[R] R raw serialization. (#5123)

* Add bindings for serialization.
* Change `xgb.save.raw' into full serialization instead of simple model.
* Add `xgb.load.raw' for unserialization.
* Run devtools.

R-package/CMakeLists.txt

@@ -32,3 +32,7 @@ set_target_properties(
 set(XGBOOST_DEFINITIONS "${XGBOOST_DEFINITIONS};${R_DEFINITIONS}" PARENT_SCOPE)
 set(XGBOOST_OBJ_SOURCES $<TARGET_OBJECTS:xgboost-r> PARENT_SCOPE)
 set(LINKED_LIBRARIES_PRIVATE ${LINKED_LIBRARIES_PRIVATE} ${LIBR_CORE_LIBRARY} PARENT_SCOPE)
+
+if (USE_OPENMP)
+  target_link_libraries(xgboost-r PRIVATE OpenMP::OpenMP_CXX)
+endif ()

R-package/DESCRIPTION

@@ -63,5 +63,5 @@ Imports:
     data.table (>= 1.9.6),
     magrittr (>= 1.5),
     stringi (>= 0.5.2)
-RoxygenNote: 7.0.2
+RoxygenNote: 7.1.0
 SystemRequirements: GNU make, C++11

R-package/NAMESPACE

@@ -40,6 +40,7 @@ export(xgb.ggplot.deepness)
 export(xgb.ggplot.importance)
 export(xgb.importance)
 export(xgb.load)
+export(xgb.load.raw)
 export(xgb.model.dt.tree)
 export(xgb.plot.deepness)
 export(xgb.plot.importance)
@@ -48,7 +49,9 @@ export(xgb.plot.shap)
 export(xgb.plot.tree)
 export(xgb.save)
 export(xgb.save.raw)
+export(xgb.serialize)
 export(xgb.train)
+export(xgb.unserialize)
 export(xgboost)
 import(methods)
 importClassesFrom(Matrix,dgCMatrix)

R-package/R/xgb.Booster.R

@@ -5,20 +5,34 @@ xgb.Booster.handle <- function(params = list(), cachelist = list(), modelfile =
       !all(vapply(cachelist, inherits, logical(1), what = 'xgb.DMatrix'))) {
     stop("cachelist must be a list of xgb.DMatrix objects")
   }
-
-  handle <- .Call(XGBoosterCreate_R, cachelist)
+  ## Load existing model, dispatch for on disk model file and in memory buffer
   if (!is.null(modelfile)) {
     if (typeof(modelfile) == "character") {
+      ## A filename
+      handle <- .Call(XGBoosterCreate_R, cachelist)
       .Call(XGBoosterLoadModel_R, handle, modelfile[1])
+      class(handle) <- "xgb.Booster.handle"
+      if (length(params) > 0) {
+        xgb.parameters(handle) <- params
+      }
+      return(handle)
     } else if (typeof(modelfile) == "raw") {
-      .Call(XGBoosterLoadModelFromRaw_R, handle, modelfile)
+      ## A memory buffer
+      bst <- xgb.unserialize(modelfile)
+      xgb.parameters(bst) <- params
+      return (bst)
     } else if (inherits(modelfile, "xgb.Booster")) {
+      ## A booster object
       bst <- xgb.Booster.complete(modelfile, saveraw = TRUE)
-      .Call(XGBoosterLoadModelFromRaw_R, handle, bst$raw)
+      bst <- xgb.unserialize(bst$raw)
+      xgb.parameters(bst) <- params
+      return (bst)
     } else {
       stop("modelfile must be either character filename, or raw booster dump, or xgb.Booster object")
     }
   }
+  ## Create new model
+  handle <- .Call(XGBoosterCreate_R, cachelist)
   class(handle) <- "xgb.Booster.handle"
   if (length(params) > 0) {
     xgb.parameters(handle) <- params
@@ -113,8 +127,9 @@ xgb.Booster.complete <- function(object, saveraw = TRUE) {
   if (is.null.handle(object$handle)) {
     object$handle <- xgb.Booster.handle(modelfile = object$raw)
   } else {
-    if (is.null(object$raw) && saveraw)
-      object$raw <- xgb.save.raw(object$handle)
+    if (is.null(object$raw) && saveraw) {
+      object$raw <- xgb.serialize(object$handle)
+    }
   }
   return(object)
 }
@@ -399,7 +414,7 @@ predict.xgb.Booster.handle <- function(object, ...) {
 #' That would only matter if attributes need to be set many times.
 #' Note, however, that when feeding a handle of an \code{xgb.Booster} object to the attribute setters,
 #' the raw model cache of an \code{xgb.Booster} object would not be automatically updated,
-#' and it would be user's responsibility to call \code{xgb.save.raw} to update it.
+#' and it would be user's responsibility to call \code{xgb.serialize} to update it.
 #'
 #' The \code{xgb.attributes<-} setter either updates the existing or adds one or several attributes,
 #' but it doesn't delete the other existing attributes.
@@ -458,7 +473,7 @@ xgb.attr <- function(object, name) {
   }
   .Call(XGBoosterSetAttr_R, handle, as.character(name[1]), value)
   if (is(object, 'xgb.Booster') && !is.null(object$raw)) {
-    object$raw <- xgb.save.raw(object$handle)
+    object$raw <- xgb.serialize(object$handle)
   }
   object
 }
@@ -498,7 +513,7 @@ xgb.attributes <- function(object) {
     .Call(XGBoosterSetAttr_R, handle, names(a[i]), a[[i]])
   }
   if (is(object, 'xgb.Booster') && !is.null(object$raw)) {
-    object$raw <- xgb.save.raw(object$handle)
+    object$raw <- xgb.serialize(object$handle)
   }
   object
 }
@@ -528,7 +543,8 @@ xgb.config <- function(object) {
 `xgb.config<-` <- function(object, value) {
   handle <- xgb.get.handle(object)
   .Call(XGBoosterLoadJsonConfig_R, handle, value)
-  object$raw <- xgb.Booster.complete(object)
+  object$raw <- NULL  # force renew the raw buffer
+  object <- xgb.Booster.complete(object)
   object
 }
 
