diff --git a/.github/workflows/r_tests.yml b/.github/workflows/r_tests.yml
index 917245ec6..d004ab15c 100644
--- a/.github/workflows/r_tests.yml
+++ b/.github/workflows/r_tests.yml
@@ -54,7 +54,6 @@ jobs:
matrix:
config:
- {os: windows-latest, r: 'release', compiler: 'mingw', build: 'autotools'}
- - {os: windows-latest, r: '4.3.0', compiler: 'msvc', build: 'cmake'}
env:
R_REMOTES_NO_ERRORS_FROM_WARNINGS: true
RSPM: ${{ matrix.config.rspm }}
diff --git a/R-package/DESCRIPTION b/R-package/DESCRIPTION
index 7c01d50c6..bbaf3e75d 100644
--- a/R-package/DESCRIPTION
+++ b/R-package/DESCRIPTION
@@ -65,6 +65,6 @@ Imports:
data.table (>= 1.9.6),
jsonlite (>= 1.0)
Roxygen: list(markdown = TRUE)
-RoxygenNote: 7.2.3
+RoxygenNote: 7.3.0
Encoding: UTF-8
SystemRequirements: GNU make, C++17
diff --git a/R-package/NAMESPACE b/R-package/NAMESPACE
index a29c9b1e0..398b0da5a 100644
--- a/R-package/NAMESPACE
+++ b/R-package/NAMESPACE
@@ -1,5 +1,6 @@
# Generated by roxygen2: do not edit by hand
+S3method("[",xgb.Booster)
S3method("[",xgb.DMatrix)
S3method("dimnames<-",xgb.DMatrix)
S3method(coef,xgb.Booster)
@@ -7,6 +8,7 @@ S3method(dim,xgb.DMatrix)
S3method(dimnames,xgb.DMatrix)
S3method(getinfo,xgb.Booster)
S3method(getinfo,xgb.DMatrix)
+S3method(length,xgb.Booster)
S3method(predict,xgb.Booster)
S3method(print,xgb.Booster)
S3method(print,xgb.DMatrix)
@@ -62,6 +64,7 @@ export(xgb.plot.tree)
export(xgb.save)
export(xgb.save.raw)
export(xgb.set.config)
+export(xgb.slice.Booster)
export(xgb.train)
export(xgboost)
import(methods)
diff --git a/R-package/R/callbacks.R b/R-package/R/callbacks.R
index b3d6bdb1a..02e0a7cd4 100644
--- a/R-package/R/callbacks.R
+++ b/R-package/R/callbacks.R
@@ -280,7 +280,6 @@ cb.reset.parameters <- function(new_params) {
#' \code{iteration},
#' \code{begin_iteration},
#' \code{end_iteration},
-#' \code{num_parallel_tree}.
#'
#' @seealso
#' \code{\link{callbacks}},
@@ -291,7 +290,6 @@ cb.early.stop <- function(stopping_rounds, maximize = FALSE,
metric_name = NULL, verbose = TRUE) {
# state variables
best_iteration <- -1
- best_ntreelimit <- -1
best_score <- Inf
best_msg <- NULL
metric_idx <- 1
@@ -358,12 +356,10 @@ cb.early.stop <- function(stopping_rounds, maximize = FALSE,
# If the difference is due to floating-point truncation, update best_score
best_score <- attr_best_score
}
- xgb.attr(env$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$index, ] <- pr
} else {
diff --git a/R-package/R/utils.R b/R-package/R/utils.R
index 945d86132..e8ae787fc 100644
--- a/R-package/R/utils.R
+++ b/R-package/R/utils.R
@@ -208,7 +208,7 @@ xgb.iter.eval <- function(bst, watchlist, iter, feval) {
res <- sapply(seq_along(watchlist), function(j) {
w <- watchlist[[j]]
## predict using all trees
- preds <- predict(bst, w, outputmargin = TRUE, iterationrange = c(1, 1))
+ preds <- predict(bst, w, outputmargin = TRUE, iterationrange = "all")
eval_res <- feval(preds, w)
out <- eval_res$value
names(out) <- paste0(evnames[j], "-", eval_res$metric)
diff --git a/R-package/R/xgb.Booster.R b/R-package/R/xgb.Booster.R
index cee7e9fc5..7613c9152 100644
--- a/R-package/R/xgb.Booster.R
+++ b/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 +280,21 @@ 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)) {
+ stopifnot(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 +313,6 @@ 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))
)
@@ -343,24 +343,24 @@ predict.xgb.Booster <- function(object, newdata, missing = NA, outputmargin = FA
)
names(predts) <- c("shape", "results")
shape <- predts$shape
- ret <- predts$results
+ arr <- predts$results
- n_ret <- length(ret)
+ n_ret <- length(arr)
n_row <- nrow(newdata)
if (n_row != shape[1]) {
stop("Incorrect predict shape.")
}
- arr <- array(data = ret, dim = rev(shape))
+ .Call(XGSetArrayDimInplace_R, arr, rev(shape))
cnames <- if (!is.null(colnames(newdata))) c(colnames(newdata), "BIAS") else NULL
n_groups <- shape[2]
## Needed regardless of whether strict shape is being used.
if (predcontrib) {
- dimnames(arr) <- list(cnames, NULL, NULL)
+ .Call(XGSetArrayDimNamesInplace_R, arr, list(cnames, NULL, NULL))
} else if (predinteraction) {
- dimnames(arr) <- list(cnames, cnames, NULL, NULL)
+ .Call(XGSetArrayDimNamesInplace_R, arr, list(cnames, cnames, NULL, NULL))
}
if (strict_shape) {
return(arr) # strict shape is calculated by libxgboost uniformly.
@@ -368,43 +368,51 @@ predict.xgb.Booster <- function(object, newdata, missing = NA, outputmargin = FA
if (predleaf) {
## Predict leaf
- arr <- if (n_ret == n_row) {
- matrix(arr, ncol = 1)
+ if (n_ret == n_row) {
+ .Call(XGSetArrayDimInplace_R, arr, c(n_row, 1L))
} else {
- matrix(arr, nrow = n_row, byrow = TRUE)
+ arr <- matrix(arr, nrow = n_row, byrow = TRUE)
}
} else if (predcontrib) {
## Predict contribution
arr <- aperm(a = arr, perm = c(2, 3, 1)) # [group, row, col]
- arr <- if (n_ret == n_row) {
- matrix(arr, ncol = 1, dimnames = list(NULL, cnames))
+ if (n_ret == n_row) {
+ .Call(XGSetArrayDimInplace_R, arr, c(n_row, 1L))
+ .Call(XGSetArrayDimNamesInplace_R, arr, list(NULL, cnames))
} else if (n_groups != 1) {
## turns array into list of matrices
- lapply(seq_len(n_groups), function(g) arr[g, , ])
+ arr <- lapply(seq_len(n_groups), function(g) arr[g, , ])
} else {
## remove the first axis (group)
- dn <- dimnames(arr)
- matrix(arr[1, , ], nrow = dim(arr)[2], ncol = dim(arr)[3], dimnames = c(dn[2], dn[3]))
+ newdim <- dim(arr)[2:3]
+ newdn <- dimnames(arr)[2:3]
+ arr <- arr[1, , ]
+ .Call(XGSetArrayDimInplace_R, arr, newdim)
+ .Call(XGSetArrayDimNamesInplace_R, arr, newdn)
}
} else if (predinteraction) {
## Predict interaction
arr <- aperm(a = arr, perm = c(3, 4, 1, 2)) # [group, row, col, col]
- arr <- if (n_ret == n_row) {
- matrix(arr, ncol = 1, dimnames = list(NULL, cnames))
+ if (n_ret == n_row) {
+ .Call(XGSetArrayDimInplace_R, arr, c(n_row, 1L))
+ .Call(XGSetArrayDimNamesInplace_R, arr, list(NULL, cnames))
} else if (n_groups != 1) {
## turns array into list of matrices
- lapply(seq_len(n_groups), function(g) arr[g, , , ])
+ arr <- lapply(seq_len(n_groups), function(g) arr[g, , , ])
} else {
## remove the first axis (group)
arr <- arr[1, , , , drop = FALSE]
- array(arr, dim = dim(arr)[2:4], dimnames(arr)[2:4])
+ newdim <- dim(arr)[2:4]
+ newdn <- dimnames(arr)[2:4]
+ .Call(XGSetArrayDimInplace_R, arr, newdim)
+ .Call(XGSetArrayDimNamesInplace_R, arr, newdn)
}
} else {
## Normal prediction
- arr <- if (reshape && n_groups != 1) {
- matrix(arr, ncol = n_groups, byrow = TRUE)
+ if (reshape && n_groups != 1) {
+ arr <- matrix(arr, ncol = n_groups, byrow = TRUE)
} else {
- as.vector(ret)
+ .Call(XGSetArrayDimInplace_R, arr, NULL)
}
}
return(arr)
@@ -492,7 +500,7 @@ 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)
}
}
@@ -685,16 +693,94 @@ setinfo.xgb.Booster <- function(object, name, info) {
}
#' @title Get number of boosting in a fitted booster
-#' @param model A fitted `xgb.Booster` model.
+#' @param model,x A fitted `xgb.Booster` model.
#' @return The number of rounds saved in the model, as an integer.
#' @details Note that setting booster parameters related to training
#' continuation / updates through \link{xgb.parameters<-} will reset the
#' number of rounds to zero.
#' @export
+#' @rdname xgb.get.num.boosted.rounds
xgb.get.num.boosted.rounds <- function(model) {
return(.Call(XGBoosterBoostedRounds_R, xgb.get.handle(model)))
}
+#' @rdname xgb.get.num.boosted.rounds
+#' @export
+length.xgb.Booster <- function(x) {
+ return(xgb.get.num.boosted.rounds(x))
+}
+
+#' @title Slice Booster by Rounds
+#' @description Creates a new booster including only a selected range of rounds / iterations
+#' from an existing booster, as given by the sequence `seq(start, end, step)`.
+#' @details Note that any R attributes that the booster might have, will not be copied into
+#' the resulting object.
+#' @param model,x A fitted `xgb.Booster` object, which is to be sliced by taking only a subset
+#' of its rounds / iterations.
+#' @param start Start of the slice (base-1 and inclusive, like R's \link{seq}).
+#' @param end End of the slice (base-1 and inclusive, like R's \link{seq}).
+#'
+#' Passing a value of zero here is equivalent to passing the full number of rounds in the
+#' booster object.
+#' @param step Step size of the slice. Passing '1' will take every round in the sequence defined by
+#' `(start, end)`, while passing '2' will take every second value, and so on.
+#' @return A sliced booster object containing only the requested rounds.
+#' @examples
+#' data(mtcars)
+#' y <- mtcars$mpg
+#' x <- as.matrix(mtcars[, -1])
+#' dm <- xgb.DMatrix(x, label = y, nthread = 1)
+#' model <- xgb.train(data = dm, params = list(nthread = 1), nrounds = 5)
+#' model_slice <- xgb.slice.Booster(model, 1, 3)
+#' # Prediction for first three rounds
+#' predict(model, x, predleaf = TRUE)[, 1:3]
+#'
+#' # The new model has only those rounds, so
+#' # a full prediction from it is equivalent
+#' predict(model_slice, x, predleaf = TRUE)
+#' @export
+#' @rdname xgb.slice.Booster
+xgb.slice.Booster <- function(model, start, end = xgb.get.num.boosted.rounds(model), step = 1L) {
+ # This makes the slice mimic the behavior of R's 'seq',
+ # which truncates on the end of the slice when the step
+ # doesn't reach it.
+ if (end > start && step > 1) {
+ d <- (end - start + 1) / step
+ if (d != floor(d)) {
+ end <- start + step * ceiling(d) - 1
+ }
+ }
+ return(
+ .Call(
+ XGBoosterSlice_R,
+ xgb.get.handle(model),
+ start - 1,
+ end,
+ step
+ )
+ )
+}
+
+#' @export
+#' @rdname xgb.slice.Booster
+#' @param i The indices - must be an increasing sequence as generated by e.g. `seq(...)`.
+`[.xgb.Booster` <- function(x, i) {
+ if (missing(i)) {
+ return(xgb.slice.Booster(x, 1, 0))
+ }
+ if (length(i) == 1) {
+ return(xgb.slice.Booster(x, i, i))
+ }
+ steps <- diff(i)
+ if (any(steps < 0)) {
+ stop("Can only slice booster with ascending sequences.")
+ }
+ if (length(unique(steps)) > 1) {
+ stop("Can only slice booster with fixed-step sequences.")
+ }
+ return(xgb.slice.Booster(x, i[1L], i[length(i)], steps[1L]))
+}
+
#' @title Get Features Names from Booster
#' @description Returns the feature / variable / column names from a fitted
#' booster object, which are set automatically during the call to \link{xgb.train}
@@ -710,12 +796,6 @@ 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)
diff --git a/R-package/R/xgb.cv.R b/R-package/R/xgb.cv.R
index a960957ca..eb0495631 100644
--- a/R-package/R/xgb.cv.R
+++ b/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 = '')
diff --git a/R-package/R/xgb.importance.R b/R-package/R/xgb.importance.R
index 44f2eb9b3..547d9677b 100644
--- a/R-package/R/xgb.importance.R
+++ b/R-package/R/xgb.importance.R
@@ -113,19 +113,12 @@
#' xgb.importance(model = mbst)
#'
#' @export
-xgb.importance <- function(feature_names = NULL, model = NULL, trees = NULL,
+xgb.importance <- function(model = NULL, feature_names = getinfo(model, "feature_name"), trees = NULL,
data = NULL, label = NULL, target = NULL) {
if (!(is.null(data) && is.null(label) && is.null(target)))
warning("xgb.importance: parameters 'data', 'label' and 'target' are deprecated")
- if (is.null(feature_names)) {
- model_feature_names <- xgb.feature_names(model)
- if (NROW(model_feature_names)) {
- feature_names <- model_feature_names
- }
- }
-
if (!(is.null(feature_names) || is.character(feature_names)))
stop("feature_names: Has to be a character vector")
diff --git a/R-package/R/xgb.model.dt.tree.R b/R-package/R/xgb.model.dt.tree.R
index df0e672a9..ff416b73e 100644
--- a/R-package/R/xgb.model.dt.tree.R
+++ b/R-package/R/xgb.model.dt.tree.R
@@ -2,11 +2,8 @@
#'
#' Parse a boosted tree model text dump into a `data.table` structure.
#'
-#' @param feature_names Character vector of feature names. If the model already
-#' contains feature names, those will be used when \code{feature_names=NULL} (default value).
-#'
-#' Note that, if the model already contains feature names, it's \bold{not} possible to override them here.
-#' @param model Object of class `xgb.Booster`.
+#' @param model Object of class `xgb.Booster`. If it contains feature names (they can be set through
+#' \link{setinfo}), they will be used in the output from this function.
#' @param text Character vector previously generated by the function [xgb.dump()]
#' (called with parameter `with_stats = TRUE`). `text` takes precedence over `model`.
#' @param trees An integer vector of tree indices that should be used.
@@ -58,7 +55,7 @@
#'
#' # This bst model already has feature_names stored with it, so those would be used when
#' # feature_names is not set:
-#' (dt <- xgb.model.dt.tree(model = bst))
+#' dt <- xgb.model.dt.tree(bst)
#'
#' # How to match feature names of splits that are following a current 'Yes' branch:
#' merge(
@@ -69,7 +66,7 @@
#' ]
#'
#' @export
-xgb.model.dt.tree <- function(feature_names = NULL, model = NULL, text = NULL,
+xgb.model.dt.tree <- function(model = NULL, text = NULL,
trees = NULL, use_int_id = FALSE, ...) {
check.deprecation(...)
@@ -79,24 +76,15 @@ xgb.model.dt.tree <- function(feature_names = NULL, model = NULL, text = NULL,
" (or NULL if 'model' was provided).")
}
- model_feature_names <- NULL
- if (inherits(model, "xgb.Booster")) {
- model_feature_names <- xgb.feature_names(model)
- if (NROW(model_feature_names) && !is.null(feature_names)) {
- stop("'model' contains feature names. Cannot override them.")
