48 lines
1.6 KiB
R
48 lines
1.6 KiB
R
#' Load xgboost model from binary file
|
|
#'
|
|
#' Load xgboost model from the binary model file.
|
|
#'
|
|
#' @param modelfile the name of the binary input file.
|
|
#'
|
|
#' @details
|
|
#' The input file is expected to contain a model saved in an xgboost-internal binary format
|
|
#' using either \code{\link{xgb.save}} or \code{\link{cb.save.model}} in R, or using some
|
|
#' appropriate methods from other xgboost interfaces. E.g., a model trained in Python and
|
|
#' saved from there in xgboost format, could be loaded from R.
|
|
#'
|
|
#' Note: a model saved as an R-object, has to be loaded using corresponding R-methods,
|
|
#' not \code{xgb.load}.
|
|
#'
|
|
#' @return
|
|
#' An object of \code{xgb.Booster} class.
|
|
#'
|
|
#' @seealso
|
|
#' \code{\link{xgb.save}}, \code{\link{xgb.Booster.complete}}.
|
|
#'
|
|
#' @examples
|
|
#' data(agaricus.train, package='xgboost')
|
|
#' data(agaricus.test, package='xgboost')
|
|
#' train <- agaricus.train
|
|
#' test <- agaricus.test
|
|
#' bst <- xgboost(data = train$data, label = train$label, max_depth = 2,
|
|
#' eta = 1, nthread = 2, nrounds = 2,objective = "binary:logistic")
|
|
#' xgb.save(bst, 'xgb.model')
|
|
#' bst <- xgb.load('xgb.model')
|
|
#' if (file.exists('xgb.model')) file.remove('xgb.model')
|
|
#' pred <- predict(bst, test$data)
|
|
#' @export
|
|
xgb.load <- function(modelfile) {
|
|
if (is.null(modelfile))
|
|
stop("xgb.load: modelfile cannot be NULL")
|
|
|
|
handle <- xgb.Booster.handle(modelfile = modelfile)
|
|
# re-use modelfile if it is raw so we do not need to serialize
|
|
if (typeof(modelfile) == "raw") {
|
|
bst <- xgb.handleToBooster(handle, modelfile)
|
|
} else {
|
|
bst <- xgb.handleToBooster(handle, NULL)
|
|
}
|
|
bst <- xgb.Booster.complete(bst, saveraw = TRUE)
|
|
return(bst)
|
|
}
|