387 lines
15 KiB
R
387 lines
15 KiB
R
#' Cross Validation
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#'
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#' The cross validation function of xgboost.
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#'
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#' @param params the list of parameters. The complete list of parameters is
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#' available in the \href{http://xgboost.readthedocs.io/en/latest/parameter.html}{online documentation}. Below
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#' is a shorter summary:
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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:squarederror} Regression with squared loss.
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#' \item \code{binary:logistic} logistic regression for classification.
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#' \item See \code{\link[=xgb.train]{xgb.train}()} for complete list of objectives.
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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 \code{\link{xgb.train}} for further details.
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#' See also demo/ for walkthrough example in R.
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#'
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#' Note that, while `params` accepts a `seed` entry and will use such parameter for model training if
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#' supplied, this seed is not used for creation of train-test splits, which instead rely on R's own RNG
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#' system - thus, for reproducible results, one needs to call the `set.seed` function beforehand.
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#' @param data An `xgb.DMatrix` object, with corresponding fields like `label` or bounds as required
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#' for model training by the objective.
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#'
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#' Note that only the basic `xgb.DMatrix` class is supported - variants such as `xgb.QuantileDMatrix`
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#' or `xgb.ExternalDMatrix` are not supported here.
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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 prediction A logical value indicating whether to return the test fold predictions
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#' from each CV model. This parameter engages the \code{\link{xgb.cb.cv.predict}} callback.
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#' @param showsd \code{boolean}, whether to 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{mae} Mean absolute error
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#' \item \code{mape} Mean absolute percentage error
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#' \item \code{auc} Area under curve
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#' \item \code{aucpr} Area under PR 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 customized 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 A \code{boolean} indicating whether sampling of folds should be stratified
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#' by the values of outcome labels. For real-valued labels in regression objectives,
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#' stratification will be done by discretizing the labels into up to 5 buckets beforehand.
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#'
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#' If passing "auto", will be set to `TRUE` if the objective in `params` is a classification
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#' objective (from XGBoost's built-in objectives, doesn't apply to custom ones), and to
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#' `FALSE` otherwise.
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#'
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#' This parameter is ignored when `data` has a `group` field - in such case, the splitting
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#' will be based on whole groups (note that this might make the folds have different sizes).
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#'
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#' Value `TRUE` here is \bold{not} supported for custom objectives.
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#' @param folds \code{list} provides a possibility to use a list of pre-defined CV folds
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#' (each element must be a vector of test fold's indices). When folds are supplied,
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#' the \code{nfold} and \code{stratified} parameters are ignored.
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#'
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#' If `data` has a `group` field and the objective requires this field, each fold (list element)
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#' must additionally have two attributes (retrievable through \link{attributes}) named `group_test`
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#' and `group_train`, which should hold the `group` to assign through \link{setinfo.xgb.DMatrix} to
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#' the resulting DMatrices.
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#' @param train_folds \code{list} list specifying which indicies to use for training. If \code{NULL}
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#' (the default) all indices not specified in \code{folds} will be used for training.
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#'
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#' This is not supported when `data` has `group` field.
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#' @param verbose \code{boolean}, print the statistics during the process
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#' @param print_every_n Print each n-th iteration evaluation messages when \code{verbose>0}.
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#' Default is 1 which means all messages are printed. This parameter is passed to the
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#' \code{\link{xgb.cb.print.evaluation}} callback.
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#' @param early_stopping_rounds 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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#' Setting this parameter engages the \code{\link{xgb.cb.early.stop}} callback.
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#' @param maximize If \code{feval} and \code{early_stopping_rounds} are set,
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#' then this parameter must be set as well.
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#' When it is \code{TRUE}, it means the larger the evaluation score the better.
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#' This parameter is passed to the \code{\link{xgb.cb.early.stop}} callback.
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#' @param callbacks a list of callback functions to perform various task during boosting.
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#' See \code{\link{xgb.Callback}}. Some of the callbacks are automatically created depending on the
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#' parameters' values. User can provide either existing or their own callback methods in order
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#' to customize the training process.
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#' @param ... other parameters to pass to \code{params}.
