rebase

R-package/R/callbacks.R

@@ -280,7 +280,6 @@ cb.reset.parameters <- function(new_params) {
 #' \code{iteration},
 #' \code{begin_iteration},
 #' \code{end_iteration},
-#' \code{num_parallel_tree}.
 #'
 #' @seealso
 #' \code{\link{callbacks}},
@@ -291,7 +290,6 @@ cb.early.stop <- function(stopping_rounds, maximize = FALSE,
  metric_name = NULL, verbose = TRUE) {
  # state variables
  best_iteration <- -1
- best_ntreelimit <- -1
  best_score <- Inf
  best_msg <- NULL
  metric_idx <- 1
@@ -358,12 +356,10 @@ cb.early.stop <- function(stopping_rounds, maximize = FALSE,
  # If the difference is due to floating-point truncation, update best_score
  best_score <- attr_best_score
  }
- xgb.attr(env$bst, "best_iteration") <- best_iteration
- xgb.attr(env$bst, "best_ntreelimit") <- best_ntreelimit
+ xgb.attr(env$bst, "best_iteration") <- best_iteration - 1
  xgb.attr(env$bst, "best_score") <- best_score
  } else {
  env$basket$best_iteration <- best_iteration
- env$basket$best_ntreelimit <- best_ntreelimit
  }
  }
 
@@ -385,14 +381,13 @@ cb.early.stop <- function(stopping_rounds, maximize = FALSE,
  )
  best_score <<- score
  best_iteration <<- i
- best_ntreelimit <<- best_iteration * env$num_parallel_tree
  # save the property to attributes, so they will occur in checkpoint
  if (!is.null(env$bst)) {
  xgb.attributes(env$bst) <- list(
  best_iteration = best_iteration - 1, # convert to 0-based index
  best_score = best_score,
- best_msg = best_msg,
-  best_ntreelimit = best_ntreelimit)
+ best_msg = best_msg
+ )
  }
  } else if (i - best_iteration >= stopping_rounds) {
  env$stop_condition <- TRUE
@@ -475,8 +470,6 @@ cb.save.model <- function(save_period = 0, save_name = "xgboost.ubj") {
 #' \code{data},
 #' \code{end_iteration},
 #' \code{params},
-#' \code{num_parallel_tree},
-#' \code{num_class}.
 #'
 #' @return
 #' Predictions are returned inside of the \code{pred} element, which is either a vector or a matrix,
@@ -499,19 +492,21 @@ cb.cv.predict <- function(save_models = FALSE) {
  stop("'cb.cv.predict' callback requires 'basket' and 'bst_folds' lists in its calling frame")
 
  N <- nrow(env$data)
- pred <-
- if (env$num_class > 1) {
- matrix(NA_real_, N, env$num_class)
- } else {
- rep(NA_real_, N)
- }
+ pred <- NULL
 
- iterationrange <- c(1, NVL(env$basket$best_iteration, env$end_iteration) + 1)
+ iterationrange <- c(1, NVL(env$basket$best_iteration, env$end_iteration))
  if (NVL(env$params[['booster']], '') == 'gblinear') {
- iterationrange <- c(1, 1) # must be 0 for gblinear
+ iterationrange <- "all"
  }
  for (fd in env$bst_folds) {
  pr <- predict(fd$bst, fd$watchlist[[2]], iterationrange = iterationrange, reshape = TRUE)
+ if (is.null(pred)) {
+ if (NCOL(pr) > 1L) {
+ pred <- matrix(NA_real_, N, ncol(pr))
+ } else {
+ pred <- matrix(NA_real_, N)
+ }
+ }
  if (is.matrix(pred)) {
  pred[fd$index, ] <- pr
  } else {

R-package/R/utils.R

@@ -208,7 +208,7 @@ xgb.iter.eval <- function(bst, watchlist, iter, feval) {
  res <- sapply(seq_along(watchlist), function(j) {
  w <- watchlist[[j]]
  ## predict using all trees
- preds <- predict(bst, w, outputmargin = TRUE, iterationrange = c(1, 1))
+ preds <- predict(bst, w, outputmargin = TRUE, iterationrange = "all")
  eval_res <- feval(preds, w)
  out <- eval_res$value
  names(out) <- paste0(evnames[j], "-", eval_res$metric)

