[R] Prefer descriptive parameter names (#11102)

R-package/R/callbacks.R

@@ -960,17 +960,17 @@ xgb.cb.cv.predict <- function(save_models = FALSE, outputmargin = FALSE) {
 #'   booster = "gblinear",
 #'   objective = "reg:logistic",
 #'   eval_metric = "auc",
-#'   lambda = 0.0003,
-#'   alpha = 0.0003,
+#'   reg_lambda = 0.0003,
+#'   reg_alpha = 0.0003,
 #'   nthread = nthread
 #' )
 #'
-#' # For 'shotgun', which is a default linear updater, using high eta values may result in
+#' # For 'shotgun', which is a default linear updater, using high learning_rate values may result in
 #' # unstable behaviour in some datasets. With this simple dataset, however, the high learning
 #' # rate does not break the convergence, but allows us to illustrate the typical pattern of
 #' # "stochastic explosion" behaviour of this lock-free algorithm at early boosting iterations.
 #' bst <- xgb.train(
-#'   c(param, list(eta = 1.)),
+#'   c(param, list(learning_rate = 1.)),
 #'   dtrain,
 #'   evals = list(tr = dtrain),
 #'   nrounds = 200,
@@ -987,7 +987,7 @@ xgb.cb.cv.predict <- function(save_models = FALSE, outputmargin = FALSE) {
 #'   c(
 #'     param,
 #'     xgb.params(
-#'       eta = 0.8,
+#'       learning_rate = 0.8,
 #'       updater = "coord_descent",
 #'       feature_selector = "thrifty",
 #'       top_k = 1
@@ -1000,12 +1000,20 @@ xgb.cb.cv.predict <- function(save_models = FALSE, outputmargin = FALSE) {
 #' )
 #' matplot(xgb.gblinear.history(bst), type = "l")
 #' #  Componentwise boosting is known to have similar effect to Lasso regularization.
-#' # Try experimenting with various values of top_k, eta, nrounds,
+#' # Try experimenting with various values of top_k, learning_rate, nrounds,
 #' # as well as different feature_selectors.
 #'
 #' # For xgb.cv:
 #' bst <- xgb.cv(
-#'   c(param, list(eta = 0.8)),
+#'   c(
+#'     param,
+#'     xgb.params(
+#'       learning_rate = 0.8,
+#'       updater = "coord_descent",
+#'       feature_selector = "thrifty",
+#'       top_k = 1
+#'     )
+#'   ),
 #'   dtrain,
 #'   nfold = 5,
 #'   nrounds = 100,
@@ -1022,15 +1030,15 @@ xgb.cb.cv.predict <- function(save_models = FALSE, outputmargin = FALSE) {
 #'   booster = "gblinear",
 #'   objective = "multi:softprob",
 #'   num_class = 3,
-#'   lambda = 0.0003,
-#'   alpha = 0.0003,
+#'   reg_lambda = 0.0003,
+#'   reg_alpha = 0.0003,
 #'   nthread = nthread
 #' )
 #'
 #' # For the default linear updater 'shotgun' it sometimes is helpful
-#' # to use smaller eta to reduce instability
+#' # to use smaller learning_rate to reduce instability
 #' bst <- xgb.train(
-#'   c(param, list(eta = 0.5)),
+#'   c(param, list(learning_rate = 0.5)),
 #'   dtrain,
 #'   evals = list(tr = dtrain),
 #'   nrounds = 50,
@@ -1044,7 +1052,7 @@ xgb.cb.cv.predict <- function(save_models = FALSE, outputmargin = FALSE) {
 #'
 #' # CV:
 #' bst <- xgb.cv(
-#'   c(param, list(eta = 0.5)),
+#'   c(param, list(learning_rate = 0.5)),
 #'   dtrain,
 #'   nfold = 5,
 #'   nrounds = 70,

R-package/R/utils.R

@@ -493,7 +493,6 @@ NULL
 #'   nrounds = 2,
 #'   params = xgb.params(
 #'     max_depth = 2,
-#'     eta = 1,
 #'     nthread = 2,
 #'     objective = "binary:logistic"
 #'   )

