72 lines
2.5 KiB
R
72 lines
2.5 KiB
R
#' eXtreme Gradient Boosting (Tree) library
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#'
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#' A simple interface for xgboost in R
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#'
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#' @param data takes \code{matrix}, \code{dgCMatrix}, local data file or
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#' \code{xgb.DMatrix}.
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#' @param label the response variable. User should not set this field,
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# if data is local data file or \code{xgb.DMatrix}.
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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 \url{https://github.com/tqchen/xgboost/wiki/Parameters} for
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#' further details. See also demo/demo.R for walkthrough example in R.
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#' @param nrounds the max number of iterations
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#' @param verbose If 0, xgboost will stay silent. If 1, xgboost will print
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#' information of performance. If 2, xgboost will print information of both
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#' performance and construction progress information
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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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#' This is the modeling function for xgboost.
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#'
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#' Parallelization is automatically enabled if OpenMP is present.
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#' Number of threads can also be manually specified via "nthread" parameter
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#'
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#' @examples
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#' data(iris)
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#' bst <- xgboost(as.matrix(iris[,1:4]),as.numeric(iris[,5]), nrounds = 2)
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#' pred <- predict(bst, as.matrix(iris[,1:4]))
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#' @export
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#'
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xgboost <- function(data = NULL, label = NULL, params = list(), nrounds,
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verbose = 1, ...) {
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inClass <- class(data)
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if (inClass == "dgCMatrix" || inClass == "matrix") {
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if (is.null(label))
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stop("xgboost: need label when data is a matrix")
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dtrain <- xgb.DMatrix(data, label = label)
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} else {
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if (!is.null(label))
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warning("xgboost: label will be ignored.")
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if (inClass == "character")
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dtrain <- xgb.DMatrix(data) else if (inClass == "xgb.DMatrix")
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dtrain <- data else stop("xgboost: Invalid input of data")
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}
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if (verbose > 1) {
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silent <- 0
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} else {
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silent <- 1
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}
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params <- append(params, list(silent = silent))
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params <- append(params, list(...))
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if (verbose > 0)
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watchlist <- list(train = dtrain) else watchlist <- list()
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bst <- xgb.train(params, dtrain, nrounds, watchlist)
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return(bst)
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}
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