- }
- }
- if (is.null(feature_names) && !is.null(model) && !is.null(model_feature_names))
- feature_names <- model_feature_names
-
- if (!(is.null(feature_names) || is.character(feature_names))) {
- stop("feature_names: must be a character vector")
- }
-
if (!(is.null(trees) || is.numeric(trees))) {
stop("trees: must be a vector of integers.")
}
+ feature_names <- NULL
+ if (inherits(model, "xgb.Booster")) {
+ feature_names <- xgb.feature_names(model)
+ }
+
from_text <- TRUE
if (is.null(text)) {
text <- xgb.dump(model = model, with_stats = TRUE)
@@ -134,7 +122,7 @@ xgb.model.dt.tree <- function(feature_names = NULL, model = NULL, text = NULL,
branch_rx_w_names <- paste0("\\d+:\\[(.+)<(", anynumber_regex, ")\\] yes=(\\d+),no=(\\d+),missing=(\\d+),",
"gain=(", anynumber_regex, "),cover=(", anynumber_regex, ")")
text_has_feature_names <- FALSE
- if (NROW(model_feature_names)) {
+ if (NROW(feature_names)) {
branch_rx <- branch_rx_w_names
text_has_feature_names <- TRUE
} else {
@@ -148,9 +136,6 @@ xgb.model.dt.tree <- function(feature_names = NULL, model = NULL, text = NULL,
}
}
}
- if (text_has_feature_names && is.null(model) && !is.null(feature_names)) {
- stop("'text' contains feature names. Cannot override them.")
- }
branch_cols <- c("Feature", "Split", "Yes", "No", "Missing", "Gain", "Cover")
td[
isLeaf == FALSE,
diff --git a/R-package/R/xgb.plot.multi.trees.R b/R-package/R/xgb.plot.multi.trees.R
index 88616cfb7..e6d678ee7 100644
--- a/R-package/R/xgb.plot.multi.trees.R
+++ b/R-package/R/xgb.plot.multi.trees.R
@@ -62,13 +62,13 @@
#' }
#'
#' @export
-xgb.plot.multi.trees <- function(model, feature_names = NULL, features_keep = 5, plot_width = NULL, plot_height = NULL,
+xgb.plot.multi.trees <- function(model, features_keep = 5, plot_width = NULL, plot_height = NULL,
render = TRUE, ...) {
if (!requireNamespace("DiagrammeR", quietly = TRUE)) {
stop("DiagrammeR is required for xgb.plot.multi.trees")
}
check.deprecation(...)
- tree.matrix <- xgb.model.dt.tree(feature_names = feature_names, model = model)
+ tree.matrix <- xgb.model.dt.tree(model = model)
# first number of the path represents the tree, then the following numbers are related to the path to follow
# root init
diff --git a/R-package/R/xgb.plot.tree.R b/R-package/R/xgb.plot.tree.R
index c75a42e84..5ed1e70f6 100644
--- a/R-package/R/xgb.plot.tree.R
+++ b/R-package/R/xgb.plot.tree.R
@@ -2,9 +2,8 @@
#'
#' Read a tree model text dump and plot the model.
#'
-#' @param feature_names Character vector used to overwrite the feature names
-#' of the model. The default (`NULL`) uses the original feature names.
-#' @param model Object of class `xgb.Booster`.
+#' @param model Object of class `xgb.Booster`. If it contains feature names (they can be set through
+#' \link{setinfo}), they will be used in the output from this function.
#' @param trees An integer vector of tree indices that should be used.
#' The default (`NULL`) uses all trees.
#' Useful, e.g., in multiclass classification to get only
@@ -103,7 +102,7 @@
#' }
#'
#' @export
-xgb.plot.tree <- function(feature_names = NULL, model = NULL, trees = NULL, plot_width = NULL, plot_height = NULL,
+xgb.plot.tree <- function(model = NULL, trees = NULL, plot_width = NULL, plot_height = NULL,
render = TRUE, show_node_id = FALSE, style = c("R", "xgboost"), ...) {
check.deprecation(...)
if (!inherits(model, "xgb.Booster")) {
@@ -120,17 +119,12 @@ xgb.plot.tree <- function(feature_names = NULL, model = NULL, trees = NULL, plot
if (NROW(trees) != 1L || !render || show_node_id) {
stop("style='xgboost' is only supported for single, rendered tree, without node IDs.")
}
- if (!is.null(feature_names)) {
- stop(
- "style='xgboost' cannot override 'feature_names'. Will automatically take them from the model."
- )
- }
txt <- xgb.dump(model, dump_format = "dot")
return(DiagrammeR::grViz(txt[[trees + 1]], width = plot_width, height = plot_height))
}
- dt <- xgb.model.dt.tree(feature_names = feature_names, model = model, trees = trees)
+ dt <- xgb.model.dt.tree(model = model, trees = trees)
dt[, label := paste0(Feature, "\nCover: ", Cover, ifelse(Feature == "Leaf", "\nValue: ", "\nGain: "), Gain)]
if (show_node_id)
diff --git a/R-package/R/xgb.train.R b/R-package/R/xgb.train.R
index a313ed32f..f0f2332b5 100644
--- a/R-package/R/xgb.train.R
+++ b/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)")
diff --git a/R-package/demo/predict_first_ntree.R b/R-package/demo/predict_first_ntree.R
index 02c168b77..179c18c70 100644
--- a/R-package/demo/predict_first_ntree.R
+++ b/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)
diff --git a/R-package/man/cb.cv.predict.Rd b/R-package/man/cb.cv.predict.Rd
index ded899e8a..4cabac1c9 100644
--- a/R-package/man/cb.cv.predict.Rd
+++ b/R-package/man/cb.cv.predict.Rd
@@ -35,8 +35,6 @@ Callback function expects the following values to be set in its calling frame:
\code{data},
\code{end_iteration},
\code{params},
-\code{num_parallel_tree},
-\code{num_class}.
}
\seealso{
\code{\link{callbacks}}
diff --git a/R-package/man/cb.early.stop.Rd b/R-package/man/cb.early.stop.Rd
index 7b6efa842..7cd51a3ce 100644
--- a/R-package/man/cb.early.stop.Rd
+++ b/R-package/man/cb.early.stop.Rd
@@ -55,7 +55,6 @@ Callback function expects the following values to be set in its calling frame:
\code{iteration},
\code{begin_iteration},
\code{end_iteration},
-\code{num_parallel_tree}.
}
\seealso{
\code{\link{callbacks}},
diff --git a/R-package/man/predict.xgb.Booster.Rd b/R-package/man/predict.xgb.Booster.Rd
index 66194c64f..7a6dd6c13 100644
--- a/R-package/man/predict.xgb.Booster.Rd
+++ b/R-package/man/predict.xgb.Booster.Rd
@@ -9,7 +9,6 @@
newdata,
missing = NA,
outputmargin = FALSE,
- ntreelimit = NULL,
predleaf = FALSE,
predcontrib = FALSE,
approxcontrib = FALSE,
@@ -36,8 +35,6 @@ missing values in data (e.g., 0 or some other extreme value).}
sum of predictions from boosting iterations' results. E.g., setting \code{outputmargin=TRUE} for
logistic regression would return log-odds instead of probabilities.}
-\item{ntreelimit}{Deprecated, use \code{iterationrange} instead.}
-
\item{predleaf}{Whether to predict pre-tree leaf indices.}
\item{predcontrib}{Whether to return feature contributions to individual predictions (see Details).}
@@ -53,11 +50,18 @@ or \code{predinteraction} is \code{TRUE}.}
\item{training}{Whether the predictions are used for training. For dart booster,
training predicting will perform dropout.}
-\item{iterationrange}{Specifies which trees are used in prediction. For
-example, take a random forest with 100 rounds.
-With \code{iterationrange=c(1, 21)}, only the trees built during \verb{[1, 21)} (half open set)
-rounds are used in this prediction. The index is 1-based just like an R vector. When set
-to \code{c(1, 1)}, XGBoost will use all trees.}
+\item{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 \code{seq} - i.e.
+base-1 indexing, and inclusive of both ends).
+
+\if{html}{\out{}}\preformatted{ 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.
+}\if{html}{\out{
}}}
\item{strict_shape}{Default is \code{FALSE}. When set to \code{TRUE}, the output
type and shape of predictions are invariant to the model type.}
@@ -145,7 +149,7 @@ bst <- xgb.train(
# 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
@@ -216,7 +220,7 @@ str(pred)
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)
}
diff --git a/R-package/man/xgb.cv.Rd b/R-package/man/xgb.cv.Rd
index 2d8508c4d..9f6103a52 100644
--- a/R-package/man/xgb.cv.Rd
+++ b/R-package/man/xgb.cv.Rd
@@ -135,7 +135,6 @@ It is created by the \code{\link{cb.evaluation.log}} callback.
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
diff --git a/R-package/man/xgb.get.num.boosted.rounds.Rd b/R-package/man/xgb.get.num.boosted.rounds.Rd
index 74c94d95b..551dc4a83 100644
--- a/R-package/man/xgb.get.num.boosted.rounds.Rd
+++ b/R-package/man/xgb.get.num.boosted.rounds.Rd
@@ -2,12 +2,15 @@
% Please edit documentation in R/xgb.Booster.R
\name{xgb.get.num.boosted.rounds}
\alias{xgb.get.num.boosted.rounds}
+\alias{length.xgb.Booster}
\title{Get number of boosting in a fitted booster}
\usage{
xgb.get.num.boosted.rounds(model)
+
+\method{length}{xgb.Booster}(x)
}
\arguments{
-\item{model}{A fitted \code{xgb.Booster} model.}
+\item{model, x}{A fitted \code{xgb.Booster} model.}
}
\value{
The number of rounds saved in the model, as an integer.
diff --git a/R-package/man/xgb.importance.Rd b/R-package/man/xgb.importance.Rd
index fca1b70c4..73b91e8b4 100644
--- a/R-package/man/xgb.importance.Rd
+++ b/R-package/man/xgb.importance.Rd
@@ -5,8 +5,8 @@
\title{Feature importance}
\usage{
xgb.importance(
- feature_names = NULL,
model = NULL,
+ feature_names = getinfo(model, "feature_name"),
trees = NULL,
data = NULL,
label = NULL,
@@ -14,11 +14,11 @@ xgb.importance(
)
}
\arguments{
+\item{model}{Object of class \code{xgb.Booster}.}
+
\item{feature_names}{Character vector used to overwrite the feature names
of the model. The default is \code{NULL} (use original feature names).}
-\item{model}{Object of class \code{xgb.Booster}.}
-
\item{trees}{An integer vector of tree indices that should be included
into the importance calculation (only for the "gbtree" booster).
The default (\code{NULL}) parses all trees.
diff --git a/R-package/man/xgb.model.dt.tree.Rd b/R-package/man/xgb.model.dt.tree.Rd
index e63bd4b10..75f1cd0f4 100644
--- a/R-package/man/xgb.model.dt.tree.Rd
+++ b/R-package/man/xgb.model.dt.tree.Rd
@@ -5,7 +5,6 @@
\title{Parse model text dump}
\usage{
xgb.model.dt.tree(
- feature_names = NULL,
model = NULL,
text = NULL,
trees = NULL,
@@ -14,13 +13,8 @@ xgb.model.dt.tree(
)
}
\arguments{
-\item{feature_names}{Character vector of feature names. If the model already
-contains feature names, those will be used when \code{feature_names=NULL} (default value).
-
-\if{html}{\out{}}\preformatted{ Note that, if the model already contains feature names, it's \\bold\{not\} possible to override them here.
-}\if{html}{\out{
}}}
-
-\item{model}{Object of class \code{xgb.Booster}.}
+\item{model}{Object of class \code{xgb.Booster}. If it contains feature names (they can be set through
+\link{setinfo}), they will be used in the output from this function.}
\item{text}{Character vector previously generated by the function \code{\link[=xgb.dump]{xgb.dump()}}
(called with parameter \code{with_stats = TRUE}). \code{text} takes precedence over \code{model}.}
@@ -81,7 +75,7 @@ bst <- xgboost(
# This bst model already has feature_names stored with it, so those would be used when
# feature_names is not set:
-(dt <- xgb.model.dt.tree(model = bst))
+dt <- xgb.model.dt.tree(bst)
# How to match feature names of splits that are following a current 'Yes' branch:
merge(
diff --git a/R-package/man/xgb.plot.multi.trees.Rd b/R-package/man/xgb.plot.multi.trees.Rd
index d98a3482c..7fa75c85d 100644
--- a/R-package/man/xgb.plot.multi.trees.Rd
+++ b/R-package/man/xgb.plot.multi.trees.Rd
@@ -6,7 +6,6 @@
\usage{
xgb.plot.multi.trees(
model,
- feature_names = NULL,
features_keep = 5,
plot_width = NULL,
plot_height = NULL,
@@ -15,10 +14,8 @@ xgb.plot.multi.trees(
)
}
\arguments{
-\item{model}{Object of class \code{xgb.Booster}.}
-
-\item{feature_names}{Character vector used to overwrite the feature names
-of the model. The default (\code{NULL}) uses the original feature names.}
+\item{model}{Object of class \code{xgb.Booster}. If it contains feature names (they can be set through
+\link{setinfo}), they will be used in the output from this function.}
\item{features_keep}{Number of features to keep in each position of the multi trees,
by default 5.}
diff --git a/R-package/man/xgb.plot.tree.Rd b/R-package/man/xgb.plot.tree.Rd
index a09bb7183..69d37301d 100644
--- a/R-package/man/xgb.plot.tree.Rd
+++ b/R-package/man/xgb.plot.tree.Rd
@@ -5,7 +5,6 @@
\title{Plot boosted trees}
\usage{
xgb.plot.tree(
- feature_names = NULL,
model = NULL,
trees = NULL,
plot_width = NULL,
@@ -17,10 +16,8 @@ xgb.plot.tree(
)
}
\arguments{
-\item{feature_names}{Character vector used to overwrite the feature names
-of the model. The default (\code{NULL}) uses the original feature names.}
-
-\item{model}{Object of class \code{xgb.Booster}.}
+\item{model}{Object of class \code{xgb.Booster}. If it contains feature names (they can be set through
+\link{setinfo}), they will be used in the output from this function.}
\item{trees}{An integer vector of tree indices that should be used.
The default (\code{NULL}) uses all trees.
diff --git a/R-package/man/xgb.slice.Booster.Rd b/R-package/man/xgb.slice.Booster.Rd
new file mode 100644
index 000000000..759139901
--- /dev/null
+++ b/R-package/man/xgb.slice.Booster.Rd
@@ -0,0 +1,57 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/xgb.Booster.R
+\name{xgb.slice.Booster}
+\alias{xgb.slice.Booster}
+\alias{[.xgb.Booster}
+\title{Slice Booster by Rounds}
+\usage{
+xgb.slice.Booster(
+ model,
+ start,
+ end = xgb.get.num.boosted.rounds(model),
+ step = 1L
+)
+
+\method{[}{xgb.Booster}(x, i)
+}
+\arguments{
+\item{model, x}{A fitted \code{xgb.Booster} object, which is to be sliced by taking only a subset
+of its rounds / iterations.}
+
+\item{start}{Start of the slice (base-1 and inclusive, like R's \link{seq}).}
+
+\item{end}{End of the slice (base-1 and inclusive, like R's \link{seq}).
+
+Passing a value of zero here is equivalent to passing the full number of rounds in the
+booster object.}
+
+\item{step}{Step size of the slice. Passing '1' will take every round in the sequence defined by
+\verb{(start, end)}, while passing '2' will take every second value, and so on.}
+
+\item{i}{The indices - must be an increasing sequence as generated by e.g. \code{seq(...)}.}
+}
+\value{
+A sliced booster object containing only the requested rounds.
+}
+\description{
+Creates a new booster including only a selected range of rounds / iterations
+from an existing booster, as given by the sequence \code{seq(start, end, step)}.
+}
+\details{
+Note that any R attributes that the booster might have, will not be copied into
+the resulting object.