R-package/R/xgb.Booster.R

@@ -267,7 +267,6 @@ xgb.get.handle <- function(object) {
 #'   nrounds = 5,
 #'   params = xgb.params(
 #'     max_depth = 2,
-#'     eta = 0.5,
 #'     nthread = nthread,
 #'     objective = "binary:logistic"
 #'   )
@@ -312,7 +311,6 @@ xgb.get.handle <- function(object) {
 #'   nrounds = 10,
 #'   params = xgb.params(
 #'     max_depth = 4,
-#'     eta = 0.5,
 #'     nthread = 2,
 #'     subsample = 0.5,
 #'     objective = "multi:softprob",
@@ -336,7 +334,6 @@ xgb.get.handle <- function(object) {
 #'   nrounds = 10,
 #'   params = xgb.params(
 #'     max_depth = 4,
-#'     eta = 0.5,
 #'     nthread = 2,
 #'     subsample = 0.5,
 #'     objective = "multi:softmax",
@@ -671,7 +668,6 @@ validate.features <- function(bst, newdata) {
 #'   nrounds = 2,
 #'   params = xgb.params(
 #'     max_depth = 2,
-#'     eta = 1,
 #'     nthread = 2,
 #'     objective = "binary:logistic"
 #'   )
@@ -779,7 +775,6 @@ xgb.attributes <- function(object) {
 #'   nrounds = 2,
 #'   params = xgb.params(
 #'     max_depth = 2,
-#'     eta = 1,
 #'     nthread = nthread,
 #'     objective = "binary:logistic"
 #'   )
@@ -834,13 +829,13 @@ xgb.config <- function(object) {
 #'   nrounds = 2,
 #'   params = xgb.params(
 #'     max_depth = 2,
-#'     eta = 1,
+#'     learning_rate = 1,
 #'     nthread = 2,
 #'     objective = "binary:logistic"
 #'   )
 #' )
 #'
-#' xgb.model.parameters(bst) <- list(eta = 0.1)
+#' xgb.model.parameters(bst) <- list(learning_rate = 0.1)
 #'
 #' @rdname xgb.model.parameters
 #' @export
@@ -1285,7 +1280,6 @@ xgb.is.same.Booster <- function(obj1, obj2) {
 #'   nrounds = 2,
 #'   params = xgb.params(
 #'     max_depth = 2,
-#'     eta = 1,
 #'     nthread = 2,
 #'     objective = "binary:logistic"
 #'   )

R-package/R/xgb.create.features.R

@@ -52,7 +52,7 @@
 #' dtrain <- with(agaricus.train, xgb.DMatrix(data, label = label, nthread = 2))
 #' dtest <- with(agaricus.test, xgb.DMatrix(data, label = label, nthread = 2))
 #'
-#' param <- list(max_depth = 2, eta = 1, objective = 'binary:logistic', nthread = 1)
+#' param <- list(max_depth = 2, learning_rate = 1, objective = 'binary:logistic', nthread = 1)
 #' nrounds = 4
 #'
 #' bst <- xgb.train(params = param, data = dtrain, nrounds = nrounds)

R-package/R/xgb.cv.R

@@ -96,7 +96,6 @@
 #'   params = xgb.params(
 #'     nthread = 2,
 #'     max_depth = 3,
-#'     eta = 1,
 #'     objective = "binary:logistic"
 #'   ),
 #'   nfold = 5,
@@ -316,7 +315,6 @@ xgb.cv <- function(params = xgb.params(), data, nrounds, nfold,
 #'   nrounds = 2,
 #'   params = xgb.params(
 #'     max_depth = 2,
-#'     eta = 1,
 #'     nthread = 2,
 #'     objective = "binary:logistic"
 #'   )

R-package/R/xgb.dump.R

@@ -34,7 +34,6 @@
 #'   nrounds = 2,
 #'   params = xgb.params(
 #'     max_depth = 2,
-#'     eta = 1,
 #'     nthread = 2,
 #'     objective = "binary:logistic"
 #'   )

R-package/R/xgb.importance.R

@@ -47,7 +47,6 @@
 #'   nrounds = 2,
 #'   params = xgb.params(
 #'     max_depth = 2,
-#'     eta = 1,
 #'     nthread = 2,
 #'     objective = "binary:logistic"
 #'   )
@@ -61,7 +60,7 @@
 #'   nrounds = 20,
 #'   params = xgb.params(
 #'     booster = "gblinear",
-#'     eta = 0.3,
+#'     learning_rate = 0.3,
 #'     nthread = 1,
 #'     objective = "binary:logistic"
 #'   )
@@ -80,7 +79,6 @@
 #'   nrounds = nrounds,
 #'   params = xgb.params(
 #'     max_depth = 3,
-#'     eta = 0.2,
 #'     nthread = 2,
 #'     objective = "multi:softprob",
 #'     num_class = nclass
@@ -110,7 +108,7 @@
 #'   nrounds = 15,
 #'   params = xgb.params(
 #'     booster = "gblinear",
-#'     eta = 0.2,
+#'     learning_rate = 0.2,
 #'     nthread = 1,
 #'     objective = "multi:softprob",
 #'     num_class = nclass