@@ -568,7 +584,7 @@ xgb.config <- function(object) {
     .Call(XGBoosterSetParam_R, handle, names(p[i]), p[[i]])
   }
   if (is(object, 'xgb.Booster') && !is.null(object$raw)) {
-    object$raw <- xgb.save.raw(object$handle)
+    object$raw <- xgb.serialize(object$handle)
   }
   object
 }

R-package/R/xgb.load.raw.R

@@ -0,0 +1,14 @@
+#' Load serialised xgboost model from R's raw vector
+#'
+#' User can generate raw memory buffer by calling xgb.save.raw
+#'
+#' @param buffer the buffer returned by xgb.save.raw
+#'
+#' @export
+xgb.load.raw <- function(buffer) {
+  cachelist <- list()
+  handle <- .Call(XGBoosterCreate_R, cachelist)
+  .Call(XGBoosterLoadModelFromRaw_R, handle, buffer)
+  class(handle) <- "xgb.Booster.handle"
+  return (handle)
+}

R-package/R/xgb.save.raw.R

@@ -1,23 +1,23 @@
 #' Save xgboost model to R's raw vector,
-#' user can call xgb.load to load the model back from raw vector
-#' 
+#' user can call xgb.load.raw to load the model back from raw vector
+#'
 #' Save xgboost model from xgboost or xgb.train
-#' 
+#'
 #' @param model the model object.
-#' 
+#'
 #' @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")
 #' raw <- xgb.save.raw(bst)
-#' bst <- xgb.load(raw)
+#' bst <- xgb.load.raw(raw)
 #' pred <- predict(bst, test$data)
 #'
 #' @export
 xgb.save.raw <- function(model) {
-  model <- xgb.get.handle(model)
-  .Call(XGBoosterModelToRaw_R, model)
+  handle <- xgb.get.handle(model)
+  .Call(XGBoosterModelToRaw_R, handle)
 }