+}
+\examples{
+data(mtcars)
+y <- mtcars$mpg
+x <- as.matrix(mtcars[, -1])
+dm <- xgb.DMatrix(x, label = y, nthread = 1)
+model <- xgb.train(data = dm, params = list(nthread = 1), nrounds = 5)
+model_slice <- xgb.slice.Booster(model, 1, 3)
+# Prediction for first three rounds
+predict(model, x, predleaf = TRUE)[, 1:3]
+
+# The new model has only those rounds, so
+# a full prediction from it is equivalent
+predict(model_slice, x, predleaf = TRUE)
+}
diff --git a/R-package/src/init.c b/R-package/src/init.c
index 81c28c401..fff5d9f90 100644
--- a/R-package/src/init.c
+++ b/R-package/src/init.c
@@ -42,6 +42,8 @@ extern SEXP XGBoosterSetAttr_R(SEXP, SEXP, SEXP);
extern SEXP XGBoosterSetParam_R(SEXP, SEXP, SEXP);
extern SEXP XGBoosterUpdateOneIter_R(SEXP, SEXP, SEXP);
extern SEXP XGCheckNullPtr_R(SEXP);
+extern SEXP XGSetArrayDimInplace_R(SEXP, SEXP);
+extern SEXP XGSetArrayDimNamesInplace_R(SEXP, SEXP);
extern SEXP XGDMatrixCreateFromCSC_R(SEXP, SEXP, SEXP, SEXP, SEXP, SEXP);
extern SEXP XGDMatrixCreateFromCSR_R(SEXP, SEXP, SEXP, SEXP, SEXP, SEXP);
extern SEXP XGDMatrixCreateFromFile_R(SEXP, SEXP);
@@ -62,6 +64,7 @@ extern SEXP XGDMatrixSliceDMatrix_R(SEXP, SEXP);
extern SEXP XGBSetGlobalConfig_R(SEXP);
extern SEXP XGBGetGlobalConfig_R(void);
extern SEXP XGBoosterFeatureScore_R(SEXP, SEXP);
+extern SEXP XGBoosterSlice_R(SEXP, SEXP, SEXP, SEXP);
static const R_CallMethodDef CallEntries[] = {
{"XGDuplicate_R", (DL_FUNC) &XGDuplicate_R, 1},
@@ -90,6 +93,8 @@ static const R_CallMethodDef CallEntries[] = {
{"XGBoosterSetParam_R", (DL_FUNC) &XGBoosterSetParam_R, 3},
{"XGBoosterUpdateOneIter_R", (DL_FUNC) &XGBoosterUpdateOneIter_R, 3},
{"XGCheckNullPtr_R", (DL_FUNC) &XGCheckNullPtr_R, 1},
+ {"XGSetArrayDimInplace_R", (DL_FUNC) &XGSetArrayDimInplace_R, 2},
+ {"XGSetArrayDimNamesInplace_R", (DL_FUNC) &XGSetArrayDimNamesInplace_R, 2},
{"XGDMatrixCreateFromCSC_R", (DL_FUNC) &XGDMatrixCreateFromCSC_R, 6},
{"XGDMatrixCreateFromCSR_R", (DL_FUNC) &XGDMatrixCreateFromCSR_R, 6},
{"XGDMatrixCreateFromFile_R", (DL_FUNC) &XGDMatrixCreateFromFile_R, 2},
@@ -110,6 +115,7 @@ static const R_CallMethodDef CallEntries[] = {
{"XGBSetGlobalConfig_R", (DL_FUNC) &XGBSetGlobalConfig_R, 1},
{"XGBGetGlobalConfig_R", (DL_FUNC) &XGBGetGlobalConfig_R, 0},
{"XGBoosterFeatureScore_R", (DL_FUNC) &XGBoosterFeatureScore_R, 2},
+ {"XGBoosterSlice_R", (DL_FUNC) &XGBoosterSlice_R, 4},
{NULL, NULL, 0}
};
diff --git a/R-package/src/xgboost_R.cc b/R-package/src/xgboost_R.cc
index 63f36ad6a..1d01b9aae 100644
--- a/R-package/src/xgboost_R.cc
+++ b/R-package/src/xgboost_R.cc
@@ -263,6 +263,16 @@ XGB_DLL SEXP XGCheckNullPtr_R(SEXP handle) {
return Rf_ScalarLogical(R_ExternalPtrAddr(handle) == nullptr);
}
+XGB_DLL SEXP XGSetArrayDimInplace_R(SEXP arr, SEXP dims) {
+ Rf_setAttrib(arr, R_DimSymbol, dims);
+ return R_NilValue;
+}
+
+XGB_DLL SEXP XGSetArrayDimNamesInplace_R(SEXP arr, SEXP dim_names) {
+ Rf_setAttrib(arr, R_DimNamesSymbol, dim_names);
+ return R_NilValue;
+}
+
namespace {
void _DMatrixFinalizer(SEXP ext) {
R_API_BEGIN();
@@ -1279,3 +1289,18 @@ XGB_DLL SEXP XGBoosterFeatureScore_R(SEXP handle, SEXP json_config) {
return r_out;
}
+
+XGB_DLL SEXP XGBoosterSlice_R(SEXP handle, SEXP begin_layer, SEXP end_layer, SEXP step) {
+ SEXP out = Rf_protect(XGBMakeEmptyAltrep());
+ R_API_BEGIN();
+ BoosterHandle handle_out = nullptr;
+ CHECK_CALL(XGBoosterSlice(R_ExternalPtrAddr(handle),
+ Rf_asInteger(begin_layer),
+ Rf_asInteger(end_layer),
+ Rf_asInteger(step),
+ &handle_out));
+ XGBAltrepSetPointer(out, handle_out);
+ R_API_END();
+ Rf_unprotect(1);
+ return out;
+}
diff --git a/R-package/src/xgboost_R.h b/R-package/src/xgboost_R.h
index 79d441792..ec30dbada 100644
--- a/R-package/src/xgboost_R.h
+++ b/R-package/src/xgboost_R.h
@@ -23,6 +23,22 @@
*/
XGB_DLL SEXP XGCheckNullPtr_R(SEXP handle);
+/*!
+ * \brief set the dimensions of an array in-place
+ * \param arr
+ * \param dims dimensions to set to the array
+ * \return NULL value
+ */
+XGB_DLL SEXP XGSetArrayDimInplace_R(SEXP arr, SEXP dims);
+
+/*!
+ * \brief set the names of the dimensions of an array in-place
+ * \param arr
+ * \param dim_names names for the dimensions to set
+ * \return NULL value
+ */
+XGB_DLL SEXP XGSetArrayDimNamesInplace_R(SEXP arr, SEXP dim_names);
+
/*!
* \brief Set global configuration
* \param json_str a JSON string representing the list of key-value pairs
@@ -386,4 +402,14 @@ XGB_DLL SEXP XGBoosterGetAttrNames_R(SEXP handle);
*/
XGB_DLL SEXP XGBoosterFeatureScore_R(SEXP handle, SEXP json_config);
+/*!
+ * \brief Slice a fitted booster model (by rounds)
+ * \param handle handle to the fitted booster
+ * \param begin_layer start of the slice
+ * \param end_later end of the slice; end_layer=0 is equivalent to end_layer=num_boost_round
+ * \param step step size of the slice
+ * \return The sliced booster with the requested rounds only
+ */
+XGB_DLL SEXP XGBoosterSlice_R(SEXP handle, SEXP begin_layer, SEXP end_layer, SEXP step);
+
#endif // XGBOOST_WRAPPER_R_H_ // NOLINT(*)
diff --git a/R-package/tests/testthat/test_basic.R b/R-package/tests/testthat/test_basic.R
index 8dd934765..03a8ddbe1 100644
--- a/R-package/tests/testthat/test_basic.R
+++ b/R-package/tests/testthat/test_basic.R
@@ -33,15 +33,11 @@ test_that("train and predict binary classification", {
pred <- predict(bst, test$data)
expect_length(pred, 1611)
- pred1 <- predict(bst, train$data, ntreelimit = 1)
+ pred1 <- predict(bst, train$data, iterationrange = c(1, 1))
expect_length(pred1, 6513)
err_pred1 <- sum((pred1 > 0.5) != train$label) / length(train$label)
err_log <- attributes(bst)$evaluation_log[1, train_error]
expect_lt(abs(err_pred1 - err_log), 10e-6)
-
- pred2 <- predict(bst, train$data, iterationrange = c(1, 2))
- expect_length(pred1, 6513)
- expect_equal(pred1, pred2)
})
test_that("parameter validation works", {
@@ -117,8 +113,8 @@ test_that("dart prediction works", {
nrounds = nrounds,
objective = "reg:squarederror"
)
- pred_by_xgboost_0 <- predict(booster_by_xgboost, newdata = d, ntreelimit = 0)
- pred_by_xgboost_1 <- predict(booster_by_xgboost, newdata = d, ntreelimit = nrounds)
+ pred_by_xgboost_0 <- predict(booster_by_xgboost, newdata = d, iterationrange = NULL)
+ pred_by_xgboost_1 <- predict(booster_by_xgboost, newdata = d, iterationrange = c(1, nrounds))
expect_true(all(matrix(pred_by_xgboost_0, byrow = TRUE) == matrix(pred_by_xgboost_1, byrow = TRUE)))
pred_by_xgboost_2 <- predict(booster_by_xgboost, newdata = d, training = TRUE)
@@ -139,8 +135,8 @@ test_that("dart prediction works", {
data = dtrain,
nrounds = nrounds
)
- pred_by_train_0 <- predict(booster_by_train, newdata = dtrain, ntreelimit = 0)
- pred_by_train_1 <- predict(booster_by_train, newdata = dtrain, ntreelimit = nrounds)
+ pred_by_train_0 <- predict(booster_by_train, newdata = dtrain, iterationrange = NULL)
+ pred_by_train_1 <- predict(booster_by_train, newdata = dtrain, iterationrange = c(1, nrounds))
pred_by_train_2 <- predict(booster_by_train, newdata = dtrain, training = TRUE)
expect_true(all(matrix(pred_by_train_0, byrow = TRUE) == matrix(pred_by_xgboost_0, byrow = TRUE)))
@@ -162,7 +158,7 @@ test_that("train and predict softprob", {
)
expect_false(is.null(attributes(bst)$evaluation_log))
expect_lt(attributes(bst)$evaluation_log[, min(train_merror)], 0.025)
- expect_equal(xgb.get.num.boosted.rounds(bst) * 3, xgb.ntree(bst))
+ expect_equal(xgb.get.num.boosted.rounds(bst), 5)
pred <- predict(bst, as.matrix(iris[, -5]))
expect_length(pred, nrow(iris) * 3)
# row sums add up to total probability of 1:
@@ -174,12 +170,12 @@ test_that("train and predict softprob", {
err <- sum(pred_labels != lb) / length(lb)
expect_equal(attributes(bst)$evaluation_log[5, train_merror], err, tolerance = 5e-6)
# manually calculate error at the 1st iteration:
- mpred <- predict(bst, as.matrix(iris[, -5]), reshape = TRUE, ntreelimit = 1)
+ mpred <- predict(bst, as.matrix(iris[, -5]), reshape = TRUE, iterationrange = c(1, 1))
pred_labels <- max.col(mpred) - 1
err <- sum(pred_labels != lb) / length(lb)
expect_equal(attributes(bst)$evaluation_log[1, train_merror], err, tolerance = 5e-6)
- mpred1 <- predict(bst, as.matrix(iris[, -5]), reshape = TRUE, iterationrange = c(1, 2))
+ mpred1 <- predict(bst, as.matrix(iris[, -5]), reshape = TRUE, iterationrange = c(1, 1))
expect_equal(mpred, mpred1)
d <- cbind(
@@ -213,7 +209,7 @@ test_that("train and predict softmax", {
)
expect_false(is.null(attributes(bst)$evaluation_log))
expect_lt(attributes(bst)$evaluation_log[, min(train_merror)], 0.025)
- expect_equal(xgb.get.num.boosted.rounds(bst) * 3, xgb.ntree(bst))
+ expect_equal(xgb.get.num.boosted.rounds(bst), 5)
pred <- predict(bst, as.matrix(iris[, -5]))
expect_length(pred, nrow(iris))
@@ -233,19 +229,15 @@ test_that("train and predict RF", {
watchlist = list(train = xgb.DMatrix(train$data, label = lb))
)
expect_equal(xgb.get.num.boosted.rounds(bst), 1)
- expect_equal(xgb.ntree(bst), 20)
pred <- predict(bst, train$data)
pred_err <- sum((pred > 0.5) != lb) / length(lb)
expect_lt(abs(attributes(bst)$evaluation_log[1, train_error] - pred_err), 10e-6)
# expect_lt(pred_err, 0.03)
- pred <- predict(bst, train$data, ntreelimit = 20)
+ pred <- predict(bst, train$data, iterationrange = c(1, 1))
pred_err_20 <- sum((pred > 0.5) != lb) / length(lb)
expect_equal(pred_err_20, pred_err)
-
- pred1 <- predict(bst, train$data, iterationrange = c(1, 2))
- expect_equal(pred, pred1)
})
test_that("train and predict RF with softprob", {
@@ -261,7 +253,6 @@ test_that("train and predict RF with softprob", {
watchlist = list(train = xgb.DMatrix(as.matrix(iris[, -5]), label = lb))
)
expect_equal(xgb.get.num.boosted.rounds(bst), 15)
- expect_equal(xgb.ntree(bst), 15 * 3 * 4)
# predict for all iterations:
pred <- predict(bst, as.matrix(iris[, -5]), reshape = TRUE)
expect_equal(dim(pred), c(nrow(iris), 3))
@@ -269,7 +260,7 @@ test_that("train and predict RF with softprob", {
err <- sum(pred_labels != lb) / length(lb)
expect_equal(attributes(bst)$evaluation_log[nrounds, train_merror], err, tolerance = 5e-6)
# predict for 7 iterations and adjust for 4 parallel trees per iteration
- pred <- predict(bst, as.matrix(iris[, -5]), reshape = TRUE, ntreelimit = 7 * 4)
+ pred <- predict(bst, as.matrix(iris[, -5]), reshape = TRUE, iterationrange = c(1, 7))
err <- sum((max.col(pred) - 1) != lb) / length(lb)
expect_equal(attributes(bst)$evaluation_log[7, train_merror], err, tolerance = 5e-6)
})
diff --git a/R-package/tests/testthat/test_booster_slicing.R b/R-package/tests/testthat/test_booster_slicing.R
new file mode 100644
index 000000000..711ccd8b6
--- /dev/null
+++ b/R-package/tests/testthat/test_booster_slicing.R
@@ -0,0 +1,67 @@
+context("testing xgb.Booster slicing")
+
+data(agaricus.train, package = "xgboost")
+dm <- xgb.DMatrix(agaricus.train$data, label = agaricus.train$label, nthread = 1)
+# Note: here need large step sizes in order for the predictions
+# to have substantially different leaf assignments on each tree
+model <- xgb.train(
+ params = list(objective = "binary:logistic", nthread = 1, max_depth = 4, eta = 0.5),
+ data = dm,
+ nrounds = 20
+)
+pred <- predict(model, dm, predleaf = TRUE, reshape = TRUE)
+
+test_that("Slicing full model", {
+ new_model <- xgb.slice.Booster(model, 1, 0)
+ expect_equal(xgb.save.raw(new_model), xgb.save.raw(model))
+
+ new_model <- model[]
+ expect_equal(xgb.save.raw(new_model), xgb.save.raw(model))
+
+ new_model <- model[1:length(model)] # nolint
+ expect_equal(xgb.save.raw(new_model), xgb.save.raw(model))
+})
+
+test_that("Slicing sequence from start", {
+ new_model <- xgb.slice.Booster(model, 1, 10)
+ new_pred <- predict(new_model, dm, predleaf = TRUE, reshape = TRUE)
+ expect_equal(new_pred, pred[, seq(1, 10)])
+
+ new_model <- model[1:10]