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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,
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#' 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
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#' \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{https://en.wikipedia.org/wiki/Cross-validation_\%28statistics\%29}
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#'
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#' @return
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#' An object of class \code{xgb.cv.synchronous} with the following elements:
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#' \itemize{
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#' \item \code{call} a function call.
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#' \item \code{params} parameters that were passed to the xgboost library. Note that it does not
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#' capture parameters changed by the \code{\link{xgb.cb.reset.parameters}} callback.
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#' \item \code{evaluation_log} evaluation history stored as a \code{data.table} with the
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#' first column corresponding to iteration number and the rest corresponding to the
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#' CV-based evaluation means and standard deviations for the training and test CV-sets.
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#' It is created by the \code{\link{xgb.cb.evaluation.log}} callback.
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#' \item \code{niter} number of boosting iterations.
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#' \item \code{nfeatures} number of features in training data.
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#' \item \code{folds} the list of CV folds' indices - either those passed through the \code{folds}
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#' parameter or randomly generated.
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#' \item \code{best_iteration} iteration number with the best evaluation metric value
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#' (only available with early stopping).
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#' }
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#'
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#' Plus other potential elements that are the result of callbacks, such as a list `cv_predict` with
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#' a sub-element `pred` when passing `prediction = TRUE`, which is added by the \link{xgb.cb.cv.predict}
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#' callback (note that one can also pass it manually under `callbacks` with different settings,
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#' such as saving also the models created during cross validation); or a list `early_stop` which
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#' will contain elements such as `best_iteration` when using the early stopping callback (\link{xgb.cb.early.stop}).
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#'
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#' @examples
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#' data(agaricus.train, package='xgboost')
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#' dtrain <- with(agaricus.train, xgb.DMatrix(data, label = label, nthread = 2))
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#' cv <- xgb.cv(data = dtrain, nrounds = 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(cv)
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#' print(cv, verbose=TRUE)
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#'
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#' @export
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xgb.cv <- function(params = list(), data, nrounds, nfold,
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prediction = FALSE, showsd = TRUE, metrics = list(),
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obj = NULL, feval = NULL, stratified = "auto", folds = NULL, train_folds = NULL,
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verbose = TRUE, print_every_n = 1L,
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early_stopping_rounds = NULL, maximize = NULL, callbacks = list(), ...) {
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check.deprecation(...)
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stopifnot(inherits(data, "xgb.DMatrix"))
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if (inherits(data, "xgb.DMatrix") && .Call(XGCheckNullPtr_R, data)) {
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stop("'data' is an invalid 'xgb.DMatrix' object. Must be constructed again.")
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}
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params <- check.booster.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 (stratified == "auto") {
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if (is.character(params$objective)) {
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stratified <- (
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(params$objective %in% .CLASSIFICATION_OBJECTIVES())
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&& !(params$objective %in% .RANKING_OBJECTIVES())
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)
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} else {
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stratified <- FALSE
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}
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}
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# Check the labels and groups
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cv_label <- getinfo(data, "label")
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cv_group <- getinfo(data, "group")
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if (!is.null(train_folds) && NROW(cv_group)) {
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stop("'train_folds' is not supported for DMatrix object with 'group' field.")