R-package/R/xgb.serialize.R

@@ -0,0 +1,21 @@
+#' Serialize the booster instance into R's raw vector.  The serialization method differs
+#' from \code{\link{xgb.save.raw}} as the latter one saves only the model but not
+#' parameters.  This serialization format is not stable across different xgboost versions.
+#'
+#' @param booster the booster instance
+#'
+#' @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")
+#' raw <- xgb.serialize(bst)
+#' bst <- xgb.unserialize(raw)
+#'
+#' @export
+xgb.serialize <- function(booster) {
+  handle <- xgb.get.handle(booster)
+  .Call(XGBoosterSerializeToBuffer_R, handle)
+}

R-package/R/xgb.unserialize.R

@@ -0,0 +1,12 @@
+#' Load the instance back from \code{\link{xgb.serialize}}
+#'
+#' @param buffer the buffer containing booster instance saved by \code{\link{xgb.serialize}}
+#'
+#' @export
+xgb.unserialize <- function(buffer) {
+  cachelist <- list()
+  handle <- .Call(XGBoosterCreate_R, cachelist)
+  .Call(XGBoosterUnserializeFromBuffer_R, handle, buffer)
+  class(handle) <- "xgb.Booster.handle"
+  return (handle)
+}

R-package/man/agaricus.test.Rd

@@ -4,8 +4,10 @@
 \name{agaricus.test}
 \alias{agaricus.test}
 \title{Test part from Mushroom Data Set}
-\format{A list containing a label vector, and a dgCMatrix object with 1611
-rows and 126 variables}
+\format{
+A list containing a label vector, and a dgCMatrix object with 1611
+rows and 126 variables
+}
 \usage{
 data(agaricus.test)
 }

R-package/man/agaricus.train.Rd

@@ -4,8 +4,10 @@
 \name{agaricus.train}
 \alias{agaricus.train}
 \title{Training part from Mushroom Data Set}
-\format{A list containing a label vector, and a dgCMatrix object with 6513
-rows and 127 variables}
+\format{
+A list containing a label vector, and a dgCMatrix object with 6513
+rows and 127 variables
+}
 \usage{
 data(agaricus.train)
 }

R-package/man/xgb.attr.Rd

@@ -55,7 +55,7 @@ than for \code{xgb.Booster}, since only just a handle (pointer) would need to be
 That would only matter if attributes need to be set many times.
 Note, however, that when feeding a handle of an \code{xgb.Booster} object to the attribute setters,
 the raw model cache of an \code{xgb.Booster} object would not be automatically updated,
-and it would be user's responsibility to call \code{xgb.save.raw} to update it.
+and it would be user's responsibility to call \code{xgb.serialize} to update it.
 
 The \code{xgb.attributes<-} setter either updates the existing or adds one or several attributes,
 but it doesn't delete the other existing attributes.

R-package/man/xgb.cv.Rd

@@ -135,7 +135,7 @@ An object of class \code{xgb.cv.synchronous} with the following elements:
         (only available with early stopping).
   \item \code{pred} CV prediction values available when \code{prediction} is set.
         It is either vector or matrix (see \code{\link{cb.cv.predict}}).
-  \item \code{models} a liost of the CV folds' models. It is only available with the explicit
+  \item \code{models} a list of the CV folds' models. It is only available with the explicit
         setting of the \code{cb.cv.predict(save_models = TRUE)} callback.
 }
 }

R-package/man/xgb.load.raw.Rd

@@ -0,0 +1,14 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/xgb.load.raw.R
+\name{xgb.load.raw}
+\alias{xgb.load.raw}
+\title{Load serialised xgboost model from R's raw vector}
+\usage{
+xgb.load.raw(buffer)
+}
+\arguments{
+\item{buffer}{the buffer returned by xgb.save.raw}
+}
+\description{
+User can generate raw memory buffer by calling xgb.save.raw
+}

R-package/man/xgb.save.raw.Rd

@@ -3,7 +3,7 @@
 \name{xgb.save.raw}
 \alias{xgb.save.raw}
 \title{Save xgboost model to R's raw vector,
-user can call xgb.load to load the model back from raw vector}
+user can call xgb.load.raw to load the model back from raw vector}
 \usage{
 xgb.save.raw(model)
 }
@@ -18,10 +18,10 @@ 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")
 raw <- xgb.save.raw(bst)
-bst <- xgb.load(raw)
+bst <- xgb.load.raw(raw)
 pred <- predict(bst, test$data)
 