+ new_pred <- predict(new_model, dm, predleaf = TRUE, reshape = TRUE)
+ expect_equal(new_pred, pred[, seq(1, 10)])
+})
+
+test_that("Slicing sequence from middle", {
+ new_model <- xgb.slice.Booster(model, 5, 10)
+ new_pred <- predict(new_model, dm, predleaf = TRUE, reshape = TRUE)
+ expect_equal(new_pred, pred[, seq(5, 10)])
+
+ new_model <- model[5:10]
+ new_pred <- predict(new_model, dm, predleaf = TRUE, reshape = TRUE)
+ expect_equal(new_pred, pred[, seq(5, 10)])
+})
+
+test_that("Slicing with non-unit step", {
+ for (s in 2:5) {
+ new_model <- xgb.slice.Booster(model, 1, 17, s)
+ new_pred <- predict(new_model, dm, predleaf = TRUE, reshape = TRUE)
+ expect_equal(new_pred, pred[, seq(1, 17, s)])
+
+ new_model <- model[seq(1, 17, s)]
+ new_pred <- predict(new_model, dm, predleaf = TRUE, reshape = TRUE)
+ expect_equal(new_pred, pred[, seq(1, 17, s)])
+ }
+})
+
+test_that("Slicing with non-unit step from middle", {
+ for (s in 2:5) {
+ new_model <- xgb.slice.Booster(model, 4, 17, s)
+ new_pred <- predict(new_model, dm, predleaf = TRUE, reshape = TRUE)
+ expect_equal(new_pred, pred[, seq(4, 17, s)])
+
+ new_model <- model[seq(4, 17, s)]
+ new_pred <- predict(new_model, dm, predleaf = TRUE, reshape = TRUE)
+ expect_equal(new_pred, pred[, seq(4, 17, s)])
+ }
+})
diff --git a/R-package/tests/testthat/test_callbacks.R b/R-package/tests/testthat/test_callbacks.R
index afa270c0b..c60d0c246 100644
--- a/R-package/tests/testthat/test_callbacks.R
+++ b/R-package/tests/testthat/test_callbacks.R
@@ -211,12 +211,11 @@ test_that("early stopping xgb.train works", {
, "Stopping. Best iteration")
expect_false(is.null(xgb.attr(bst, "best_iteration")))
expect_lt(xgb.attr(bst, "best_iteration"), 19)
- expect_equal(xgb.attr(bst, "best_iteration"), xgb.attr(bst, "best_ntreelimit"))
pred <- predict(bst, dtest)
expect_equal(length(pred), 1611)
err_pred <- err(ltest, pred)
- err_log <- attributes(bst)$evaluation_log[xgb.attr(bst, "best_iteration"), test_error]
+ err_log <- attributes(bst)$evaluation_log[xgb.attr(bst, "best_iteration") + 1, test_error]
expect_equal(err_log, err_pred, tolerance = 5e-6)
set.seed(11)
@@ -231,8 +230,7 @@ test_that("early stopping xgb.train works", {
loaded <- xgb.load(fname)
expect_false(is.null(xgb.attr(loaded, "best_iteration")))
- expect_equal(xgb.attr(loaded, "best_iteration"), xgb.attr(bst, "best_ntreelimit"))
- expect_equal(xgb.attr(loaded, "best_ntreelimit"), xgb.attr(bst, "best_ntreelimit"))
+ expect_equal(xgb.attr(loaded, "best_iteration"), xgb.attr(bst, "best_iteration"))
})
test_that("early stopping using a specific metric works", {
@@ -245,12 +243,11 @@ test_that("early stopping using a specific metric works", {
, "Stopping. Best iteration")
expect_false(is.null(xgb.attr(bst, "best_iteration")))
expect_lt(xgb.attr(bst, "best_iteration"), 19)
- expect_equal(xgb.attr(bst, "best_iteration"), xgb.attr(bst, "best_ntreelimit"))
- pred <- predict(bst, dtest, ntreelimit = xgb.attr(bst, "best_ntreelimit"))
+ pred <- predict(bst, dtest, iterationrange = c(1, xgb.attr(bst, "best_iteration") + 1))
expect_equal(length(pred), 1611)
logloss_pred <- sum(-ltest * log(pred) - (1 - ltest) * log(1 - pred)) / length(ltest)
- logloss_log <- attributes(bst)$evaluation_log[xgb.attr(bst, "best_iteration"), test_logloss]
+ logloss_log <- attributes(bst)$evaluation_log[xgb.attr(bst, "best_iteration") + 1, test_logloss]
expect_equal(logloss_log, logloss_pred, tolerance = 1e-5)
})
@@ -286,7 +283,6 @@ test_that("early stopping xgb.cv works", {
, "Stopping. Best iteration")
expect_false(is.null(cv$best_iteration))
expect_lt(cv$best_iteration, 19)
- expect_equal(cv$best_iteration, cv$best_ntreelimit)
# the best error is min error:
expect_true(cv$evaluation_log[, test_error_mean[cv$best_iteration] == min(test_error_mean)])
})
@@ -354,3 +350,44 @@ test_that("prediction in xgb.cv for softprob works", {
expect_equal(dim(cv$pred), c(nrow(iris), 3))
expect_lt(diff(range(rowSums(cv$pred))), 1e-6)
})
+
+test_that("prediction in xgb.cv works for multi-quantile", {
+ data(mtcars)
+ y <- mtcars$mpg
+ x <- as.matrix(mtcars[, -1])
+ dm <- xgb.DMatrix(x, label = y, nthread = 1)
+ cv <- xgb.cv(
+ data = dm,
+ params = list(
+ objective = "reg:quantileerror",
+ quantile_alpha = c(0.1, 0.2, 0.5, 0.8, 0.9),
+ nthread = 1
+ ),
+ nrounds = 5,
+ nfold = 3,
+ prediction = TRUE,
+ verbose = 0
+ )
+ expect_equal(dim(cv$pred), c(nrow(x), 5))
+})
+
+test_that("prediction in xgb.cv works for multi-output", {
+ data(mtcars)
+ y <- mtcars$mpg
+ x <- as.matrix(mtcars[, -1])
+ dm <- xgb.DMatrix(x, label = cbind(y, -y), nthread = 1)
+ cv <- xgb.cv(
+ data = dm,
+ params = list(
+ tree_method = "hist",
+ multi_strategy = "multi_output_tree",
+ objective = "reg:squarederror",
+ nthread = n_threads
+ ),
+ nrounds = 5,
+ nfold = 3,
+ prediction = TRUE,
+ verbose = 0
+ )
+ expect_equal(dim(cv$pred), c(nrow(x), 2))
+})
diff --git a/R-package/tests/testthat/test_glm.R b/R-package/tests/testthat/test_glm.R
index ae698d98f..349bcce8d 100644
--- a/R-package/tests/testthat/test_glm.R
+++ b/R-package/tests/testthat/test_glm.R
@@ -72,10 +72,10 @@ test_that("gblinear early stopping works", {
booster <- xgb.train(
param, dtrain, n, list(eval = dtest, train = dtrain), early_stopping_rounds = es_round
)
- expect_equal(xgb.attr(booster, "best_iteration"), 5)
+ expect_equal(xgb.attr(booster, "best_iteration"), 4)
predt_es <- predict(booster, dtrain)
- n <- xgb.attr(booster, "best_iteration") + es_round
+ n <- xgb.attr(booster, "best_iteration") + es_round + 1
booster <- xgb.train(
param, dtrain, n, list(eval = dtest, train = dtrain), early_stopping_rounds = es_round
)
diff --git a/R-package/tests/testthat/test_helpers.R b/R-package/tests/testthat/test_helpers.R
index 372f2520c..badac0213 100644
--- a/R-package/tests/testthat/test_helpers.R
+++ b/R-package/tests/testthat/test_helpers.R
@@ -282,9 +282,6 @@ test_that("xgb.model.dt.tree works with and without feature names", {
expect_equal(dim(dt.tree), c(188, 10))
expect_output(str(dt.tree), 'Feature.*\\"Age\\"')
- dt.tree.0 <- xgb.model.dt.tree(model = bst.Tree)
- expect_equal(dt.tree, dt.tree.0)
-
# when model contains no feature names:
dt.tree.x <- xgb.model.dt.tree(model = bst.Tree.unnamed)
expect_output(str(dt.tree.x), 'Feature.*\\"3\\"')
@@ -304,7 +301,7 @@ test_that("xgb.model.dt.tree throws error for gblinear", {
test_that("xgb.importance works with and without feature names", {
.skip_if_vcd_not_available()
- importance.Tree <- xgb.importance(feature_names = feature.names, model = bst.Tree)
+ importance.Tree <- xgb.importance(feature_names = feature.names, model = bst.Tree.unnamed)
if (!flag_32bit)
expect_equal(dim(importance.Tree), c(7, 4))
expect_equal(colnames(importance.Tree), c("Feature", "Gain", "Cover", "Frequency"))
@@ -330,9 +327,8 @@ test_that("xgb.importance works with and without feature names", {
importance <- xgb.importance(feature_names = feature.names, model = bst.Tree, trees = trees)
importance_from_dump <- function() {
- model_text_dump <- xgb.dump(model = bst.Tree.unnamed, with_stats = TRUE, trees = trees)
+ model_text_dump <- xgb.dump(model = bst.Tree, with_stats = TRUE, trees = trees)
imp <- xgb.model.dt.tree(
- feature_names = feature.names,
text = model_text_dump,
trees = trees
)[
diff --git a/R-package/tests/testthat/test_ranking.R b/R-package/tests/testthat/test_ranking.R
index 277c8f288..e49a32025 100644
--- a/R-package/tests/testthat/test_ranking.R
+++ b/R-package/tests/testthat/test_ranking.R
@@ -44,7 +44,7 @@ test_that('Test ranking with weighted data', {
expect_true(all(diff(attributes(bst)$evaluation_log$train_auc) >= 0))
expect_true(all(diff(attributes(bst)$evaluation_log$train_aucpr) >= 0))
for (i in 1:10) {
- pred <- predict(bst, newdata = dtrain, ntreelimit = i)
+ pred <- predict(bst, newdata = dtrain, iterationrange = c(1, i))
# is_sorted[i]: is i-th group correctly sorted by the ranking predictor?
is_sorted <- lapply(seq(1, 20, by = 5),
function(k) {
diff --git a/demo/guide-python/continuation.py b/demo/guide-python/continuation.py
index 84afc3710..e32c48665 100644
--- a/demo/guide-python/continuation.py
+++ b/demo/guide-python/continuation.py
@@ -16,14 +16,14 @@ def training_continuation(tmpdir: str, use_pickle: bool) -> None:
"""Basic training continuation."""
# Train 128 iterations in 1 session
X, y = load_breast_cancer(return_X_y=True)
- clf = xgboost.XGBClassifier(n_estimators=128)
- clf.fit(X, y, eval_set=[(X, y)], eval_metric="logloss")
+ clf = xgboost.XGBClassifier(n_estimators=128, eval_metric="logloss")
+ clf.fit(X, y, eval_set=[(X, y)])
print("Total boosted rounds:", clf.get_booster().num_boosted_rounds())
# Train 128 iterations in 2 sessions, with the first one runs for 32 iterations and
# the second one runs for 96 iterations
- clf = xgboost.XGBClassifier(n_estimators=32)
- clf.fit(X, y, eval_set=[(X, y)], eval_metric="logloss")
+ clf = xgboost.XGBClassifier(n_estimators=32, eval_metric="logloss")
+ clf.fit(X, y, eval_set=[(X, y)])
assert clf.get_booster().num_boosted_rounds() == 32
# load back the model, this could be a checkpoint
@@ -39,8 +39,8 @@ def training_continuation(tmpdir: str, use_pickle: bool) -> None:
loaded = xgboost.XGBClassifier()
loaded.load_model(path)
- clf = xgboost.XGBClassifier(n_estimators=128 - 32)
- clf.fit(X, y, eval_set=[(X, y)], eval_metric="logloss", xgb_model=loaded)
+ clf = xgboost.XGBClassifier(n_estimators=128 - 32, eval_metric="logloss")
+ clf.fit(X, y, eval_set=[(X, y)], xgb_model=loaded)
print("Total boosted rounds:", clf.get_booster().num_boosted_rounds())
@@ -56,19 +56,24 @@ def training_continuation_early_stop(tmpdir: str, use_pickle: bool) -> None:
n_estimators = 512
X, y = load_breast_cancer(return_X_y=True)
- clf = xgboost.XGBClassifier(n_estimators=n_estimators)
- clf.fit(X, y, eval_set=[(X, y)], eval_metric="logloss", callbacks=[early_stop])
+ clf = xgboost.XGBClassifier(
+ n_estimators=n_estimators, eval_metric="logloss", callbacks=[early_stop]
+ )
+ clf.fit(X, y, eval_set=[(X, y)])
print("Total boosted rounds:", clf.get_booster().num_boosted_rounds())
best = clf.best_iteration
# Train 512 iterations in 2 sessions, with the first one runs for 128 iterations and
# the second one runs until early stop.
- clf = xgboost.XGBClassifier(n_estimators=128)
+ clf = xgboost.XGBClassifier(
+ n_estimators=128, eval_metric="logloss", callbacks=[early_stop]
+ )
# Reinitialize the early stop callback
early_stop = xgboost.callback.EarlyStopping(
rounds=early_stopping_rounds, save_best=True
)
- clf.fit(X, y, eval_set=[(X, y)], eval_metric="logloss", callbacks=[early_stop])
+ clf.set_params(callbacks=[early_stop])
+ clf.fit(X, y, eval_set=[(X, y)])
assert clf.get_booster().num_boosted_rounds() == 128
# load back the model, this could be a checkpoint
@@ -87,13 +92,13 @@ def training_continuation_early_stop(tmpdir: str, use_pickle: bool) -> None:
early_stop = xgboost.callback.EarlyStopping(
rounds=early_stopping_rounds, save_best=True
)
- clf = xgboost.XGBClassifier(n_estimators=n_estimators - 128)
+ clf = xgboost.XGBClassifier(
+ n_estimators=n_estimators - 128, eval_metric="logloss", callbacks=[early_stop]
+ )
clf.fit(
X,
y,
eval_set=[(X, y)],
- eval_metric="logloss",
- callbacks=[early_stop],
xgb_model=loaded,
)
diff --git a/demo/guide-python/quantile_regression.py b/demo/guide-python/quantile_regression.py
index 5d186714c..a9e4532ba 100644
--- a/demo/guide-python/quantile_regression.py
+++ b/demo/guide-python/quantile_regression.py
@@ -46,10 +46,11 @@ def quantile_loss(args: argparse.Namespace) -> None:
X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=rng)
# We will be using the `hist` tree method, quantile DMatrix can be used to preserve
- # memory.
+ # memory (which has nothing to do with quantile regression itself, see its document
+ # for details).