R-package/R/xgb.load.R

@@ -35,7 +35,6 @@
 #'   nrounds = 2,
 #'   params = xgb.params(
 #'     max_depth = 2,
-#'     eta = 1,
 #'     nthread = nthread,
 #'     objective = "binary:logistic"
 #'   )

R-package/R/xgb.model.dt.tree.R

@@ -47,7 +47,6 @@
 #'   nrounds = 2,
 #'   params = xgb.params(
 #'     max_depth = 2,
-#'     eta = 1,
 #'     nthread = nthread,
 #'     objective = "binary:logistic"
 #'   )

R-package/R/xgb.plot.importance.R

@@ -55,7 +55,6 @@
 #'   nrounds = 2,
 #'   params = xgb.params(
 #'     max_depth = 3,
-#'     eta = 1,
 #'     nthread = nthread,
 #'     objective = "binary:logistic"
 #'   )

R-package/R/xgb.plot.multi.trees.R

@@ -42,7 +42,7 @@
 #'   verbose = 0,
 #'   params = xgb.params(
 #'     max_depth = 15,
-#'     eta = 1,
+#'     learning_rate = 1,
 #'     nthread = nthread,
 #'     objective = "binary:logistic",
 #'     min_child_weight = 50

R-package/R/xgb.plot.shap.R

@@ -86,7 +86,7 @@
 #'   nrounds = nrounds,
 #'   verbose = 0,
 #'   params = xgb.params(
-#'     eta = 0.1,
+#'     learning_rate = 0.1,
 #'     max_depth = 3,
 #'     subsample = 0.5,
 #'     objective = "binary:logistic",
@@ -114,7 +114,6 @@
 #'   verbose = 0,
 #'   params = xgb.params(
 #'     max_depth = 2,
-#'     eta = 0.3,
 #'     subsample = 0.5,
 #'     nthread = nthread,
 #'     objective = "multi:softprob",

R-package/R/xgb.plot.tree.R

@@ -44,7 +44,6 @@
 #'   nrounds = 2,
 #'   params = xgb.params(
 #'     max_depth = 3,
-#'     eta = 1,
 #'     nthread = 2,
 #'     objective = "binary:logistic"
 #'   )

R-package/R/xgb.save.R

@@ -47,7 +47,6 @@
 #'   nrounds = 2,
 #'   params = xgb.params(
 #'     max_depth = 2,
-#'     eta = 1,
 #'     nthread = nthread,
 #'     objective = "binary:logistic"
 #'   )

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

@@ -26,7 +26,6 @@
 #'   nrounds = 2,
 #'   params = xgb.params(
 #'     max_depth = 2,
-#'     eta = 1,
 #'     nthread = nthread,
 #'     objective = "binary:logistic"
 #'   )

R-package/R/xgb.train.R

@@ -150,7 +150,6 @@
 #' ## A simple xgb.train example:
 #' param <- xgb.params(
 #'   max_depth = 2,
-#'   eta = 1,
 #'   nthread = nthread,
 #'   objective = "binary:logistic",
 #'   eval_metric = "auc"
@@ -176,7 +175,6 @@
 #' # 'eval_metric' parameters in the params list:
 #' param <- xgb.params(
 #'   max_depth = 2,
-#'   eta = 1,
 #'   nthread = nthread,
 #'   objective = logregobj,
 #'   eval_metric = evalerror
@@ -194,20 +192,20 @@
 #' ## An xgb.train example of using variable learning rates at each iteration:
 #' param <- xgb.params(
 #'   max_depth = 2,
-#'   eta = 1,
+#'   learning_rate = 1,
 #'   nthread = nthread,
 #'   objective = "binary:logistic",
 #'   eval_metric = "auc"
 #' )
-#' my_etas <- list(eta = c(0.5, 0.1))
+#' my_learning_rates <- list(learning_rate = c(0.5, 0.1))
 #'
 #' bst <- xgb.train(
 #'  param,
 #'  dtrain,
 #'  nrounds = 2,
 #'  evals = evals,
 #'  verbose = 0,
-#'  callbacks = list(xgb.cb.reset.parameters(my_etas))
+#'  callbacks = list(xgb.cb.reset.parameters(my_learning_rates))
 #' )
 #'
 #' ## Early stopping: