251 lines
11 KiB
R
251 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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#' @importFrom data.table data.table
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#' @importFrom data.table as.data.table
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#' @importFrom magrittr %>%
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#' @importFrom data.table :=
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#' @importFrom data.table rbindlist
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#' @importFrom stringr str_extract_all
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#' @importFrom stringr str_extract
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#' @importFrom stringr str_split
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#' @importFrom stringr str_replace
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#' @importFrom stringr str_match
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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 corss 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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#' keeps getting worse consecutively 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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#' 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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#' @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, verbose = T, print.every.n=1L,
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early.stop.round = NULL, maximize = NULL, ...) {
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if (typeof(params) != "list") {
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stop("xgb.cv: first argument params must be list")
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}
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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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}
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nfold <- length(folds)
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}
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if (nfold <= 1) {
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stop("nfold must be bigger than 1")
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}
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dtrain <- xgb.get.DMatrix(data, label, missing)
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dot.params <- list(...)
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nms.params <- names(params)
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nms.dot.params <- names(dot.params)
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if (length(intersect(nms.params,nms.dot.params)) > 0)
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stop("Duplicated defined term in parameters. Please check your list of params.")
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params <- append(params, dot.params)
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params <- append(params, list(silent=1))
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for (mc in metrics) {
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params <- append(params, list("eval_metric"=mc))
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}
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# customized objective and evaluation metric interface
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if (!is.null(params$objective) && !is.null(obj))
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stop("xgb.cv: cannot assign two different objectives")
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if (!is.null(params$objective))
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if (class(params$objective) == 'function') {
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obj <- params$objective
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params[['objective']] <- NULL
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}
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# if (!is.null(params$eval_metric) && !is.null(feval))
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# stop("xgb.cv: cannot assign two different evaluation metrics")
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if (!is.null(params$eval_metric))
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if (class(params$eval_metric) == 'function') {
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feval <- params$eval_metric
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params[['eval_metric']] <- NULL
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}
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# Early Stopping
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if (!is.null(early.stop.round)){
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if (!is.null(feval) && is.null(maximize))
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stop('Please set maximize to note whether the model is maximizing the evaluation or not.')
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if (is.null(maximize) && is.null(params$eval_metric))
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stop('Please set maximize to note whether the model is maximizing the evaluation or not.')
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if (is.null(maximize))
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{
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if (params$eval_metric %in% c('rmse','logloss','error','merror','mlogloss')) {
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maximize <- FALSE
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} else {
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maximize <- TRUE
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}
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}
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if (maximize) {
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bestScore <- 0
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} else {
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bestScore <- Inf
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}
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bestInd <- 0
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earlyStopflag <- FALSE
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if (length(metrics) > 1)
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warning('Only the first metric is used for early stopping process.')
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}
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xgb_folds <- xgb.cv.mknfold(dtrain, nfold, params, stratified, folds)
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obj_type <- params[['objective']]
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mat_pred <- FALSE
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if (!is.null(obj_type) && obj_type == 'multi:softprob')
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{
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num_class <- params[['num_class']]
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if (is.null(num_class))
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stop('must set num_class to use softmax')
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predictValues <- matrix(0, nrow(dtrain), num_class)
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mat_pred <- TRUE
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}
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else
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predictValues <- rep(0, nrow(dtrain))
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history <- c()
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print.every.n <- max(as.integer(print.every.n), 1L)
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for (i in 1:nrounds) {
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msg <- list()
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for (k in 1:nfold) {
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fd <- xgb_folds[[k]]
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succ <- xgb.iter.update(fd$booster, fd$dtrain, i - 1, obj)
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msg[[k]] <- xgb.iter.eval(fd$booster, fd$watchlist, i - 1, feval) %>% str_split("\t") %>% .[[1]]
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}
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ret <- xgb.cv.aggcv(msg, showsd)
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history <- c(history, ret)
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if(verbose)
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if (0 == (i - 1L) %% print.every.n)
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cat(ret, "\n", sep="")
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# early_Stopping
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if (!is.null(early.stop.round)){
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score <- strsplit(ret,'\\s+')[[1]][1 + length(metrics) + 2]
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score <- strsplit(score,'\\+|:')[[1]][[2]]
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score <- as.numeric(score)
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if ( (maximize && score > bestScore) || (!maximize && score < bestScore)) {
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bestScore <- score
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bestInd <- i
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} else {
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if (i - bestInd >= early.stop.round) {
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earlyStopflag <- TRUE
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cat('Stopping. Best iteration:', bestInd, '\n')
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break
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}
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}
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}
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}
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if (prediction) {
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for (k in 1:nfold) {
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fd <- xgb_folds[[k]]
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if (!is.null(early.stop.round) && earlyStopflag) {
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res <- xgb.iter.eval(fd$booster, fd$watchlist, bestInd - 1, feval, prediction)
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} else {
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res <- xgb.iter.eval(fd$booster, fd$watchlist, nrounds - 1, feval, prediction)
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}
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if (mat_pred) {
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pred_mat <- matrix(res[[2]],num_class,length(fd$index))
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predictValues[fd$index,] <- t(pred_mat)
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} else {
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predictValues[fd$index] <- res[[2]]
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}
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}
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}
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colnames <- str_split(string = history[1], pattern = "\t")[[1]] %>% .[2:length(.)] %>% str_extract(".*:") %>% str_replace(":","") %>% str_replace("-", ".")
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colnamesMean <- paste(colnames, "mean")
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if(showsd) colnamesStd <- paste(colnames, "std")
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colnames <- c()
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if(showsd) for(i in 1:length(colnamesMean)) colnames <- c(colnames, colnamesMean[i], colnamesStd[i])
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else colnames <- colnamesMean
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type <- rep(x = "numeric", times = length(colnames))
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dt <- utils::read.table(text = "", colClasses = type, col.names = colnames) %>% as.data.table
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split <- str_split(string = history, pattern = "\t")
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for(line in split) dt <- line[2:length(line)] %>% str_extract_all(pattern = "\\d*\\.+\\d*") %>% unlist %>% as.numeric %>% as.list %>% {rbindlist( list( dt, .), use.names = F, fill = F)}
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if (prediction) {
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return( list( dt = dt,pred = predictValues))
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}
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return(dt)
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}
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# Avoid error messages during CRAN check.
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# The reason is that these variables are never declared
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# They are mainly column names inferred by Data.table...
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globalVariables(".")
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