# Do not use the `exact` tree method for quantile regression, otherwise the
# performance might drop.
- Xy = xgb.QuantileDMatrix(X, y)
+ Xy = xgb.QuantileDMatrix(X_train, y_train)
# use Xy as a reference
Xy_test = xgb.QuantileDMatrix(X_test, y_test, ref=Xy)
diff --git a/demo/guide-python/sklearn_evals_result.py b/demo/guide-python/sklearn_evals_result.py
index 9aed58500..781ab81af 100644
--- a/demo/guide-python/sklearn_evals_result.py
+++ b/demo/guide-python/sklearn_evals_result.py
@@ -16,30 +16,35 @@ labels, y = np.unique(y, return_inverse=True)
X_train, X_test = X[:1600], X[1600:]
y_train, y_test = y[:1600], y[1600:]
-param_dist = {'objective':'binary:logistic', 'n_estimators':2}
+param_dist = {"objective": "binary:logistic", "n_estimators": 2}
-clf = xgb.XGBModel(**param_dist)
+clf = xgb.XGBModel(
+ **param_dist,
+ eval_metric="logloss",
+)
# Or you can use: clf = xgb.XGBClassifier(**param_dist)
-clf.fit(X_train, y_train,
- eval_set=[(X_train, y_train), (X_test, y_test)],
- eval_metric='logloss',
- verbose=True)
+clf.fit(
+ X_train,
+ y_train,
+ eval_set=[(X_train, y_train), (X_test, y_test)],
+ verbose=True,
+)
# Load evals result by calling the evals_result() function
evals_result = clf.evals_result()
-print('Access logloss metric directly from validation_0:')
-print(evals_result['validation_0']['logloss'])
+print("Access logloss metric directly from validation_0:")
+print(evals_result["validation_0"]["logloss"])
-print('')
-print('Access metrics through a loop:')
+print("")
+print("Access metrics through a loop:")
for e_name, e_mtrs in evals_result.items():
- print('- {}'.format(e_name))
+ print("- {}".format(e_name))
for e_mtr_name, e_mtr_vals in e_mtrs.items():
- print(' - {}'.format(e_mtr_name))
- print(' - {}'.format(e_mtr_vals))
+ print(" - {}".format(e_mtr_name))
+ print(" - {}".format(e_mtr_vals))
-print('')
-print('Access complete dict:')
+print("")
+print("Access complete dict:")
print(evals_result)
diff --git a/demo/guide-python/sklearn_examples.py b/demo/guide-python/sklearn_examples.py
index cf33e959a..0fe7a8e24 100644
--- a/demo/guide-python/sklearn_examples.py
+++ b/demo/guide-python/sklearn_examples.py
@@ -1,4 +1,4 @@
-'''
+"""
Collection of examples for using sklearn interface
==================================================
@@ -8,7 +8,7 @@ For an introduction to XGBoost's scikit-learn estimator interface, see
Created on 1 Apr 2015
@author: Jamie Hall
-'''
+"""
import pickle
import numpy as np
@@ -22,8 +22,8 @@ rng = np.random.RandomState(31337)
print("Zeros and Ones from the Digits dataset: binary classification")
digits = load_digits(n_class=2)
-y = digits['target']
-X = digits['data']
+y = digits["target"]
+X = digits["data"]
kf = KFold(n_splits=2, shuffle=True, random_state=rng)
for train_index, test_index in kf.split(X):
xgb_model = xgb.XGBClassifier(n_jobs=1).fit(X[train_index], y[train_index])
@@ -33,8 +33,8 @@ for train_index, test_index in kf.split(X):
print("Iris: multiclass classification")
iris = load_iris()
-y = iris['target']
-X = iris['data']
+y = iris["target"]
+X = iris["data"]
kf = KFold(n_splits=2, shuffle=True, random_state=rng)
for train_index, test_index in kf.split(X):
xgb_model = xgb.XGBClassifier(n_jobs=1).fit(X[train_index], y[train_index])
@@ -53,9 +53,13 @@ for train_index, test_index in kf.split(X):
print("Parameter optimization")
xgb_model = xgb.XGBRegressor(n_jobs=1)
-clf = GridSearchCV(xgb_model,
- {'max_depth': [2, 4],
- 'n_estimators': [50, 100]}, verbose=1, n_jobs=1, cv=3)
+clf = GridSearchCV(
+ xgb_model,
+ {"max_depth": [2, 4], "n_estimators": [50, 100]},
+ verbose=1,
+ n_jobs=1,
+ cv=3,
+)
clf.fit(X, y)
print(clf.best_score_)
print(clf.best_params_)
@@ -69,9 +73,8 @@ print(np.allclose(clf.predict(X), clf2.predict(X)))
# Early-stopping
-X = digits['data']
-y = digits['target']
+X = digits["data"]
+y = digits["target"]
X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0)
-clf = xgb.XGBClassifier(n_jobs=1)
-clf.fit(X_train, y_train, early_stopping_rounds=10, eval_metric="auc",
- eval_set=[(X_test, y_test)])
+clf = xgb.XGBClassifier(n_jobs=1, early_stopping_rounds=10, eval_metric="auc")
+clf.fit(X_train, y_train, eval_set=[(X_test, y_test)])
diff --git a/demo/guide-python/sklearn_parallel.py b/demo/guide-python/sklearn_parallel.py
index 2ebefffc7..55e0bff74 100644
--- a/demo/guide-python/sklearn_parallel.py
+++ b/demo/guide-python/sklearn_parallel.py
@@ -12,6 +12,7 @@ import xgboost as xgb
if __name__ == "__main__":
print("Parallel Parameter optimization")
X, y = fetch_california_housing(return_X_y=True)
+ # Make sure the number of threads is balanced.
xgb_model = xgb.XGBRegressor(
n_jobs=multiprocessing.cpu_count() // 2, tree_method="hist"
)
diff --git a/dev/change_scala_version.py b/dev/change_scala_version.py
new file mode 100644
index 000000000..d9438f76a
--- /dev/null
+++ b/dev/change_scala_version.py
@@ -0,0 +1,79 @@
+import argparse
+import pathlib
+import re
+import shutil
+
+
+def main(args):
+ if args.scala_version == "2.12":
+ scala_ver = "2.12"
+ scala_patchver = "2.12.18"
+ elif args.scala_version == "2.13":
+ scala_ver = "2.13"
+ scala_patchver = "2.13.11"
+ else:
+ raise ValueError(f"Unsupported Scala version: {args.scala_version}")
+
+ # Clean artifacts
+ if args.purge_artifacts:
+ for target in pathlib.Path("jvm-packages/").glob("**/target"):
+ if target.is_dir():
+ print(f"Removing {target}...")
+ shutil.rmtree(target)
+
+ # Update pom.xml
+ for pom in pathlib.Path("jvm-packages/").glob("**/pom.xml"):
+ print(f"Updating {pom}...")
+ with open(pom, "r", encoding="utf-8") as f:
+ lines = f.readlines()
+ with open(pom, "w", encoding="utf-8") as f:
+ replaced_scalaver = False
+ replaced_scala_binver = False
+ for line in lines:
+ for artifact in [
+ "xgboost-jvm",
+ "xgboost4j",
+ "xgboost4j-gpu",
+ "xgboost4j-spark",
+ "xgboost4j-spark-gpu",
+ "xgboost4j-flink",
+ "xgboost4j-example",
+ ]:
+ line = re.sub(
+ f"{artifact}_[0-9\\.]*",
+ f"{artifact}_{scala_ver}",
+ line,
+ )
+ # Only replace the first occurrence of scala.version
+ if not replaced_scalaver:
+ line, nsubs = re.subn(
+ r"[0-9\.]*",
+ f"{scala_patchver}",
+ line,
+ )
+ if nsubs > 0:
+ replaced_scalaver = True
+ # Only replace the first occurrence of scala.binary.version
+ if not replaced_scala_binver:
+ line, nsubs = re.subn(
+ r"[0-9\.]*",
+ f"{scala_ver}",
+ line,
+ )
+ if nsubs > 0:
+ replaced_scala_binver = True
+ f.write(line)
+
+
+if __name__ == "__main__":
+ parser = argparse.ArgumentParser()
+ parser.add_argument("--purge-artifacts", action="store_true")
+ parser.add_argument(
+ "--scala-version",
+ type=str,
+ required=True,
+ help="Version of Scala to use in the JVM packages",
+ choices=["2.12", "2.13"],
+ )
+ parsed_args = parser.parse_args()
+ main(parsed_args)
diff --git a/dev/prepare_jvm_release.py b/dev/prepare_jvm_release.py
index 0cf5796a2..5d4d2e66f 100644
--- a/dev/prepare_jvm_release.py
+++ b/dev/prepare_jvm_release.py
@@ -2,7 +2,6 @@ import argparse
import errno
import glob
import os
-import platform
import re
import shutil
import subprocess
@@ -88,10 +87,6 @@ def main():
help="Version of the release being prepared",
)
args = parser.parse_args()
-
- if sys.platform != "darwin" or platform.machine() != "arm64":
- raise NotImplementedError("Please run this script using an M1 Mac")
-
version = args.release_version
expected_git_tag = "v" + version
current_git_tag = get_current_git_tag()
@@ -141,6 +136,7 @@ def main():
("linux", "x86_64"),
("windows", "x86_64"),
("macos", "x86_64"),
+ ("macos", "aarch64"),
]:
output_dir = f"xgboost4j/src/main/resources/lib/{os_ident}/{arch}"
maybe_makedirs(output_dir)
@@ -164,6 +160,10 @@ def main():
url=f"{nightly_bucket_prefix}/{git_branch}/libxgboost4j/libxgboost4j_{commit_hash}.dylib",
filename="xgboost4j/src/main/resources/lib/macos/x86_64/libxgboost4j.dylib",
)
+ retrieve(
+ url=f"{nightly_bucket_prefix}/{git_branch}/libxgboost4j/libxgboost4j_m1_{commit_hash}.dylib",
+ filename="xgboost4j/src/main/resources/lib/macos/aarch64/libxgboost4j.dylib",
+ )
with tempfile.TemporaryDirectory() as tempdir:
# libxgboost4j.so for Linux x86_64, CPU only
@@ -210,13 +210,31 @@ def main():
"2. Store the Sonatype credentials in .m2/settings.xml. See insturctions in "
"https://central.sonatype.org/publish/publish-maven/"
)
- print("3. Now on a Mac machine, run:")
- print(" GPG_TTY=$(tty) mvn deploy -Prelease -DskipTests")
+ print(
+ "3. Now on a Linux machine, run the following to build Scala 2.12 artifacts. "
+ "Make sure to use an Internet connection with fast upload speed:"
+ )
+ print(
+ " # Skip native build, since we have all needed native binaries from CI\n"
+ " export MAVEN_SKIP_NATIVE_BUILD=1\n"
+ " GPG_TTY=$(tty) mvn deploy -Prelease -DskipTests"
+ )
print(
"4. Log into https://oss.sonatype.org/. On the left menu panel, click Staging "
- "Repositories. Visit the URL https://oss.sonatype.org/content/repositories/mldmlc-1085 "
+ "Repositories. Visit the URL https://oss.sonatype.org/content/repositories/mldmlc-xxxx "
"to inspect the staged JAR files. Finally, press Release button to publish the "
- "artifacts to the Maven Central repository."
+ "artifacts to the Maven Central repository. The top-level metapackage should be "
+ "named xgboost-jvm_2.12."
+ )
+ print(
+ "5. Remove the Scala 2.12 artifacts and build Scala 2.13 artifacts:\n"
+ " export MAVEN_SKIP_NATIVE_BUILD=1\n"
+ " python dev/change_scala_version.py --scala-version 2.13 --purge-artifacts\n"
+ " GPG_TTY=$(tty) mvn deploy -Prelease -DskipTests"
+ )
+ print(
+ "6. Go to https://oss.sonatype.org/ to release the Scala 2.13 artifacts. "
+ "The top-level metapackage should be named xgboost-jvm_2.13."
)
diff --git a/dev/release-artifacts.py b/dev/release-artifacts.py
index 429fac078..d5f28f6fc 100644
--- a/dev/release-artifacts.py
+++ b/dev/release-artifacts.py
@@ -153,7 +153,7 @@ Following steps should be done manually:
def download_r_packages(
release: str, branch: str, rc: str, commit: str, outdir: str
) -> Tuple[Dict[str, str], List[str]]:
- platforms = ["win64", "linux"]
+ platforms = ["linux"]
dirname = os.path.join(outdir, "r-packages")
if not os.path.exists(dirname):
os.mkdir(dirname)
diff --git a/doc/tutorials/custom_metric_obj.rst b/doc/tutorials/custom_metric_obj.rst
index 118a099c1..36bd0c8d6 100644
--- a/doc/tutorials/custom_metric_obj.rst
+++ b/doc/tutorials/custom_metric_obj.rst
@@ -123,11 +123,11 @@ monitor our model's performance. As mentioned above, the default metric for ``S
elements = np.power(np.log1p(y) - np.log1p(predt), 2)
return 'PyRMSLE', float(np.sqrt(np.sum(elements) / len(y)))
-Since we are demonstrating in Python, the metric or objective need not be a function,
-any callable object should suffice. Similar to the objective function, our metric also
-accepts ``predt`` and ``dtrain`` as inputs, but returns the name of the metric itself and a
-floating point value as the result. After passing it into XGBoost as argument of ``feval``
-parameter:
+Since we are demonstrating in Python, the metric or objective need not be a function, any
+callable object should suffice. Similar to the objective function, our metric also
+accepts ``predt`` and ``dtrain`` as inputs, but returns the name of the metric itself and
+a floating point value as the result. After passing it into XGBoost as argument of
+``custom_metric`` parameter:
.. code-block:: python
@@ -136,7 +136,7 @@ parameter:
dtrain=dtrain,
num_boost_round=10,
obj=squared_log,
- feval=rmsle,
+ custom_metric=rmsle,
evals=[(dtrain, 'dtrain'), (dtest, 'dtest')],
evals_result=results)
diff --git a/include/xgboost/tree_model.h b/include/xgboost/tree_model.h
index 393dda59c..4c475da2e 100644
--- a/include/xgboost/tree_model.h
+++ b/include/xgboost/tree_model.h
@@ -398,8 +398,8 @@ class RegTree : public Model {
if (!func(nidx)) {
return;
}
- auto left = self[nidx].LeftChild();
- auto right = self[nidx].RightChild();
+ auto left = self.LeftChild(nidx);
+ auto right = self.RightChild(nidx);
if (left != RegTree::kInvalidNodeId) {
nodes.push(left);
}
diff --git a/jvm-packages/create_jni.py b/jvm-packages/create_jni.py
index e2b15dd2a..395bc79b0 100755
--- a/jvm-packages/create_jni.py
+++ b/jvm-packages/create_jni.py
@@ -1,6 +1,6 @@
#!/usr/bin/env python
-import errno
import argparse
+import errno
import glob
import os
import platform
@@ -19,13 +19,12 @@ CONFIG = {
"USE_HDFS": "OFF",
"USE_AZURE": "OFF",
"USE_S3": "OFF",
-
"USE_CUDA": "OFF",
"USE_NCCL": "OFF",
"USE_HIP": "OFF",
"USE_RCCL": "OFF",
"JVM_BINDINGS": "ON",
- "LOG_CAPI_INVOCATION": "OFF"
+ "LOG_CAPI_INVOCATION": "OFF",
}
@@ -72,18 +71,13 @@ def normpath(path):
return normalized
-if __name__ == "__main__":
- parser = argparse.ArgumentParser()
- parser.add_argument('--log-capi-invocation', type=str, choices=['ON', 'OFF'], default='OFF')
- parser.add_argument('--use-cuda', type=str, choices=['ON', 'OFF'], default='OFF')
- parser.add_argument('--use-hip', type=str, choices=['ON', 'OFF'], default='OFF')
- cli_args = parser.parse_args()
-
+def native_build(args):
if sys.platform == "darwin":
# Enable of your compiler supports OpenMP.
CONFIG["USE_OPENMP"] = "OFF"
- os.environ["JAVA_HOME"] = subprocess.check_output(
- "/usr/libexec/java_home").strip().decode()
+ os.environ["JAVA_HOME"] = (
+ subprocess.check_output("/usr/libexec/java_home").strip().decode()
+ )
print("building Java wrapper")
with cd(".."):
@@ -92,7 +86,7 @@ if __name__ == "__main__":
with cd(build_dir):
if sys.platform == "win32":
