1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
|
#' 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)
}
|