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}
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# CV folds
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if (!is.null(folds)) {
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if (!is.list(folds) || length(folds) < 2)
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stop("'folds' must be a list with 2 or more elements that are vectors of indices for each CV-fold")
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nfold <- length(folds)
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} else {
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if (nfold <= 1)
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stop("'nfold' must be > 1")
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folds <- generate.cv.folds(nfold, nrow(data), stratified, cv_label, cv_group, params)
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}
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# Callbacks
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tmp <- .process.callbacks(callbacks, is_cv = TRUE)
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callbacks <- tmp$callbacks
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cb_names <- tmp$cb_names
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rm(tmp)
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# Early stopping callback
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if (!is.null(early_stopping_rounds) && !("early_stop" %in% cb_names)) {
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callbacks <- add.callback(
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callbacks,
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xgb.cb.early.stop(
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early_stopping_rounds,
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maximize = maximize,
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verbose = verbose
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),
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as_first_elt = TRUE
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)
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}
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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 (verbose && !("print_evaluation" %in% cb_names)) {
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callbacks <- add.callback(callbacks, xgb.cb.print.evaluation(print_every_n, showsd = showsd))
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}
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# evaluation log callback: always is on in CV
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if (!("evaluation_log" %in% cb_names)) {
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callbacks <- add.callback(callbacks, xgb.cb.evaluation.log())
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}
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# CV-predictions callback
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if (prediction && !("cv_predict" %in% cb_names)) {
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callbacks <- add.callback(callbacks, xgb.cb.cv.predict(save_models = FALSE))
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}
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# create the booster-folds
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# train_folds
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dall <- data
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bst_folds <- lapply(seq_along(folds), function(k) {
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dtest <- xgb.slice.DMatrix(dall, folds[[k]], allow_groups = TRUE)
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# code originally contributed by @RolandASc on stackoverflow
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if (is.null(train_folds))
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dtrain <- xgb.slice.DMatrix(dall, unlist(folds[-k]), allow_groups = TRUE)
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else
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dtrain <- xgb.slice.DMatrix(dall, train_folds[[k]], allow_groups = TRUE)
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if (!is.null(attributes(folds[[k]])$group_test)) {
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setinfo(dtest, "group", attributes(folds[[k]])$group_test)
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setinfo(dtrain, "group", attributes(folds[[k]])$group_train)
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}
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bst <- xgb.Booster(
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params = params,
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cachelist = list(dtrain, dtest),
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modelfile = NULL
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)
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bst <- bst$bst
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list(dtrain = dtrain, bst = bst, evals = list(train = dtrain, test = dtest), index = folds[[k]])
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})
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# extract parameters that can affect the relationship b/w #trees and #iterations
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num_class <- max(as.numeric(NVL(params[['num_class']], 1)), 1) # nolint
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# those are fixed for CV (no training continuation)
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begin_iteration <- 1
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end_iteration <- nrounds
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.execute.cb.before.training(
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callbacks,
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bst_folds,
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dall,
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NULL,
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begin_iteration,
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end_iteration
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)
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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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.execute.cb.before.iter(
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callbacks,
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bst_folds,
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dall,
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NULL,
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iteration
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)
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msg <- lapply(bst_folds, function(fd) {
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xgb.iter.update(
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bst = fd$bst,
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dtrain = fd$dtrain,
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iter = iteration - 1,
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obj = obj
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)
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xgb.iter.eval(
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bst = fd$bst,
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evals = fd$evals,
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iter = iteration - 1,
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feval = feval
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)
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})
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msg <- simplify2array(msg)
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should_stop <- .execute.cb.after.iter(
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callbacks,
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bst_folds,
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dall,
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NULL,
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iteration,
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msg
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)
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if (should_stop) break
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}
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cb_outputs <- .execute.cb.after.training(
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callbacks,
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bst_folds,
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dall,
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NULL,
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iteration,
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msg
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)
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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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niter = iteration,
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nfeatures = ncol(dall),
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folds = folds
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)
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ret <- c(ret, cb_outputs)
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class(ret) <- 'xgb.cv.synchronous'
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return(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 <- xgb.cv(data = xgb.DMatrix(train$data, label = train$label), nfold = 5, max_depth = 2,
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#' eta = 1, nthread = 2, nrounds = 2, objective = "binary:logistic")
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#' print(cv)
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#' print(cv, verbose=TRUE)
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#'
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#' @rdname print.xgb.cv
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#' @method print xgb.cv.synchronous
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#' @export
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print.xgb.cv.synchronous <- function(x, verbose = FALSE, ...) {
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cat('##### xgb.cv ', length(x$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$params), '"'),
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sep = ' = ', collapse = ', '), '\n', sep = '')
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}
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for (n in c('niter', 'best_iteration')) {
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if (is.null(x$early_stop[[n]]))
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next
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cat(n, ': ', x$early_stop[[n]], '\n', sep = '')
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}
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if (!is.null(x$cv_predict$pred)) {
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cat('pred:\n')
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str(x$cv_predict$pred)
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}
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}
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if (verbose)
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cat('evaluation_log:\n')
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print(x$evaluation_log, row.names = FALSE, ...)
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if (!is.null(x$early_stop$best_iteration)) {
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cat('Best iteration:\n')
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print(x$evaluation_log[x$early_stop$best_iteration], row.names = FALSE, ...)
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}
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invisible(x)
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}
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