# Force x64 build on Windows.
- maybe_generator = ' -A x64'
+ maybe_generator = " -A x64"
else:
maybe_generator = ""
if sys.platform == "linux":
@@ -100,12 +94,12 @@ if __name__ == "__main__":
else:
maybe_parallel_build = ""
- if cli_args.log_capi_invocation == 'ON':
- CONFIG['LOG_CAPI_INVOCATION'] = 'ON'
+ if cli_args.log_capi_invocation == "ON":
+ CONFIG["LOG_CAPI_INVOCATION"] = "ON"
- if cli_args.use_cuda == 'ON':
- CONFIG['USE_CUDA'] = 'ON'
- CONFIG['USE_NCCL'] = 'ON'
+ if cli_args.use_cuda == "ON":
+ CONFIG["USE_CUDA"] = "ON"
+ CONFIG["USE_NCCL"] = "ON"
CONFIG["USE_DLOPEN_NCCL"] = "OFF"
elif cli_args.use_hip== 'ON':
CONFIG['USE_HIP'] = 'ON'
@@ -123,7 +117,7 @@ if __name__ == "__main__":
if gpu_arch_flag is not None:
args.append("%s" % gpu_arch_flag)
- lib_dir = os.path.join(os.pardir, 'lib')
+ lib_dir = os.path.join(os.pardir, "lib")
if os.path.exists(lib_dir):
shutil.rmtree(lib_dir)
run("cmake .. " + " ".join(args) + maybe_generator)
@@ -133,8 +127,10 @@ if __name__ == "__main__":
run(f'"{sys.executable}" mapfeat.py')
run(f'"{sys.executable}" mknfold.py machine.txt 1')
- xgboost4j = 'xgboost4j-gpu' if cli_args.use_cuda == 'ON' or cli_args.use_hip== 'ON' else 'xgboost4j'
- xgboost4j_spark = 'xgboost4j-spark-gpu' if cli_args.use_cuda == 'ON' or cli_args.use_hip == 'ON' else 'xgboost4j-spark'
+ xgboost4j = "xgboost4j-gpu" if cli_args.use_cuda == "ON" or cli_args.use_hip== "ON" else "xgboost4j"
+ xgboost4j_spark = (
+ "xgboost4j-spark-gpu" if cli_args.use_cuda == "ON" or cli_args.use_hip == "ON" else "xgboost4j-spark"
+ )
print("copying native library")
library_name, os_folder = {
@@ -149,14 +145,19 @@ if __name__ == "__main__":
"i86pc": "x86_64", # on Solaris x86_64
"sun4v": "sparc", # on Solaris sparc
"arm64": "aarch64", # on macOS & Windows ARM 64-bit
- "aarch64": "aarch64"
+ "aarch64": "aarch64",
}[platform.machine().lower()]
- output_folder = "{}/src/main/resources/lib/{}/{}".format(xgboost4j, os_folder, arch_folder)
+ output_folder = "{}/src/main/resources/lib/{}/{}".format(
+ xgboost4j, os_folder, arch_folder
+ )
maybe_makedirs(output_folder)
cp("../lib/" + library_name, output_folder)
print("copying pure-Python tracker")
- cp("../python-package/xgboost/tracker.py", "{}/src/main/resources".format(xgboost4j))
+ cp(
+ "../python-package/xgboost/tracker.py",
+ "{}/src/main/resources".format(xgboost4j),
+ )
print("copying train/test files")
maybe_makedirs("{}/src/test/resources".format(xgboost4j_spark))
@@ -172,3 +173,18 @@ if __name__ == "__main__":
maybe_makedirs("{}/src/test/resources".format(xgboost4j))
for file in glob.glob("../demo/data/agaricus.*"):
cp(file, "{}/src/test/resources".format(xgboost4j))
+
+
+if __name__ == "__main__":
+ if "MAVEN_SKIP_NATIVE_BUILD" in os.environ:
+ print("MAVEN_SKIP_NATIVE_BUILD is set. Skipping native build...")
+ else:
+ parser = argparse.ArgumentParser()
+ parser.add_argument(
+ "--log-capi-invocation", type=str, choices=["ON", "OFF"], default="OFF"
+ )
+ parser.add_argument(
+ "--use-cuda", type=str, choices=["ON", "OFF"], default="OFF"
+ )
+ cli_args = parser.parse_args()
+ native_build(cli_args)
diff --git a/jvm-packages/pom.xml b/jvm-packages/pom.xml
index 08bfd841c..5b6f82b6a 100644
--- a/jvm-packages/pom.xml
+++ b/jvm-packages/pom.xml
@@ -5,7 +5,7 @@
4.0.0
ml.dmlc
- xgboost-jvm
+ xgboost-jvm_2.12
2.1.0-SNAPSHOT
pom
XGBoost JVM Package
@@ -43,10 +43,10 @@
5
OFF
OFF
- 23.10.0
- 23.10.0
+ 23.12.1
+ 23.12.1
+ cuda12
OFF
- cuda11
3.2.17
2.11.0
@@ -91,14 +91,6 @@
-
- scala-2.13
-
- 2.13
- 2.13.11
-
-
-
gpu
diff --git a/jvm-packages/xgboost4j-example/pom.xml b/jvm-packages/xgboost4j-example/pom.xml
index 3a56615d6..431c6766a 100644
--- a/jvm-packages/xgboost4j-example/pom.xml
+++ b/jvm-packages/xgboost4j-example/pom.xml
@@ -5,11 +5,11 @@
4.0.0
ml.dmlc
- xgboost-jvm
+ xgboost-jvm_2.12
2.1.0-SNAPSHOT
xgboost4j-example
- xgboost4j-example_${scala.binary.version}
+ xgboost4j-example_2.12
2.1.0-SNAPSHOT
jar
@@ -26,7 +26,7 @@
ml.dmlc
- xgboost4j-spark_${scala.binary.version}
+ xgboost4j-spark_2.12
${project.version}
@@ -37,7 +37,7 @@
ml.dmlc
- xgboost4j-flink_${scala.binary.version}
+ xgboost4j-flink_2.12
${project.version}
diff --git a/jvm-packages/xgboost4j-flink/pom.xml b/jvm-packages/xgboost4j-flink/pom.xml
index 6f700ca0a..e3dfb3830 100644
--- a/jvm-packages/xgboost4j-flink/pom.xml
+++ b/jvm-packages/xgboost4j-flink/pom.xml
@@ -5,12 +5,12 @@
4.0.0
ml.dmlc
- xgboost-jvm
+ xgboost-jvm_2.12
2.1.0-SNAPSHOT
xgboost4j-flink
- xgboost4j-flink_${scala.binary.version}
+ xgboost4j-flink_2.12
2.1.0-SNAPSHOT
2.2.0
@@ -30,7 +30,7 @@
ml.dmlc
- xgboost4j_${scala.binary.version}
+ xgboost4j_2.12
${project.version}
diff --git a/jvm-packages/xgboost4j-gpu/pom.xml b/jvm-packages/xgboost4j-gpu/pom.xml
index bcc3fe3bb..2dc36d52d 100644
--- a/jvm-packages/xgboost4j-gpu/pom.xml
+++ b/jvm-packages/xgboost4j-gpu/pom.xml
@@ -5,10 +5,10 @@
4.0.0
ml.dmlc
- xgboost-jvm
+ xgboost-jvm_2.12
2.1.0-SNAPSHOT
- xgboost4j-gpu_${scala.binary.version}
+ xgboost4j-gpu_2.12
xgboost4j-gpu
2.1.0-SNAPSHOT
jar
diff --git a/jvm-packages/xgboost4j-spark-gpu/pom.xml b/jvm-packages/xgboost4j-spark-gpu/pom.xml
index a29b4e056..149f2f3a3 100644
--- a/jvm-packages/xgboost4j-spark-gpu/pom.xml
+++ b/jvm-packages/xgboost4j-spark-gpu/pom.xml
@@ -5,11 +5,11 @@
4.0.0
ml.dmlc
- xgboost-jvm
+ xgboost-jvm_2.12
2.1.0-SNAPSHOT
xgboost4j-spark-gpu
- xgboost4j-spark-gpu_${scala.binary.version}
+ xgboost4j-spark-gpu_2.12
@@ -24,7 +24,7 @@
ml.dmlc
- xgboost4j-gpu_${scala.binary.version}
+ xgboost4j-gpu_2.12
${project.version}
diff --git a/jvm-packages/xgboost4j-spark/pom.xml b/jvm-packages/xgboost4j-spark/pom.xml
index 179b1c762..6f16335f0 100644
--- a/jvm-packages/xgboost4j-spark/pom.xml
+++ b/jvm-packages/xgboost4j-spark/pom.xml
@@ -5,11 +5,11 @@
4.0.0
ml.dmlc
- xgboost-jvm
+ xgboost-jvm_2.12
2.1.0-SNAPSHOT
xgboost4j-spark
- xgboost4j-spark_${scala.binary.version}
+ xgboost4j-spark_2.12
@@ -24,7 +24,7 @@
ml.dmlc
- xgboost4j_${scala.binary.version}
+ xgboost4j_2.12
${project.version}
diff --git a/jvm-packages/xgboost4j/pom.xml b/jvm-packages/xgboost4j/pom.xml
index e05bbcf48..7eb186919 100644
--- a/jvm-packages/xgboost4j/pom.xml
+++ b/jvm-packages/xgboost4j/pom.xml
@@ -5,11 +5,11 @@
4.0.0
ml.dmlc
- xgboost-jvm
+ xgboost-jvm_2.12
2.1.0-SNAPSHOT
xgboost4j
- xgboost4j_${scala.binary.version}
+ xgboost4j_2.12
2.1.0-SNAPSHOT
jar
diff --git a/python-package/xgboost/dask/__init__.py b/python-package/xgboost/dask/__init__.py
index 6b4ae5b07..a9b51f35d 100644
--- a/python-package/xgboost/dask/__init__.py
+++ b/python-package/xgboost/dask/__init__.py
@@ -61,7 +61,7 @@ from typing import (
import numpy
from xgboost import collective, config
-from xgboost._typing import _T, FeatureNames, FeatureTypes, ModelIn
+from xgboost._typing import _T, FeatureNames, FeatureTypes
from xgboost.callback import TrainingCallback
from xgboost.compat import DataFrame, LazyLoader, concat, lazy_isinstance
from xgboost.core import (
@@ -1774,14 +1774,11 @@ class DaskXGBRegressor(DaskScikitLearnBase, XGBRegressorBase):
sample_weight: Optional[_DaskCollection],
base_margin: Optional[_DaskCollection],
eval_set: Optional[Sequence[Tuple[_DaskCollection, _DaskCollection]]],
- eval_metric: Optional[Union[str, Sequence[str], Metric]],
sample_weight_eval_set: Optional[Sequence[_DaskCollection]],
base_margin_eval_set: Optional[Sequence[_DaskCollection]],
- early_stopping_rounds: Optional[int],
verbose: Union[int, bool],
xgb_model: Optional[Union[Booster, XGBModel]],
feature_weights: Optional[_DaskCollection],
- callbacks: Optional[Sequence[TrainingCallback]],
) -> _DaskCollection:
params = self.get_xgb_params()
dtrain, evals = await _async_wrap_evaluation_matrices(
@@ -1809,9 +1806,7 @@ class DaskXGBRegressor(DaskScikitLearnBase, XGBRegressorBase):
obj: Optional[Callable] = _objective_decorator(self.objective)
else:
obj = None
- model, metric, params, early_stopping_rounds, callbacks = self._configure_fit(
- xgb_model, eval_metric, params, early_stopping_rounds, callbacks
- )
+ model, metric, params = self._configure_fit(xgb_model, params)
results = await self.client.sync(
_train_async,
asynchronous=True,
@@ -1826,8 +1821,8 @@ class DaskXGBRegressor(DaskScikitLearnBase, XGBRegressorBase):
feval=None,
custom_metric=metric,
verbose_eval=verbose,
- early_stopping_rounds=early_stopping_rounds,
- callbacks=callbacks,
+ early_stopping_rounds=self.early_stopping_rounds,
+ callbacks=self.callbacks,
xgb_model=model,
)
self._Booster = results["booster"]
@@ -1844,14 +1839,11 @@ class DaskXGBRegressor(DaskScikitLearnBase, XGBRegressorBase):
sample_weight: Optional[_DaskCollection] = None,
base_margin: Optional[_DaskCollection] = None,
eval_set: Optional[Sequence[Tuple[_DaskCollection, _DaskCollection]]] = None,
- eval_metric: Optional[Union[str, Sequence[str], Callable]] = None,
- early_stopping_rounds: Optional[int] = None,
verbose: Union[int, bool] = True,
xgb_model: Optional[Union[Booster, XGBModel]] = None,
sample_weight_eval_set: Optional[Sequence[_DaskCollection]] = None,
base_margin_eval_set: Optional[Sequence[_DaskCollection]] = None,
feature_weights: Optional[_DaskCollection] = None,
- callbacks: Optional[Sequence[TrainingCallback]] = None,
) -> "DaskXGBRegressor":
_assert_dask_support()
args = {k: v for k, v in locals().items() if k not in ("self", "__class__")}
@@ -1871,14 +1863,11 @@ class DaskXGBClassifier(DaskScikitLearnBase, XGBClassifierBase):
sample_weight: Optional[_DaskCollection],
base_margin: Optional[_DaskCollection],
eval_set: Optional[Sequence[Tuple[_DaskCollection, _DaskCollection]]],
- eval_metric: Optional[Union[str, Sequence[str], Metric]],
sample_weight_eval_set: Optional[Sequence[_DaskCollection]],
base_margin_eval_set: Optional[Sequence[_DaskCollection]],
- early_stopping_rounds: Optional[int],
verbose: Union[int, bool],
xgb_model: Optional[Union[Booster, XGBModel]],
feature_weights: Optional[_DaskCollection],
- callbacks: Optional[Sequence[TrainingCallback]],
) -> "DaskXGBClassifier":
params = self.get_xgb_params()
dtrain, evals = await _async_wrap_evaluation_matrices(
@@ -1924,9 +1913,7 @@ class DaskXGBClassifier(DaskScikitLearnBase, XGBClassifierBase):
obj: Optional[Callable] = _objective_decorator(self.objective)
else:
obj = None
- model, metric, params, early_stopping_rounds, callbacks = self._configure_fit(
- xgb_model, eval_metric, params, early_stopping_rounds, callbacks
- )
+ model, metric, params = self._configure_fit(xgb_model, params)
results = await self.client.sync(
_train_async,
asynchronous=True,
@@ -1941,8 +1928,8 @@ class DaskXGBClassifier(DaskScikitLearnBase, XGBClassifierBase):
feval=None,
custom_metric=metric,
verbose_eval=verbose,
- early_stopping_rounds=early_stopping_rounds,
- callbacks=callbacks,
+ early_stopping_rounds=self.early_stopping_rounds,
+ callbacks=self.callbacks,
xgb_model=model,
)
self._Booster = results["booster"]
@@ -1960,14 +1947,11 @@ class DaskXGBClassifier(DaskScikitLearnBase, XGBClassifierBase):
sample_weight: Optional[_DaskCollection] = None,
base_margin: Optional[_DaskCollection] = None,
eval_set: Optional[Sequence[Tuple[_DaskCollection, _DaskCollection]]] = None,
- eval_metric: Optional[Union[str, Sequence[str], Callable]] = None,
- early_stopping_rounds: Optional[int] = None,
verbose: Union[int, bool] = True,
xgb_model: Optional[Union[Booster, XGBModel]] = None,
sample_weight_eval_set: Optional[Sequence[_DaskCollection]] = None,
base_margin_eval_set: Optional[Sequence[_DaskCollection]] = None,
feature_weights: Optional[_DaskCollection] = None,
- callbacks: Optional[Sequence[TrainingCallback]] = None,
) -> "DaskXGBClassifier":
_assert_dask_support()
args = {k: v for k, v in locals().items() if k not in ("self", "__class__")}
@@ -2063,7 +2047,7 @@ class DaskXGBRanker(DaskScikitLearnBase, XGBRankerMixIn):
def __init__(self, *, objective: str = "rank:pairwise", **kwargs: Any):
if callable(objective):
raise ValueError("Custom objective function not supported by XGBRanker.")
- super().__init__(objective=objective, kwargs=kwargs)
+ super().__init__(objective=objective, **kwargs)
async def _fit_async(
self,
@@ -2078,12 +2062,9 @@ class DaskXGBRanker(DaskScikitLearnBase, XGBRankerMixIn):
base_margin_eval_set: Optional[Sequence[_DaskCollection]],
eval_group: Optional[Sequence[_DaskCollection]],
eval_qid: Optional[Sequence[_DaskCollection]],
- eval_metric: Optional[Union[str, Sequence[str], Metric]],
- early_stopping_rounds: Optional[int],
verbose: Union[int, bool],
xgb_model: Optional[Union[XGBModel, Booster]],
feature_weights: Optional[_DaskCollection],
- callbacks: Optional[Sequence[TrainingCallback]],
) -> "DaskXGBRanker":
msg = "Use `qid` instead of `group` on dask interface."
if not (group is None and eval_group is None):
@@ -2111,14 +2092,7 @@ class DaskXGBRanker(DaskScikitLearnBase, XGBRankerMixIn):
enable_categorical=self.enable_categorical,
feature_types=self.feature_types,
)
- if eval_metric is not None:
- if callable(eval_metric):
- raise ValueError(
- "Custom evaluation metric is not yet supported for XGBRanker."
