing callback:
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params <- c(params, list(silent = 1))
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print.every.n <- max( as.integer(print.every.n), 1L)
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if (!has.callbacks(callbacks, 'cb.print_evaluation') && verbose)
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callbacks <- c(callbacks, cb.print_evaluation(print.every.n))
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# evaluation log callback: always is on in CV
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evaluation_log <- list()
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if (!has.callbacks(callbacks, 'cb.log_evaluation'))
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callbacks <- c(callbacks, cb.log_evaluation())
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# Early stopping callback
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stop_condition <- FALSE
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if (!is.null(early.stop.round) &&
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!has.callbacks(callbacks, 'cb.early_stop'))
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callbacks <- c(callbacks, cb.early_stop(early.stop.round, maximize=maximize, verbose=verbose))
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# Sort the callbacks into categories
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names(callbacks) <- callback.names(callbacks)
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cb <- categorize.callbacks(callbacks)
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# create the booster-folds
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dall <- xgb.get.DMatrix(data, label, missing)
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bst_folds <- lapply(1:length(folds), function(k) {
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dtest <- slice(dall, folds[[k]])
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dtrain <- slice(dall, unlist(folds[-k]))
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bst <- xgb.Booster(params, list(dtrain, dtest))
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list(dtrain=dtrain, bst=bst, watchlist=list(train=dtrain, test=dtest), index=folds[[k]])
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})
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num_class <- max(as.numeric(NVL(params[['num_class']], 1)), 1)
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num_parallel_tree <- max(as.numeric(NVL(params[['num_parallel_tree']], 1)), 1)
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begin_iteration <- 1
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end_iteration <- nrounds
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# synchronous CV boosting: run CV folds' models within each iteration
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for (iteration in begin_iteration:end_iteration) {
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for (f in cb$pre_iter) f()
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msg <- lapply(bst_folds, function(fd) {
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xgb.iter.update(fd$bst, fd$dtrain, iteration - 1, obj)
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xgb.iter.eval(fd$bst, fd$watchlist, iteration - 1, feval)
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})
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msg <- simplify2array(msg)
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bst_evaluation <- rowMeans(msg)
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bst_evaluation_err <- sqrt(rowMeans(msg^2) - bst_evaluation^2)
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for (f in cb$post_iter) f()
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if (stop_condition) break
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}
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for (f in cb$finalize) f(finalize=TRUE)
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# the CV result
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ret <- list(
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call = match.call(),
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params = params,
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callbacks = callbacks,
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evaluation_log = evaluation_log,
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nboost = end_iteration,
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ntree = end_iteration * num_parallel_tree * num_class
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)
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if (!is.null(attr(bst_folds, 'best_iteration'))) {
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ret$best_iteration <- attr(bst_folds, 'best_iteration')
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ret$best_ntreelimit <- attr(bst_folds, 'best_ntreelimit')
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}
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ret$folds <- folds
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# TODO: should making prediction go
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# a. into a callback?
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# b. return folds' models, and have a separate method for predictions?
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if (prediction) {
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ret$pred <- ifelse(num_class > 1,
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matrix(0, nrow(data), num_class),
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rep(0, nrow(data)))
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ntreelimit <- NVL(ret$best_ntreelimit, ret$ntree)
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for (fd in bst_folds) {
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pred <- predict(fd$bst, fd$watchlist[[2]], ntreelimit = ntreelimit)
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if (is.matrix(ret$pred))
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ret$pred[fd$index,] <- t(matrix(pred, num_class, length(fd$index)))
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else
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ret$pred[fd$index] <- pred
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}
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ret$bst <- lapply(bst_folds, function(x) {
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xgb.Booster.check(xgb.handleToBooster(x$bst), saveraw = TRUE)
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})
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}
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class(ret) <- 'xgb.cv.synchronous'
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invisible(ret)
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}
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#' Print xgb.cv result
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#'
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#' Prints formatted results of \code{xgb.cv}.
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#'
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#' @param x an \code{xgb.cv.synchronous} object
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#' @param verbose whether to print detailed data
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#' @param ... passed to \code{data.table.print}
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#'
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#' @details
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#' When not verbose, it would only print the evaluation results,
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#' including the best iteration (when available).
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#'
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#' @examples
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#' data(agaricus.train, package='xgboost')
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#' train <- agaricus.train
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#' cv <- xgbcv(data = train$data, label = train$label, max.depth = 2,
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#' eta = 1, nthread = 2, nround = 2, objective = "binary:logistic")
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#' print(cv)
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#' print(cv, verbose=TRUE)
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#'
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#' @rdname print.xgb.cv
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#' @export
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print.xgb.cv.synchronous <- function(x, verbose=FALSE, ...) {
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cat('##### xgb.cv ', length(x$folds), '-folds\n', sep='')
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if (verbose) {
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if (!is.null(x$call)) {
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cat('call:\n ')
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print(x$call)
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}
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if (!is.null(x$params)) {
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cat('params (as set within xgb.cv):\n')
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cat( ' ',
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paste(names(x$params),
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paste0('"', unlist(x$params), '"'),
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sep=' = ', collapse=', '), '\n', sep='')
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}
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if (!is.null(x$callbacks) && length(x$callbacks) > 0) {
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cat('callbacks:\n')
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lapply(callback.calls(x$callbacks), function(x) {
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cat(' ')
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print(x)
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})
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}
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for (n in c('nboost', 'ntree', 'best_iteration', 'best_ntreelimit')) {
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if (is.null(x[[n]]))
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next
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cat(n, ': ', x[[n]], '\n', sep='')
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}
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cat('nfolds: ', length(x$folds), '\n', sep='')
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if (!is.null(x$pred)) {
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cat('pred:\n')
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str(x$pred)
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}
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}
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if (verbose)
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cat('evaluation_log:\n')
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print(x$evaluation_log, row.names = FALSE, ...)
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if (!is.null(x$best_iteration)) {
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cat('Best iteration:\n')
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print(x$evaluation_log[x$best_iteration], row.names = FALSE, ...)
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}
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invisible(x)
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}
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