 }

R-package/man/xgb.serialize.Rd

@@ -0,0 +1,29 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/xgb.serialize.R
+\name{xgb.serialize}
+\alias{xgb.serialize}
+\title{Serialize the booster instance into R's raw vector.  The serialization method differs
+from \code{\link{xgb.save.raw}} as the latter one saves only the model but not
+parameters.  This serialization format is not stable across different xgboost versions.}
+\usage{
+xgb.serialize(booster)
+}
+\arguments{
+\item{booster}{the booster instance}
+}
+\description{
+Serialize the booster instance into R's raw vector.  The serialization method differs
+from \code{\link{xgb.save.raw}} as the latter one saves only the model but not
+parameters.  This serialization format is not stable across different xgboost versions.
+}
+\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")
+raw <- xgb.serialize(bst)
+bst <- xgb.unserialize(raw)
+
+}

R-package/man/xgb.unserialize.Rd

@@ -0,0 +1,14 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/xgb.unserialize.R
+\name{xgb.unserialize}
+\alias{xgb.unserialize}
+\title{Load the instance back from \code{\link{xgb.serialize}}}
+\usage{
+xgb.unserialize(buffer)
+}
+\arguments{
+\item{buffer}{the buffer containing booster instance saved by \code{\link{xgb.serialize}}}
+}
+\description{
+Load the instance back from \code{\link{xgb.serialize}}
+}

R-package/src/init.c

@@ -25,6 +25,8 @@ extern SEXP XGBoosterLoadModelFromRaw_R(SEXP, SEXP);
 extern SEXP XGBoosterLoadModel_R(SEXP, SEXP);
 extern SEXP XGBoosterSaveJsonConfig_R(SEXP handle);
 extern SEXP XGBoosterLoadJsonConfig_R(SEXP handle, SEXP value);
+extern SEXP XGBoosterSerializeToBuffer_R(SEXP handle);
+extern SEXP XGBoosterUnserializeFromBuffer_R(SEXP handle, SEXP raw);
 extern SEXP XGBoosterModelToRaw_R(SEXP);
 extern SEXP XGBoosterPredict_R(SEXP, SEXP, SEXP, SEXP, SEXP);
 extern SEXP XGBoosterSaveModel_R(SEXP, SEXP);
@@ -53,6 +55,8 @@ static const R_CallMethodDef CallEntries[] = {
   {"XGBoosterLoadModel_R",        (DL_FUNC) &XGBoosterLoadModel_R,        2},
   {"XGBoosterSaveJsonConfig_R",   (DL_FUNC) &XGBoosterSaveJsonConfig_R,   1},
   {"XGBoosterLoadJsonConfig_R",   (DL_FUNC) &XGBoosterLoadJsonConfig_R,   2},
+  {"XGBoosterSerializeToBuffer_R",     (DL_FUNC) &XGBoosterSerializeToBuffer_R,     1},
+  {"XGBoosterUnserializeFromBuffer_R", (DL_FUNC) &XGBoosterUnserializeFromBuffer_R, 2},
   {"XGBoosterModelToRaw_R",       (DL_FUNC) &XGBoosterModelToRaw_R,       1},
   {"XGBoosterPredict_R",          (DL_FUNC) &XGBoosterPredict_R,          5},
   {"XGBoosterSaveModel_R",        (DL_FUNC) &XGBoosterSaveModel_R,        2},

R-package/src/xgboost_R.cc

@@ -338,15 +338,6 @@ SEXP XGBoosterSaveModel_R(SEXP handle, SEXP fname) {
   return R_NilValue;
 }
 
-SEXP XGBoosterLoadModelFromRaw_R(SEXP handle, SEXP raw) {
-  R_API_BEGIN();
-  CHECK_CALL(XGBoosterLoadModelFromBuffer(R_ExternalPtrAddr(handle),
-                                          RAW(raw),
-                                          length(raw)));
-  R_API_END();
-  return R_NilValue;
-}
-
 SEXP XGBoosterModelToRaw_R(SEXP handle) {
   SEXP ret;
   R_API_BEGIN();
@@ -362,6 +353,15 @@ SEXP XGBoosterModelToRaw_R(SEXP handle) {
   return ret;
 }
 
