116 lines
3.8 KiB
R
116 lines
3.8 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 inst/examples/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(agaricus.train, package='xgboost')
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#' data(agaricus.test, package='xgboost')
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#' train <- agaricus.train
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#' test <- agaricus.test
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#' bst <- xgboost(data = train$data, label = train$label, max.depth = 2,
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#' eta = 1, nround = 2,objective = "binary:logistic")
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#' pred <- predict(bst, test$data)
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#'
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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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dtrain <- xgb.get.DMatrix(data, label)
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params <- append(params, list(...))
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if (verbose > 0) {
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watchlist <- list(train = dtrain)
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} else {
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watchlist <- list()
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}
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bst <- xgb.train(params, dtrain, nrounds, watchlist, verbose=verbose)
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return(bst)
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}
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#' Training part from Mushroom Data Set
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#'
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#' This data set is originally from the Mushroom data set,
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#' UCI Machine Learning Repository.
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#'
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#' This data set includes the following fields:
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#'
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#' \itemize{
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#' \item \code{label} the label for each record
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#' \item \code{data} a sparse Matrix of \code{dgCMatrix} class, with 127 rows.
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#' }
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#'
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#'#' @references
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#' https://archive.ics.uci.edu/ml/datasets/Mushroom
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#'
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#' Bache, K. & Lichman, M. (2013). UCI Machine Learning Repository
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#' [http://archive.ics.uci.edu/ml]. Irvine, CA: University of California,
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#' School of Information and Computer Science.
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#'
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#' @docType data
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#' @keywords datasets
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#' @name agaricus.train
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#' @usage data(agaricus.train)
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#' @format A list containing a label vector, and a dgCMatrix object with 6513
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#' rows and 127 variables
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NULL
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#' Test part from Mushroom Data Set
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#'
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#' This data set is originally from the Mushroom data set,
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#' UCI Machine Learning Repository.
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#'
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#' This data set includes the following fields:
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#'
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#' \itemize{
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#' \item \code{label} the label for each record
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#' \item \code{data} a sparse Matrix of \code{dgCMatrix} class, with 127 rows.
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#' }
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#'
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#' @references
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#' https://archive.ics.uci.edu/ml/datasets/Mushroom
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#'
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#' Bache, K. & Lichman, M. (2013). UCI Machine Learning Repository
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#' [http://archive.ics.uci.edu/ml]. Irvine, CA: University of California,
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#' School of Information and Computer Science.
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#'
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#' @docType data
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#' @keywords datasets
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#' @name agaricus.test
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#' @usage data(agaricus.test)
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#' @format A list containing a label vector, and a dgCMatrix object with 1611
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#' rows and 127 variables
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NULL
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