R-package/R/xgb.Booster.R

@@ -89,7 +89,6 @@ xgb.get.handle <- function(object) {
 #' @param outputmargin Whether the prediction should be returned in the form of original untransformed
 #' sum of predictions from boosting iterations' results. E.g., setting `outputmargin=TRUE` for
 #' logistic regression would return log-odds instead of probabilities.
-#' @param ntreelimit Deprecated, use `iterationrange` instead.
 #' @param predleaf Whether to predict pre-tree leaf indices.
 #' @param predcontrib Whether to return feature contributions to individual predictions (see Details).
 #' @param approxcontrib Whether to use a fast approximation for feature contributions (see Details).
@@ -99,11 +98,17 @@ xgb.get.handle <- function(object) {
 #' or `predinteraction` is `TRUE`.
 #' @param training Whether the predictions are used for training. For dart booster,
 #' training predicting will perform dropout.
-#' @param iterationrange Specifies which trees are used in prediction. For
-#' example, take a random forest with 100 rounds.
-#' With `iterationrange=c(1, 21)`, only the trees built during `[1, 21)` (half open set)
-#' rounds are used in this prediction. The index is 1-based just like an R vector. When set
-#' to `c(1, 1)`, XGBoost will use all trees.
+#' @param iterationrange Sequence of rounds/iterations from the model to use for prediction, specified by passing
+#' a two-dimensional vector with the start and end numbers in the sequence (same format as R's `seq` - i.e.
+#' base-1 indexing, and inclusive of both ends).
+#'
+#' For example, passing `c(1,20)` will predict using the first twenty iterations, while passing `c(1,1)` will
+#' predict using only the first one.
+#'
+#' If passing `NULL`, will either stop at the best iteration if the model used early stopping, or use all
+#' of the iterations (rounds) otherwise.
+#'
+#' If passing "all", will use all of the rounds regardless of whether the model had early stopping or not.
 #' @param strict_shape Default is `FALSE`. When set to `TRUE`, the output
 #' type and shape of predictions are invariant to the model type.
 #' @param ... Not used.
@@ -189,7 +194,7 @@ xgb.get.handle <- function(object) {
 #' # use all trees by default
 #' pred <- predict(bst, test$data)
 #' # use only the 1st tree
-#' pred1 <- predict(bst, test$data, iterationrange = c(1, 2))
+#' pred1 <- predict(bst, test$data, iterationrange = c(1, 1))
 #'
 #' # Predicting tree leafs:
 #' # the result is an nsamples X ntrees matrix
@@ -260,11 +265,11 @@ xgb.get.handle <- function(object) {
 #' all.equal(pred, pred_labels)
 #' # prediction from using only 5 iterations should result
 #' # in the same error as seen in iteration 5:
-#' pred5 <- predict(bst, as.matrix(iris[, -5]), iterationrange = c(1, 6))
+#' pred5 <- predict(bst, as.matrix(iris[, -5]), iterationrange = c(1, 5))
 #' sum(pred5 != lb) / length(lb)
 #'
 #' @export
-predict.xgb.Booster <- function(object, newdata, missing = NA, outputmargin = FALSE, ntreelimit = NULL,
+predict.xgb.Booster <- function(object, newdata, missing = NA, outputmargin = FALSE,
  predleaf = FALSE, predcontrib = FALSE, approxcontrib = FALSE, predinteraction = FALSE,
  reshape = FALSE, training = FALSE, iterationrange = NULL, strict_shape = FALSE, ...) {
  if (!inherits(newdata, "xgb.DMatrix")) {
@@ -275,25 +280,22 @@ predict.xgb.Booster <- function(object, newdata, missing = NA, outputmargin = FA
  )
  }
 
- if (NVL(xgb.booster_type(object), '') == 'gblinear' || is.null(ntreelimit))
- ntreelimit <- 0
 