+SEXP XGBoosterLoadModelFromRaw_R(SEXP handle, SEXP raw) {
+  R_API_BEGIN();
+  CHECK_CALL(XGBoosterLoadModelFromBuffer(R_ExternalPtrAddr(handle),
+                                          RAW(raw),
+                                          length(raw)));
+  R_API_END();
+  return R_NilValue;
+}
+
 SEXP XGBoosterSaveJsonConfig_R(SEXP handle) {
   const char* ret;
   R_API_BEGIN();
@@ -380,6 +380,30 @@ SEXP XGBoosterLoadJsonConfig_R(SEXP handle, SEXP value) {
   return R_NilValue;
 }
 
+SEXP XGBoosterSerializeToBuffer_R(SEXP handle) {
+  SEXP ret;
+  R_API_BEGIN();
+  bst_ulong out_len;
+  const char *raw;
+  CHECK_CALL(XGBoosterSerializeToBuffer(R_ExternalPtrAddr(handle), &out_len, &raw));
+  ret = PROTECT(allocVector(RAWSXP, out_len));
+  if (out_len != 0) {
+    memcpy(RAW(ret), raw, out_len);
+  }
+  R_API_END();
+  UNPROTECT(1);
+  return ret;
+}
+
+SEXP XGBoosterUnserializeFromBuffer_R(SEXP handle, SEXP raw) {
+  R_API_BEGIN();
+  XGBoosterUnserializeFromBuffer(R_ExternalPtrAddr(handle),
+                                 RAW(raw),
+                                 length(raw));
+  R_API_END();
+  return R_NilValue;
+}
+
 SEXP XGBoosterDumpModel_R(SEXP handle, SEXP fmap, SEXP with_stats, SEXP dump_format) {
   SEXP out;
   R_API_BEGIN();

R-package/src/xgboost_R.h

@@ -187,6 +187,7 @@ XGB_DLL SEXP XGBoosterModelToRaw_R(SEXP handle);
  * \param handle handle
  * \return JSON string
  */
+
 XGB_DLL SEXP XGBoosterSaveJsonConfig_R(SEXP handle);
 /*!
  * \brief Load the JSON string returnd by XGBoosterSaveJsonConfig_R
@@ -195,6 +196,22 @@ XGB_DLL SEXP XGBoosterSaveJsonConfig_R(SEXP handle);
  * \return R_NilValue
  */
 XGB_DLL SEXP XGBoosterLoadJsonConfig_R(SEXP handle, SEXP value);
+
+/*!
+  * \brief Memory snapshot based serialization method.  Saves everything states
+  *        into buffer.
+  * \param handle handle to booster
+  */
+XGB_DLL SEXP XGBoosterSerializeToBuffer_R(SEXP handle);
+
+/*!
+ * \brief Memory snapshot based serialization method.  Loads the buffer returned
+ *        from `XGBoosterSerializeToBuffer'.
+ * \param handle handle to booster
+ * \return raw byte array
+ */
+XGB_DLL SEXP XGBoosterUnserializeFromBuffer_R(SEXP handle, SEXP raw);
+
 /*!
  * \brief dump model into a string
  * \param handle handle

R-package/tests/testthat/test_basic.R

@@ -219,6 +219,21 @@ test_that("training continuation works", {
   expect_equal(dim(bst2$evaluation_log), c(2, 2))
 })
 
+test_that("model serialization works", {
+  out_path <- "model_serialization"
+  dtrain <- xgb.DMatrix(train$data, label = train$label)
+  watchlist = list(train=dtrain)
+  param <- list(objective = "binary:logistic")
+  booster <- xgb.train(param, dtrain, nrounds = 4, watchlist)
+  raw <- xgb.serialize(booster)
+  saveRDS(raw, out_path)
+  raw <- readRDS(out_path)
+
+  loaded <- xgb.unserialize(raw)
+  raw_from_loaded <- xgb.serialize(loaded)
+  expect_equal(raw, raw_from_loaded)
+  file.remove(out_path)
+})
 
 test_that("xgb.cv works", {
   set.seed(11)

R-package/tests/testthat/test_callbacks.R

@@ -30,16 +30,16 @@ param <- list(objective = "binary:logistic", max_depth = 2, nthread = 2)
 
 
 test_that("cb.print.evaluation works as expected", {
-  
+
   bst_evaluation <- c('train-auc'=0.9, 'test-auc'=0.8)
   bst_evaluation_err <- NULL
   begin_iteration <- 1
   end_iteration <- 7
-  
+
   f0 <- cb.print.evaluation(period=0)
   f1 <- cb.print.evaluation(period=1)
   f5 <- cb.print.evaluation(period=5)
-  
+
   expect_false(is.null(attr(f1, 'call')))
   expect_equal(attr(f1, 'name'), 'cb.print.evaluation')
 
@@ -48,15 +48,15 @@ test_that("cb.print.evaluation works as expected", {
   expect_output(f1(), "\\[1\\]\ttrain-auc:0.900000\ttest-auc:0.800000")
   expect_output(f5(), "\\[1\\]\ttrain-auc:0.900000\ttest-auc:0.800000")
   expect_null(f1())
-  
+
   iteration <- 2
   expect_output(f1(), "\\[2\\]\ttrain-auc:0.900000\ttest-auc:0.800000")
   expect_silent(f5())
-  
+
   iteration <- 7
   expect_output(f1(), "\\[7\\]\ttrain-auc:0.900000\ttest-auc:0.800000")
   expect_output(f5(), "\\[7\\]\ttrain-auc:0.900000\ttest-auc:0.800000")
-  
+
   bst_evaluation_err  <- c('train-auc'=0.1, 'test-auc'=0.2)
   expect_output(f1(), "\\[7\\]\ttrain-auc:0.900000\\+0.100000\ttest-auc:0.800000\\+0.200000")
 })
@@ -65,40 +65,40 @@ test_that("cb.evaluation.log works as expected", {
 
