2b5b96d760
* [R] xgb.save must work when handle in nil but raw exists * [R] print.xgb.Booster should still print other info when handle is nil * [R] rename internal function xgb.Booster to xgb.Booster.handle to make its intent clear * [R] rename xgb.Booster.check to xgb.Booster.complete and make it visible; more docs * [R] storing evaluation_log should depend only on watchlist, not on verbose * [R] reduce the excessive chattiness of unit tests * [R] only disable some tests in windows when it's not 64-bit * [R] clean-up xgb.DMatrix * [R] test xgb.DMatrix loading from libsvm text file * [R] store feature_names in xgb.Booster, use them from utility functions * [R] remove non-functional co-occurence computation from xgb.importance * [R] verbose=0 is enough without a callback * [R] added forgotten xgb.Booster.complete.Rd; cran check fixes * [R] update installation instructions
42 lines
1.6 KiB
R
42 lines
1.6 KiB
R
#' Save xgboost model to binary file
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#'
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#' Save xgboost model to a file in binary format.
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#'
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#' @param model model object of \code{xgb.Booster} class.
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#' @param fname name of the file to write.
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#'
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#' @details
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#' This methods allows to save a model in an xgboost-internal binary format which is universal
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#' among the various xgboost interfaces. In R, the saved model file could be read-in later
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#' using either the \code{\link{xgb.load}} function or the \code{xgb_model} parameter
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#' of \code{\link{xgb.train}}.
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#'
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#' Note: a model can also be saved as an R-object (e.g., by using \code{\link[base]{readRDS}}
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#' or \code{\link[base]{save}}). However, it would then only be compatible with R, and
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#' corresponding R-methods would need to be used to load it.
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#'
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#' @seealso
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#' \code{\link{xgb.load}}, \code{\link{xgb.Booster.complete}}.
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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, nthread = 2, nrounds = 2,objective = "binary:logistic")
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#' xgb.save(bst, 'xgb.model')
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#' bst <- xgb.load('xgb.model')
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#' pred <- predict(bst, test$data)
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#' @export
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xgb.save <- function(model, fname) {
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if (typeof(fname) != "character")
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stop("fname must be character")
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if (class(model) != "xgb.Booster")
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stop("the input must be xgb.Booster. Use xgb.DMatrix.save to save xgb.DMatrix object.")
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model <- xgb.Booster.complete(model, saveraw = FALSE)
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.Call("XGBoosterSaveModel_R", model$handle, fname, PACKAGE = "xgboost")
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return(TRUE)
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}
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