[R] allow using seed with regular RNG (#10029)

R-package/R/xgb.train.R

@@ -178,6 +178,11 @@
 #' Number of threads can also be manually specified via the \code{nthread}
 #' parameter.
 #'
+#' While in other interfaces, the default random seed defaults to zero, in R, if a parameter `seed`
+#' is not manually supplied, it will generate a random seed through R's own random number generator,
+#' whose seed in turn is controllable through `set.seed`. If `seed` is passed, it will override the
+#' RNG from R.
+#'
 #' The evaluation metric is chosen automatically by XGBoost (according to the objective)
 #' when the \code{eval_metric} parameter is not provided.
 #' User may set one or several \code{eval_metric} parameters.
@@ -363,8 +368,8 @@ xgb.train <- function(params = list(), data, nrounds, watchlist = list(),
   # Sort the callbacks into categories
   cb <- categorize.callbacks(callbacks)
   params['validate_parameters'] <- TRUE
-  if (!is.null(params[['seed']])) {
+  if (!("seed" %in% names(params))) {
-    warning("xgb.train: `seed` is ignored in R package.  Use `set.seed()` instead.")
+    params[["seed"]] <- sample(.Machine$integer.max, size = 1)
   }
 
   # The tree updating process would need slightly different handling

R-package/man/xgb.train.Rd

@@ -241,6 +241,11 @@ Parallelization is automatically enabled if \code{OpenMP} is present.
 Number of threads can also be manually specified via the \code{nthread}
 parameter.
 
+While in other interfaces, the default random seed defaults to zero, in R, if a parameter \code{seed}
+is not manually supplied, it will generate a random seed through R's own random number generator,
+whose seed in turn is controllable through \code{set.seed}. If \code{seed} is passed, it will override the
+RNG from R.
+
 The evaluation metric is chosen automatically by XGBoost (according to the objective)
 when the \code{eval_metric} parameter is not provided.
 User may set one or several \code{eval_metric} parameters.

R-package/src/xgboost_custom.cc

@@ -41,16 +41,6 @@ double LogGamma(double v) {
   return lgammafn(v);
 }
 #endif  // !defined(XGBOOST_USE_CUDA)
-// customize random engine.
-void CustomGlobalRandomEngine::seed(CustomGlobalRandomEngine::result_type val) {
-  // ignore the seed
-}
 
-// use R's PRNG to replacd
-CustomGlobalRandomEngine::result_type
-CustomGlobalRandomEngine::operator()() {
-  return static_cast<result_type>(
-      std::floor(unif_rand() * CustomGlobalRandomEngine::max()));
-}
 }  // namespace common
 }  // namespace xgboost

R-package/tests/testthat/test_basic.R

@@ -778,3 +778,66 @@ test_that("DMatrix field are set to booster when training", {
   expect_equal(getinfo(model_feature_types, "feature_type"), c("q", "c", "q"))
   expect_equal(getinfo(model_both, "feature_type"), c("q", "c", "q"))
 })
+
+test_that("Seed in params override PRNG from R", {
+  set.seed(123)
+  model1 <- xgb.train(
+    data = xgb.DMatrix(
+      agaricus.train$data,
+      label = agaricus.train$label, nthread = 1L
+    ),
+    params = list(
+      objective = "binary:logistic",
+      max_depth = 3L,
+      subsample = 0.1,
+      colsample_bytree = 0.1,
+      seed = 111L
+    ),
+    nrounds = 3L
+  )
+
+  set.seed(456)
+  model2 <- xgb.train(
+    data = xgb.DMatrix(
+      agaricus.train$data,
+      label = agaricus.train$label, nthread = 1L
+    ),
+    params = list(
+      objective = "binary:logistic",
+      max_depth = 3L,
+      subsample = 0.1,
+      colsample_bytree = 0.1,
+      seed = 111L
+    ),
+    nrounds = 3L
+  )
+
+  expect_equal(
+    xgb.save.raw(model1, raw_format = "json"),
+    xgb.save.raw(model2, raw_format = "json")
+  )
+
+  set.seed(123)
+  model3 <- xgb.train(
+    data = xgb.DMatrix(
+      agaricus.train$data,
+      label = agaricus.train$label, nthread = 1L
+    ),
+    params = list(
+      objective = "binary:logistic",
+      max_depth = 3L,
+      subsample = 0.1,
+      colsample_bytree = 0.1,
+      seed = 222L
+    ),
+    nrounds = 3L
+  )
+  expect_false(
+    isTRUE(
+      all.equal(
+        xgb.save.raw(model1, raw_format = "json"),
+        xgb.save.raw(model3, raw_format = "json")
+      )
+    )
+  )
+})

doc/parameter.rst

@@ -450,7 +450,7 @@ Specify the learning task and the corresponding learning objective. The objectiv
 
 * ``seed`` [default=0]
 
-  - Random number seed.  This parameter is ignored in R package, use `set.seed()` instead.
+  - Random number seed.  In the R package, if not specified, instead of defaulting to seed 'zero', will take a random seed through R's own RNG engine.
 
 * ``seed_per_iteration`` [default= ``false``]
 

include/xgboost/base.h

@@ -37,7 +37,7 @@
  * \brief Whether to customize global PRNG.
  */
 #ifndef XGBOOST_CUSTOMIZE_GLOBAL_PRNG
-#define XGBOOST_CUSTOMIZE_GLOBAL_PRNG XGBOOST_STRICT_R_MODE
+#define XGBOOST_CUSTOMIZE_GLOBAL_PRNG 0
 #endif  // XGBOOST_CUSTOMIZE_GLOBAL_PRNG
 
 /*!

src/common/random.h

@@ -31,7 +31,7 @@ namespace xgboost::common {
  */
 using RandomEngine = std::mt19937;
 
-#if XGBOOST_CUSTOMIZE_GLOBAL_PRNG
+#if defined(XGBOOST_CUSTOMIZE_GLOBAL_PRNG) && XGBOOST_CUSTOMIZE_GLOBAL_PRNG == 1
 /*!
  * \brief An customized random engine, used to be plugged in PRNG from other systems.
  *  The implementation of this library is not provided by xgboost core library.