   bst_evaluation <- c('train-auc'=0.9, 'test-auc'=0.8)
   bst_evaluation_err <- NULL
-  
+
   evaluation_log <- list()
   f <- cb.evaluation.log()
-  
+
   expect_false(is.null(attr(f, 'call')))
   expect_equal(attr(f, 'name'), 'cb.evaluation.log')
-  
+
   iteration <- 1
   expect_silent(f())
-  expect_equal(evaluation_log, 
+  expect_equal(evaluation_log,
                list(c(iter=1, bst_evaluation)))
   iteration <- 2
   expect_silent(f())
-  expect_equal(evaluation_log, 
+  expect_equal(evaluation_log,
                list(c(iter=1, bst_evaluation), c(iter=2, bst_evaluation)))
   expect_silent(f(finalize = TRUE))
-  expect_equal(evaluation_log, 
+  expect_equal(evaluation_log,
                data.table(iter=1:2, train_auc=c(0.9,0.9), test_auc=c(0.8,0.8)))
-  
+
   bst_evaluation_err  <- c('train-auc'=0.1, 'test-auc'=0.2)
   evaluation_log <- list()
   f <- cb.evaluation.log()
-  
+
   iteration <- 1
   expect_silent(f())
-  expect_equal(evaluation_log, 
+  expect_equal(evaluation_log,
                list(c(iter=1, c(bst_evaluation, bst_evaluation_err))))
   iteration <- 2
   expect_silent(f())
-  expect_equal(evaluation_log, 
+  expect_equal(evaluation_log,
                list(c(iter=1, c(bst_evaluation, bst_evaluation_err)),
                     c(iter=2, c(bst_evaluation, bst_evaluation_err))))
   expect_silent(f(finalize = TRUE))
-  expect_equal(evaluation_log, 
+  expect_equal(evaluation_log,
                data.table(iter=1:2,
                           train_auc_mean=c(0.9,0.9), train_auc_std=c(0.1,0.1),
                           test_auc_mean=c(0.8,0.8), test_auc_std=c(0.2,0.2)))
@@ -130,18 +130,18 @@ test_that("cb.reset.parameters works as expected", {
   bst1 <- xgb.train(param, dtrain, nrounds = 2, watchlist, verbose = 0,
                     callbacks = list(cb.reset.parameters(my_par)))
   expect_false(is.null(bst1$evaluation_log$train_error))
-  expect_equal(bst0$evaluation_log$train_error, 
+  expect_equal(bst0$evaluation_log$train_error,
                bst1$evaluation_log$train_error)
-  
+
   # same eta but re-set via a function in the callback
   set.seed(111)
   my_par <- list(eta = function(itr, itr_end) 0.9)
   bst2 <- xgb.train(param, dtrain, nrounds = 2, watchlist, verbose = 0,
                     callbacks = list(cb.reset.parameters(my_par)))
   expect_false(is.null(bst2$evaluation_log$train_error))
-  expect_equal(bst0$evaluation_log$train_error, 
+  expect_equal(bst0$evaluation_log$train_error,
                bst2$evaluation_log$train_error)
-  
+
   # different eta re-set as a vector parameter in the callback
   set.seed(111)
   my_par <- list(eta = c(0.6, 0.5))
@@ -149,7 +149,7 @@ test_that("cb.reset.parameters works as expected", {
                     callbacks = list(cb.reset.parameters(my_par)))
   expect_false(is.null(bst3$evaluation_log$train_error))
   expect_false(all(bst0$evaluation_log$train_error == bst3$evaluation_log$train_error))
-  
+
   # resetting multiple parameters at the same time runs with no error
   my_par <- list(eta = c(1., 0.5), gamma = c(1, 2), max_depth = c(4, 8))
   expect_error(
@@ -175,7 +175,7 @@ test_that("cb.reset.parameters works as expected", {
 test_that("cb.save.model works as expected", {
   files <- c('xgboost_01.model', 'xgboost_02.model', 'xgboost.model')
   for (f in files) if (file.exists(f)) file.remove(f)
-  
+
   bst <- xgb.train(param, dtrain, nrounds = 2, watchlist, eta = 1, verbose = 0,
                    save_period = 1, save_name = "xgboost_%02d.model")
   expect_true(file.exists('xgboost_01.model'))
@@ -184,6 +184,9 @@ test_that("cb.save.model works as expected", {
   expect_equal(xgb.ntree(b1), 1)
   b2 <- xgb.load('xgboost_02.model')
   expect_equal(xgb.ntree(b2), 2)
+
+  xgb.config(b2) <- xgb.config(bst)
+  expect_equal(xgb.config(bst), xgb.config(b2))
   expect_equal(bst$raw, b2$raw)
 
   # save_period = 0 saves the last iteration's model
@@ -191,8 +194,9 @@ test_that("cb.save.model works as expected", {
                    save_period = 0)
   expect_true(file.exists('xgboost.model'))
   b2 <- xgb.load('xgboost.model')
+  xgb.config(b2) <- xgb.config(bst)
   expect_equal(bst$raw, b2$raw)
-  
+
   for (f in files) if (file.exists(f)) file.remove(f)
 })
 
@@ -211,7 +215,7 @@ test_that("early stopping xgb.train works", {
   err_pred <- err(ltest, pred)
   err_log <- bst$evaluation_log[bst$best_iteration, test_error]
   expect_equal(err_log, err_pred, tolerance = 5e-6)
-  
+
   set.seed(11)
   expect_silent(
     bst0 <- xgb.train(param, dtrain, nrounds = 20, watchlist, eta = 0.3,
@@ -288,13 +292,13 @@ test_that("prediction in early-stopping xgb.cv works", {
                  early_stopping_rounds = 5, maximize = FALSE, stratified = FALSE,
                  prediction = TRUE)
   , "Stopping. Best iteration")
-  
+
   expect_false(is.null(cv$best_iteration))
   expect_lt(cv$best_iteration, 19)
   expect_false(is.null(cv$evaluation_log))
   expect_false(is.null(cv$pred))
   expect_length(cv$pred, nrow(train$data))
-  
+
   err_pred <- mean( sapply(cv$folds, function(f) mean(err(ltrain[f], cv$pred[f]))) )
   err_log <- cv$evaluation_log[cv$best_iteration, test_error_mean]
   expect_equal(err_pred, err_log, tolerance = 1e-6)