Restore attributes in complete.

R-package/R/xgb.Booster.R

@@ -131,6 +131,25 @@ xgb.Booster.complete <- function(object, saveraw = TRUE) {
       object$raw <- xgb.serialize(object$handle)
     }
   }
+
+  attrs <- xgb.attributes(object)
+  if (!is.null(attrs$best_ntreelimit)) {
+    object$best_ntreelimit <- as.integer(attrs$best_ntreelimit)
+  }
+  if (!is.null(attrs$best_iteration)) {
+    ## Convert from 0 based back to 1 based.
+    object$best_iteration <- as.integer(attrs$best_iteration) + 1
+  }
+  if (!is.null(attrs$best_score)) {
+    object$best_score <- as.numeric(attrs$best_score)
+  }
+  if (!is.null(attrs$best_msg)) {
+    object$best_msg <- attrs$best_msg
+  }
+  if (!is.null(attrs$niter)) {
+    object$niter <- as.integer(attrs$niter)
+  }
+
   return(object)
 }
 

R-package/R/xgb.load.R

@@ -1,30 +1,30 @@
 #' Load xgboost model from binary file
-#' 
-#' Load xgboost model from the binary model file. 
-#' 
+#'
+#' Load xgboost model from the binary model file.
+#'
 #' @param modelfile the name of the binary input file.
-#' 
-#' @details 
+#'
+#' @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 
+#' 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 
+#'
+#' @return
 #' An object of \code{xgb.Booster} class.
-#' 
-#' @seealso 
-#' \code{\link{xgb.save}}, \code{\link{xgb.Booster.complete}}. 
-#' 
+#'
+#' @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, 
+#' 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')

R-package/tests/testthat/test_callbacks.R

@@ -207,7 +207,7 @@ test_that("early stopping xgb.train works", {
                      early_stopping_rounds = 3, maximize = FALSE)
   , "Stopping. Best iteration")
   expect_false(is.null(bst$best_iteration))
-  expect_lt(bst$best_iteration, 19)
+  expect_lt(bst$best_iteration, 4)
   expect_equal(bst$best_iteration, bst$best_ntreelimit)
 
   pred <- predict(bst, dtest)
@@ -222,6 +222,16 @@ test_that("early stopping xgb.train works", {
                       early_stopping_rounds = 3, maximize = FALSE, verbose = 0)
   )
   expect_equal(bst$evaluation_log, bst0$evaluation_log)
+
+  xgb.save(bst, "model.bin")
+  loaded <- xgb.load("model.bin")
+
+  print('Start expecting something.')
+  expect_false(is.null(loaded$best_iteration))
+  expect_equal(loaded$best_iteration, bst$best_ntreelimit)
+  expect_equal(loaded$best_ntreelimit, bst$best_ntreelimit)
+
+  file.remove("model.bin")
 })
 
 test_that("early stopping using a specific metric works", {