- if (ntreelimit != 0 && is.null(iterationrange)) {
- ## only ntreelimit, initialize iteration range
- iterationrange <- c(0, 0)
- } else if (ntreelimit == 0 && !is.null(iterationrange)) {
- ## only iteration range, handle 1-based indexing
- iterationrange <- c(iterationrange[1] - 1, iterationrange[2] - 1)
- } else if (ntreelimit != 0 && !is.null(iterationrange)) {
- ## both are specified, let libgxgboost throw an error
+ if (!is.null(iterationrange)) {
+ if (is.character(iterationrange) &&
+  length(iterationrange) == 1 &&
+  iterationrange == "all") {
+  iterationrange <- c(0, 0)
+ } else {
+  iterationrange[1] <- iterationrange[1] - 1 # base-0 indexing
+ }
  } else {
  ## no limit is supplied, use best
  best_iteration <- xgb.best_iteration(object)
  if (is.null(best_iteration)) {
  iterationrange <- c(0, 0)
  } else {
- ## We don't need to + 1 as R is 1-based index.
- iterationrange <- c(0, as.integer(best_iteration))
+ iterationrange <- c(0, as.integer(best_iteration) + 1L)
  }
  }
  ## Handle the 0 length values.
@@ -312,7 +314,6 @@ predict.xgb.Booster <- function(object, newdata, missing = NA, outputmargin = FA
  strict_shape = box(TRUE),
  iteration_begin = box(as.integer(iterationrange[1])),
  iteration_end = box(as.integer(iterationrange[2])),
- ntree_limit = box(as.integer(ntreelimit)),
  type = box(as.integer(0))
  )
 
@@ -492,7 +493,7 @@ xgb.attr <- function(object, name) {
  return(NULL)
  }
  if (!is.null(out)) {
- if (name %in% c("best_iteration", "best_ntreelimit", "best_score")) {
+ if (name %in% c("best_iteration", "best_score")) {
  out <- as.numeric(out)
  }
  }
@@ -710,12 +711,6 @@ variable.names.xgb.Booster <- function(object, ...) {
  return(getinfo(object, "feature_name"))
 }
 
-xgb.ntree <- function(bst) {
- config <- xgb.config(bst)
- out <- strtoi(config$learner$gradient_booster$gbtree_model_param$num_trees)
- return(out)
-}
-
 xgb.nthread <- function(bst) {
  config <- xgb.config(bst)
  out <- strtoi(config$learner$generic_param$nthread)

R-package/R/xgb.cv.R

@@ -103,7 +103,6 @@
 #' parameter or randomly generated.
 #' \item \code{best_iteration} iteration number with the best evaluation metric value
 #' (only available with early stopping).
-#' \item \code{best_ntreelimit} and the \code{ntreelimit} Deprecated attributes, use \code{best_iteration} instead.
 #' \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 list of the CV folds' models. It is only available with the explicit
@@ -218,7 +217,6 @@ xgb.cv <- function(params = list(), data, nrounds, nfold, label = NULL, missing
 
  # extract parameters that can affect the relationship b/w #trees and #iterations
  num_class <- max(as.numeric(NVL(params[['num_class']], 1)), 1) # nolint
- num_parallel_tree <- max(as.numeric(NVL(params[['num_parallel_tree']], 1)), 1) # nolint
 
  # those are fixed for CV (no training continuation)
  begin_iteration <- 1
@@ -318,7 +316,7 @@ print.xgb.cv.synchronous <- function(x, verbose = FALSE, ...) {
  })
  }
 
- for (n in c('niter', 'best_iteration', 'best_ntreelimit')) {
+ for (n in c('niter', 'best_iteration')) {
  if (is.null(x[[n]]))
  next
  cat(n, ': ', x[[n]], '\n', sep = '')

R-package/R/xgb.train.R

@@ -393,7 +393,6 @@ xgb.train <- function(params = list(), data, nrounds, watchlist = list(),
  # Note: it might look like these aren't used, but they need to be defined in this
  # environment for the callbacks for work correctly.
  num_class <- max(as.numeric(NVL(params[['num_class']], 1)), 1) # nolint
- num_parallel_tree <- max(as.numeric(NVL(params[['num_parallel_tree']], 1)), 1) # nolint
 
  if (is_update && nrounds > niter_init)
  stop("nrounds cannot be larger than ", niter_init, " (nrounds of xgb_model)")

R-package/demo/predict_first_ntree.R

@@ -15,7 +15,7 @@ cat('start testing prediction from first n trees\n')
 labels <- getinfo(dtest, 'label')
 
 ### predict using first 1 tree
-ypred1 <- predict(bst, dtest, ntreelimit = 1)
+ypred1 <- predict(bst, dtest, iterationrange = c(1, 1))
 # by default, we predict using all the trees
 ypred2 <- predict(bst, dtest)