- )
- model, metric, params, early_stopping_rounds, callbacks = self._configure_fit(
- xgb_model, eval_metric, params, early_stopping_rounds, callbacks
- )
+ model, metric, params = self._configure_fit(xgb_model, params)
results = await self.client.sync(
_train_async,
asynchronous=True,
@@ -2133,8 +2107,8 @@ class DaskXGBRanker(DaskScikitLearnBase, XGBRankerMixIn):
feval=None,
custom_metric=metric,
verbose_eval=verbose,
- early_stopping_rounds=early_stopping_rounds,
- callbacks=callbacks,
+ early_stopping_rounds=self.early_stopping_rounds,
+ callbacks=self.callbacks,
xgb_model=model,
)
self._Booster = results["booster"]
@@ -2155,14 +2129,11 @@ class DaskXGBRanker(DaskScikitLearnBase, XGBRankerMixIn):
eval_set: Optional[Sequence[Tuple[_DaskCollection, _DaskCollection]]] = None,
eval_group: Optional[Sequence[_DaskCollection]] = None,
eval_qid: Optional[Sequence[_DaskCollection]] = None,
- eval_metric: Optional[Union[str, Sequence[str], Callable]] = None,
- early_stopping_rounds: Optional[int] = None,
verbose: Union[int, bool] = False,
xgb_model: Optional[Union[XGBModel, Booster]] = None,
sample_weight_eval_set: Optional[Sequence[_DaskCollection]] = None,
base_margin_eval_set: Optional[Sequence[_DaskCollection]] = None,
feature_weights: Optional[_DaskCollection] = None,
- callbacks: Optional[Sequence[TrainingCallback]] = None,
) -> "DaskXGBRanker":
_assert_dask_support()
args = {k: v for k, v in locals().items() if k not in ("self", "__class__")}
@@ -2221,18 +2192,15 @@ class DaskXGBRFRegressor(DaskXGBRegressor):
sample_weight: Optional[_DaskCollection] = None,
base_margin: Optional[_DaskCollection] = None,
eval_set: Optional[Sequence[Tuple[_DaskCollection, _DaskCollection]]] = None,
- eval_metric: Optional[Union[str, Sequence[str], Callable]] = None,
- early_stopping_rounds: Optional[int] = None,
verbose: Union[int, bool] = True,
xgb_model: Optional[Union[Booster, XGBModel]] = None,
sample_weight_eval_set: Optional[Sequence[_DaskCollection]] = None,
base_margin_eval_set: Optional[Sequence[_DaskCollection]] = None,
feature_weights: Optional[_DaskCollection] = None,
- callbacks: Optional[Sequence[TrainingCallback]] = None,
) -> "DaskXGBRFRegressor":
_assert_dask_support()
args = {k: v for k, v in locals().items() if k not in ("self", "__class__")}
- _check_rf_callback(early_stopping_rounds, callbacks)
+ _check_rf_callback(self.early_stopping_rounds, self.callbacks)
super().fit(**args)
return self
@@ -2285,17 +2253,14 @@ class DaskXGBRFClassifier(DaskXGBClassifier):
sample_weight: Optional[_DaskCollection] = None,
base_margin: Optional[_DaskCollection] = None,
eval_set: Optional[Sequence[Tuple[_DaskCollection, _DaskCollection]]] = None,
- eval_metric: Optional[Union[str, Sequence[str], Callable]] = None,
- early_stopping_rounds: Optional[int] = None,
verbose: Union[int, bool] = True,
xgb_model: Optional[Union[Booster, XGBModel]] = None,
sample_weight_eval_set: Optional[Sequence[_DaskCollection]] = None,
base_margin_eval_set: Optional[Sequence[_DaskCollection]] = None,
feature_weights: Optional[_DaskCollection] = None,
- callbacks: Optional[Sequence[TrainingCallback]] = None,
) -> "DaskXGBRFClassifier":
_assert_dask_support()
args = {k: v for k, v in locals().items() if k not in ("self", "__class__")}
- _check_rf_callback(early_stopping_rounds, callbacks)
+ _check_rf_callback(self.early_stopping_rounds, self.callbacks)
super().fit(**args)
return self
diff --git a/python-package/xgboost/sklearn.py b/python-package/xgboost/sklearn.py
index 8c3a96784..a0fde2292 100644
--- a/python-package/xgboost/sklearn.py
+++ b/python-package/xgboost/sklearn.py
@@ -349,12 +349,6 @@ __model_doc = f"""
See :doc:`/tutorials/custom_metric_obj` and :ref:`custom-obj-metric` for more
information.
- .. note::
-
- This parameter replaces `eval_metric` in :py:meth:`fit` method. The old
- one receives un-transformed prediction regardless of whether custom
- objective is being used.
-
.. code-block:: python
from sklearn.datasets import load_diabetes
@@ -389,10 +383,6 @@ __model_doc = f"""
early stopping. If there's more than one metric in **eval_metric**, the last
metric will be used for early stopping.
- .. note::
-
- This parameter replaces `early_stopping_rounds` in :py:meth:`fit` method.
-
callbacks : Optional[List[TrainingCallback]]
List of callback functions that are applied at end of each iteration.
It is possible to use predefined callbacks by using
@@ -872,16 +862,11 @@ class XGBModel(XGBModelBase):
def _configure_fit(
self,
booster: Optional[Union[Booster, "XGBModel", str]],
- eval_metric: Optional[Union[Callable, str, Sequence[str]]],
params: Dict[str, Any],
- early_stopping_rounds: Optional[int],
- callbacks: Optional[Sequence[TrainingCallback]],
) -> Tuple[
Optional[Union[Booster, str, "XGBModel"]],
Optional[Metric],
Dict[str, Any],
- Optional[int],
- Optional[Sequence[TrainingCallback]],
]:
"""Configure parameters for :py:meth:`fit`."""
if isinstance(booster, XGBModel):
@@ -903,49 +888,16 @@ class XGBModel(XGBModelBase):
"or `set_params` instead."
)
- # Configure evaluation metric.
- if eval_metric is not None:
- _deprecated("eval_metric")
- if self.eval_metric is not None and eval_metric is not None:
- _duplicated("eval_metric")
- # - track where does the evaluation metric come from
- if self.eval_metric is not None:
- from_fit = False
- eval_metric = self.eval_metric
- else:
- from_fit = True
# - configure callable evaluation metric
metric: Optional[Metric] = None
- if eval_metric is not None:
- if callable(eval_metric) and from_fit:
- # No need to wrap the evaluation function for old parameter.
- metric = eval_metric
- elif callable(eval_metric):
- # Parameter from constructor or set_params
+ if self.eval_metric is not None:
+ if callable(self.eval_metric):
if self._get_type() == "ranker":
- metric = ltr_metric_decorator(eval_metric, self.n_jobs)
+ metric = ltr_metric_decorator(self.eval_metric, self.n_jobs)
else:
- metric = _metric_decorator(eval_metric)
+ metric = _metric_decorator(self.eval_metric)
else:
- params.update({"eval_metric": eval_metric})
-
- # Configure early_stopping_rounds
- if early_stopping_rounds is not None:
- _deprecated("early_stopping_rounds")
- if early_stopping_rounds is not None and self.early_stopping_rounds is not None:
- _duplicated("early_stopping_rounds")
- early_stopping_rounds = (
- self.early_stopping_rounds
- if self.early_stopping_rounds is not None
- else early_stopping_rounds
- )
-
- # Configure callbacks
- if callbacks is not None:
- _deprecated("callbacks")
- if callbacks is not None and self.callbacks is not None:
- _duplicated("callbacks")
- callbacks = self.callbacks if self.callbacks is not None else callbacks
+ params.update({"eval_metric": self.eval_metric})
tree_method = params.get("tree_method", None)
if self.enable_categorical and tree_method == "exact":
@@ -953,7 +905,7 @@ class XGBModel(XGBModelBase):
"Experimental support for categorical data is not implemented for"
" current tree method yet."
)
- return model, metric, params, early_stopping_rounds, callbacks
+ return model, metric, params
def _create_dmatrix(self, ref: Optional[DMatrix], **kwargs: Any) -> DMatrix:
# Use `QuantileDMatrix` to save memory.
@@ -979,14 +931,11 @@ class XGBModel(XGBModelBase):
sample_weight: Optional[ArrayLike] = None,
base_margin: Optional[ArrayLike] = None,
eval_set: Optional[Sequence[Tuple[ArrayLike, ArrayLike]]] = None,
- eval_metric: Optional[Union[str, Sequence[str], Metric]] = None,
- early_stopping_rounds: Optional[int] = None,
verbose: Optional[Union[bool, int]] = True,
xgb_model: Optional[Union[Booster, str, "XGBModel"]] = None,
sample_weight_eval_set: Optional[Sequence[ArrayLike]] = None,
base_margin_eval_set: Optional[Sequence[ArrayLike]] = None,
feature_weights: Optional[ArrayLike] = None,
- callbacks: Optional[Sequence[TrainingCallback]] = None,
) -> "XGBModel":
# pylint: disable=invalid-name,attribute-defined-outside-init
"""Fit gradient boosting model.
@@ -1017,18 +966,6 @@ class XGBModel(XGBModelBase):
metrics will be computed.
Validation metrics will help us track the performance of the model.
- eval_metric : str, list of str, or callable, optional
-
- .. deprecated:: 1.6.0
-
- Use `eval_metric` in :py:meth:`__init__` or :py:meth:`set_params` instead.
-
- early_stopping_rounds : int
-
- .. deprecated:: 1.6.0
-
- Use `early_stopping_rounds` in :py:meth:`__init__` or :py:meth:`set_params`
- instead.
verbose :
If `verbose` is True and an evaluation set is used, the evaluation metric
measured on the validation set is printed to stdout at each boosting stage.
@@ -1049,10 +986,6 @@ class XGBModel(XGBModelBase):
selected when colsample is being used. All values must be greater than 0,
otherwise a `ValueError` is thrown.
- callbacks :
- .. deprecated:: 1.6.0
- Use `callbacks` in :py:meth:`__init__` or :py:meth:`set_params` instead.
-
"""
with config_context(verbosity=self.verbosity):
evals_result: TrainingCallback.EvalsLog = {}
@@ -1082,27 +1015,19 @@ class XGBModel(XGBModelBase):
else:
obj = None
- (
- model,
- metric,
- params,
- early_stopping_rounds,
- callbacks,
- ) = self._configure_fit(
- xgb_model, eval_metric, params, early_stopping_rounds, callbacks
- )
+ model, metric, params = self._configure_fit(xgb_model, params)
self._Booster = train(
params,
train_dmatrix,
self.get_num_boosting_rounds(),
evals=evals,
- early_stopping_rounds=early_stopping_rounds,
+ early_stopping_rounds=self.early_stopping_rounds,
evals_result=evals_result,
obj=obj,
custom_metric=metric,
verbose_eval=verbose,
xgb_model=model,
- callbacks=callbacks,
+ callbacks=self.callbacks,
)
self._set_evaluation_result(evals_result)
@@ -1437,14 +1362,11 @@ class XGBClassifier(XGBModel, XGBClassifierBase):
sample_weight: Optional[ArrayLike] = None,
base_margin: Optional[ArrayLike] = None,
eval_set: Optional[Sequence[Tuple[ArrayLike, ArrayLike]]] = None,
- eval_metric: Optional[Union[str, Sequence[str], Metric]] = None,
- early_stopping_rounds: Optional[int] = None,
verbose: Optional[Union[bool, int]] = True,
xgb_model: Optional[Union[Booster, str, XGBModel]] = None,
sample_weight_eval_set: Optional[Sequence[ArrayLike]] = None,
base_margin_eval_set: Optional[Sequence[ArrayLike]] = None,
feature_weights: Optional[ArrayLike] = None,
- callbacks: Optional[Sequence[TrainingCallback]] = None,
) -> "XGBClassifier":
# pylint: disable = attribute-defined-outside-init,too-many-statements
with config_context(verbosity=self.verbosity):
@@ -1492,15 +1414,7 @@ class XGBClassifier(XGBModel, XGBClassifierBase):
params["objective"] = "multi:softprob"
params["num_class"] = self.n_classes_
- (
- model,
- metric,
- params,
- early_stopping_rounds,
- callbacks,
- ) = self._configure_fit(
- xgb_model, eval_metric, params, early_stopping_rounds, callbacks
- )
+ model, metric, params = self._configure_fit(xgb_model, params)
train_dmatrix, evals = _wrap_evaluation_matrices(
missing=self.missing,
X=X,
@@ -1525,13 +1439,13 @@ class XGBClassifier(XGBModel, XGBClassifierBase):
train_dmatrix,
self.get_num_boosting_rounds(),
evals=evals,
- early_stopping_rounds=early_stopping_rounds,
+ early_stopping_rounds=self.early_stopping_rounds,
evals_result=evals_result,
obj=obj,
custom_metric=metric,
verbose_eval=verbose,
xgb_model=model,
- callbacks=callbacks,
+ callbacks=self.callbacks,
)
if not callable(self.objective):
@@ -1693,17 +1607,14 @@ class XGBRFClassifier(XGBClassifier):
sample_weight: Optional[ArrayLike] = None,
base_margin: Optional[ArrayLike] = None,
eval_set: Optional[Sequence[Tuple[ArrayLike, ArrayLike]]] = None,
- eval_metric: Optional[Union[str, Sequence[str], Metric]] = None,
- early_stopping_rounds: Optional[int] = None,
verbose: Optional[Union[bool, int]] = True,
xgb_model: Optional[Union[Booster, str, XGBModel]] = None,
sample_weight_eval_set: Optional[Sequence[ArrayLike]] = None,
base_margin_eval_set: Optional[Sequence[ArrayLike]] = None,
feature_weights: Optional[ArrayLike] = None,
- callbacks: Optional[Sequence[TrainingCallback]] = None,
) -> "XGBRFClassifier":
args = {k: v for k, v in locals().items() if k not in ("self", "__class__")}
- _check_rf_callback(early_stopping_rounds, callbacks)
+ _check_rf_callback(self.early_stopping_rounds, self.callbacks)
super().fit(**args)
return self
@@ -1768,17 +1679,14 @@ class XGBRFRegressor(XGBRegressor):
sample_weight: Optional[ArrayLike] = None,
base_margin: Optional[ArrayLike] = None,
eval_set: Optional[Sequence[Tuple[ArrayLike, ArrayLike]]] = None,
- eval_metric: Optional[Union[str, Sequence[str], Metric]] = None,
- early_stopping_rounds: Optional[int] = None,
verbose: Optional[Union[bool, int]] = True,
xgb_model: Optional[Union[Booster, str, XGBModel]] = None,
sample_weight_eval_set: Optional[Sequence[ArrayLike]] = None,
base_margin_eval_set: Optional[Sequence[ArrayLike]] = None,
feature_weights: Optional[ArrayLike] = None,
- callbacks: Optional[Sequence[TrainingCallback]] = None,
) -> "XGBRFRegressor":
args = {k: v for k, v in locals().items() if k not in ("self", "__class__")}
- _check_rf_callback(early_stopping_rounds, callbacks)
+ _check_rf_callback(self.early_stopping_rounds, self.callbacks)
super().fit(**args)
return self
@@ -1883,14 +1791,11 @@ class XGBRanker(XGBModel, XGBRankerMixIn):
eval_set: Optional[Sequence[Tuple[ArrayLike, ArrayLike]]] = None,
eval_group: Optional[Sequence[ArrayLike]] = None,
eval_qid: Optional[Sequence[ArrayLike]] = None,
- eval_metric: Optional[Union[str, Sequence[str], Metric]] = None,
- early_stopping_rounds: Optional[int] = None,
verbose: Optional[Union[bool, int]] = False,
xgb_model: Optional[Union[Booster, str, XGBModel]] = None,
sample_weight_eval_set: Optional[Sequence[ArrayLike]] = None,
base_margin_eval_set: Optional[Sequence[ArrayLike]] = None,
feature_weights: Optional[ArrayLike] = None,
- callbacks: Optional[Sequence[TrainingCallback]] = None,
) -> "XGBRanker":
# pylint: disable = attribute-defined-outside-init,arguments-differ
"""Fit gradient boosting ranker
@@ -1960,15 +1865,6 @@ class XGBRanker(XGBModel, XGBRankerMixIn):
pair in **eval_set**. The special column convention in `X` applies to
validation datasets as well.
- eval_metric : str, list of str, optional
- .. deprecated:: 1.6.0
- use `eval_metric` in :py:meth:`__init__` or :py:meth:`set_params` instead.
-
- early_stopping_rounds : int
- .. deprecated:: 1.6.0
- use `early_stopping_rounds` in :py:meth:`__init__` or
- :py:meth:`set_params` instead.
-
verbose :
If `verbose` is True and an evaluation set is used, the evaluation metric
measured on the validation set is printed to stdout at each boosting stage.
@@ -1996,10 +1892,6 @@ class XGBRanker(XGBModel, XGBRankerMixIn):
selected when colsample is being used. All values must be greater than 0,
otherwise a `ValueError` is thrown.
- callbacks :
- .. deprecated:: 1.6.0
- Use `callbacks` in :py:meth:`__init__` or :py:meth:`set_params` instead.
-
"""
with config_context(verbosity=self.verbosity):
train_dmatrix, evals = _wrap_evaluation_matrices(
@@ -2024,27 +1916,19 @@ class XGBRanker(XGBModel, XGBRankerMixIn):
evals_result: TrainingCallback.EvalsLog = {}
params = self.get_xgb_params()
- (
- model,
- metric,
- params,
- early_stopping_rounds,
- callbacks,
- ) = self._configure_fit(
- xgb_model, eval_metric, params, early_stopping_rounds, callbacks
- )
+ model, metric, params = self._configure_fit(xgb_model, params)
self._Booster = train(
params,
train_dmatrix,
num_boost_round=self.get_num_boosting_rounds(),
- early_stopping_rounds=early_stopping_rounds,
+ early_stopping_rounds=self.early_stopping_rounds,
evals=evals,
evals_result=evals_result,
custom_metric=metric,
verbose_eval=verbose,
xgb_model=model,
- callbacks=callbacks,
+ callbacks=self.callbacks,
)
self.objective = params["objective"]
diff --git a/python-package/xgboost/testing/continuation.py b/python-package/xgboost/testing/continuation.py
new file mode 100644
index 000000000..9d6dc0338
--- /dev/null
+++ b/python-package/xgboost/testing/continuation.py
@@ -0,0 +1,58 @@
+"""Tests for training continuation."""
+import json
+from typing import Any, Dict, TypeVar
+
+import numpy as np
+import pytest
+
+import xgboost as xgb
+
+
+# pylint: disable=too-many-locals
+def run_training_continuation_model_output(device: str, tree_method: str) -> None:
+ """Run training continuation test."""
+ datasets = pytest.importorskip("sklearn.datasets")
+ n_samples = 64
+ n_features = 32
+ X, y = datasets.make_regression(n_samples, n_features, random_state=1)
+
+ dtrain = xgb.DMatrix(X, y)
+ params = {
+ "tree_method": tree_method,
+ "max_depth": "2",
+ "gamma": "0.1",
+ "alpha": "0.01",
+ "device": device,
+ }
+ bst_0 = xgb.train(params, dtrain, num_boost_round=64)
+ dump_0 = bst_0.get_dump(dump_format="json")
+
+ bst_1 = xgb.train(params, dtrain, num_boost_round=32)
+ bst_1 = xgb.train(params, dtrain, num_boost_round=32, xgb_model=bst_1)
+ dump_1 = bst_1.get_dump(dump_format="json")
+
+ T = TypeVar("T", Dict[str, Any], float, str, int, list)
+
+ def recursive_compare(obj_0: T, obj_1: T) -> None:
+ if isinstance(obj_0, float):
+ assert np.isclose(obj_0, obj_1, atol=1e-6)
+ elif isinstance(obj_0, str):
+ assert obj_0 == obj_1
+ elif isinstance(obj_0, int):
+ assert obj_0 == obj_1
+ elif isinstance(obj_0, dict):
+ for i in range(len(obj_0.items())):
+ assert list(obj_0.keys())[i] == list(obj_1.keys())[i]
+ if list(obj_0.keys())[i] != "missing":
+ recursive_compare(list(obj_0.values()), list(obj_1.values()))
+ else:
+ for i, lhs in enumerate(obj_0):
+ rhs = obj_1[i]
+ recursive_compare(lhs, rhs)
+
+ assert len(dump_0) == len(dump_1)
+
+ for i, lhs in enumerate(dump_0):
+ obj_0 = json.loads(lhs)
+ obj_1 = json.loads(dump_1[i])
+ recursive_compare(obj_0, obj_1)
diff --git a/src/collective/coll.cc b/src/collective/coll.cc
index 3191896f8..5f14e4d9a 100644
--- a/src/collective/coll.cc
+++ b/src/collective/coll.cc
@@ -18,6 +18,8 @@
#if defined(XGBOOST_USE_CUDA)
#include "cuda_fp16.h" // for __half
+#elif defined(XGBOOST_USE_HIP)
+#include // for __half
#endif
namespace xgboost::collective {
diff --git a/src/collective/loop.cc b/src/collective/loop.cc
index 5cfb0034d..b51749fcd 100644
--- a/src/collective/loop.cc
+++ b/src/collective/loop.cc
@@ -1,11 +1,19 @@
/**
- * Copyright 2023, XGBoost Contributors
+ * Copyright 2023-2024, XGBoost Contributors
*/
#include "loop.h"
-#include // for queue
+#include // for size_t
+#include // for int32_t
+#include // for exception, current_exception, rethrow_exception
+#include // for lock_guard, unique_lock
+#include // for queue
+#include // for string
+#include // for thread
+#include // for move
#include "rabit/internal/socket.h" // for PollHelper
+#include "xgboost/collective/result.h" // for Fail, Success
#include "xgboost/collective/socket.h" // for FailWithCode
#include "xgboost/logging.h" // for CHECK
@@ -109,62 +117,94 @@ Result Loop::EmptyQueue(std::queue* p_queue) const {
}
void Loop::Process() {
- // consumer
- while (true) {
- std::unique_lock lock{mu_};
- cv_.wait(lock, [this] { return !this->queue_.empty() || stop_; });
- if (stop_) {
- break;
- }
+ auto set_rc = [this](Result&& rc) {
+ std::lock_guard lock{rc_lock_};
+ rc_ = std::forward(rc);
+ };
+
+ // This loop cannot exit unless `stop_` is set to true. There must always be a thread to
+ // answer the blocking call even if there are errors, otherwise the blocking will wait
+ // forever.
+ while (true) {
+ try {
+ std::unique_lock lock{mu_};
+ cv_.wait(lock, [this] { return !this->queue_.empty() || stop_; });
+ if (stop_) {
+ break; // only point where this loop can exit.
+ }
+
+ // Move the global queue into a local variable to unblock it.
+ std::queue qcopy;
+
+ bool is_blocking = false;
+ while (!queue_.empty()) {
+ auto op = queue_.front();
+ queue_.pop();
+ if (op.code == Op::kBlock) {
+ is_blocking = true;
+ // Block must be the last op in the current batch since no further submit can be
+ // issued until the blocking call is finished.
+ CHECK(queue_.empty());
+ } else {
+ qcopy.push(op);
+ }
+ }
- auto unlock_notify = [&](bool is_blocking, bool stop) {
if (!is_blocking) {
- std::lock_guard guard{mu_};
- stop_ = stop;
- } else {
- stop_ = stop;
+ // Unblock, we can write to the global queue again.
lock.unlock();
}
- cv_.notify_one();
- };
- // move the queue
- std::queue qcopy;
- bool is_blocking = false;
- while (!queue_.empty()) {
- auto op = queue_.front();
- queue_.pop();
- if (op.code == Op::kBlock) {
- is_blocking = true;
- } else {
- qcopy.push(op);
+ // Clear the local queue, this is blocking the current worker thread (but not the
+ // client thread), wait until all operations are finished.
+ auto rc = this->EmptyQueue(&qcopy);
+
+ if (is_blocking) {
+ // The unlock is delayed if this is a blocking call
+ lock.unlock();
}
- }
- // unblock the queue
- if (!is_blocking) {
- lock.unlock();
- }
- // clear the queue
- auto rc = this->EmptyQueue(&qcopy);
- // Handle error
- if (!rc.OK()) {
- unlock_notify(is_blocking, true);
- std::lock_guard guard{rc_lock_};
- this->rc_ = std::move(rc);
- return;
- }
- CHECK(qcopy.empty());
- unlock_notify(is_blocking, false);
+ // Notify the client thread who called block after all error conditions are set.
+ auto notify_if_block = [&] {
+ if (is_blocking) {
+ std::unique_lock lock{mu_};
+ block_done_ = true;
+ lock.unlock();
+ block_cv_.notify_one();
+ }
+ };
+
+ // Handle error
+ if (!rc.OK()) {
+ set_rc(std::move(rc));
+ } else {
+ CHECK(qcopy.empty());
+ }
+
+ notify_if_block();
+ } catch (std::exception const& e) {
+ curr_exce_ = std::current_exception();
+ set_rc(Fail("Exception inside the event loop:" + std::string{e.what()}));
+ } catch (...) {
+ curr_exce_ = std::current_exception();
+ set_rc(Fail("Unknown exception inside the event loop."));
+ }
}
}
Result Loop::Stop() {
+ // Finish all remaining tasks
+ CHECK_EQ(this->Block().OK(), this->rc_.OK());
+
+ // Notify the loop to stop
std::unique_lock lock{mu_};
stop_ = true;
lock.unlock();
+ this->cv_.notify_one();
- CHECK_EQ(this->Block().OK(), this->rc_.OK());
+ if (this->worker_.joinable()) {
+ this->worker_.join();
+ }
if (curr_exce_) {
std::rethrow_exception(curr_exce_);
@@ -175,17 +215,29 @@ Result Loop::Stop() {
[[nodiscard]] Result Loop::Block() {
{
+ // Check whether the last op was successful, stop if not.
std::lock_guard guard{rc_lock_};
if (!rc_.OK()) {
- return std::move(rc_);
+ stop_ = true;
}
}
- this->Submit(Op{Op::kBlock});
- {
- std::unique_lock lock{mu_};
- cv_.wait(lock, [this] { return (this->queue_.empty()) || stop_; });
+
+ if (!this->worker_.joinable()) {
+ std::lock_guard guard{rc_lock_};
+ return Fail("Worker has stopped.", std::move(rc_));
}
+
+ this->Submit(Op{Op::kBlock});
+
{
+ // Wait for the block call to finish.
+ std::unique_lock lock{mu_};
+ block_cv_.wait(lock, [this] { return block_done_ || stop_; });
+ block_done_ = false;
+ }
+
+ {
+ // Transfer the rc.
std::lock_guard lock{rc_lock_};
return std::move(rc_);
}
@@ -193,26 +245,6 @@ Result Loop::Stop() {
Loop::Loop(std::chrono::seconds timeout) : timeout_{timeout} {
timer_.Init(__func__);
- worker_ = std::thread{[this] {
- try {
- this->Process();
- } catch (std::exception const& e) {
- std::lock_guard guard{mu_};
- if (!curr_exce_) {
- curr_exce_ = std::current_exception();
- rc_ = Fail("Exception was thrown");
- }
- stop_ = true;
- cv_.notify_all();
- } catch (...) {
- std::lock_guard guard{mu_};
- if (!curr_exce_) {
- curr_exce_ = std::current_exception();
- rc_ = Fail("Exception was thrown");
- }
- stop_ = true;
- cv_.notify_all();
- }
- }};
+ worker_ = std::thread{[this] { this->Process(); }};
}
} // namespace xgboost::collective
diff --git a/src/collective/loop.h b/src/collective/loop.h
index 0c1fdcbfe..4839abfd3 100644
--- a/src/collective/loop.h
+++ b/src/collective/loop.h
@@ -1,5 +1,5 @@
/**
- * Copyright 2023, XGBoost Contributors
+ * Copyright 2023-2024, XGBoost Contributors
*/
#pragma once
#include // for seconds
@@ -10,7 +10,6 @@
#include // for unique_lock, mutex
#include // for queue
#include // for thread
-#include // for move
#include "../common/timer.h" // for Monitor
#include "xgboost/collective/result.h" // for Result
@@ -37,10 +36,15 @@ class Loop {
};
private:
- std::thread worker_;
- std::condition_variable cv_;
- std::mutex mu_;
- std::queue queue_;
+ std::thread worker_; // thread worker to execute the tasks
+
+ std::condition_variable cv_; // CV used to notify a new submit call
+ std::condition_variable block_cv_; // CV used to notify the blocking call
+ bool block_done_{false}; // Flag to indicate whether the blocking call has finished.
+
+ std::queue queue_; // event queue
+ std::mutex mu_; // mutex to protect the queue, cv, and block_done
+
std::chrono::seconds timeout_;
Result rc_;
@@ -51,29 +55,33 @@ class Loop {
common::Monitor mutable timer_;
Result EmptyQueue(std::queue* p_queue) const;
+ // The cunsumer function that runs inside a worker thread.
void Process();
public:
+ /**
+ * @brief Stop the worker thread.
+ */
Result Stop();
void Submit(Op op) {
- // producer
std::unique_lock lock{mu_};
queue_.push(op);
lock.unlock();
cv_.notify_one();
}
+ /**
+ * @brief Block the event loop until all ops are finished. In the case of failure, this
+ * loop should be not be used for new operations.
+ */
[[nodiscard]] Result Block();
explicit Loop(std::chrono::seconds timeout);
~Loop() noexcept(false) {
+ // The worker will be joined in the stop function.
this->Stop();
-
- if (worker_.joinable()) {
- worker_.join();
- }
}
};
} // namespace xgboost::collective
diff --git a/src/tree/gpu_hist/histogram.cu b/src/tree/gpu_hist/histogram.cu
index 409c1f260..62e40f4d4 100644
--- a/src/tree/gpu_hist/histogram.cu
+++ b/src/tree/gpu_hist/histogram.cu
@@ -318,7 +318,6 @@ void BuildGradientHistogram(CUDAContext const* ctx, EllpackDeviceAccessor const&
grid_size = std::min(common::DivRoundUp(grid_size, num_groups), static_cast(
common::DivRoundUp(items_per_group, kMinItemsPerBlock)));
#endif
-
dh::LaunchKernel {dim3(grid_size, num_groups), static_cast(kBlockThreads), smem_size,
ctx->Stream()} (kernel, matrix, feature_groups, d_ridx, histogram.data(),
gpair.data(), rounding);
diff --git a/src/tree/hist/evaluate_splits.h b/src/tree/hist/evaluate_splits.h
index 680c50398..bc534d351 100644
--- a/src/tree/hist/evaluate_splits.h
+++ b/src/tree/hist/evaluate_splits.h
@@ -730,6 +730,9 @@ class HistMultiEvaluator {
std::size_t n_nodes = p_tree->Size();
gain_.resize(n_nodes);
+ // Re-calculate weight without learning rate.
+ CalcWeight(*param_, left_sum, left_weight);
+ CalcWeight(*param_, right_sum, right_weight);
gain_[left_child] = CalcGainGivenWeight(*param_, left_sum, left_weight);
gain_[right_child] = CalcGainGivenWeight(*param_, right_sum, right_weight);
diff --git a/src/tree/multi_target_tree_model.cc b/src/tree/multi_target_tree_model.cc
index bccc1967e..11ee1f6dd 100644
--- a/src/tree/multi_target_tree_model.cc
+++ b/src/tree/multi_target_tree_model.cc
@@ -195,8 +195,9 @@ void MultiTargetTree::Expand(bst_node_t nidx, bst_feature_t split_idx, float spl
split_index_.resize(n);
split_index_[nidx] = split_idx;
- split_conds_.resize(n);
+ split_conds_.resize(n, std::numeric_limits::quiet_NaN());
split_conds_[nidx] = split_cond;
+
default_left_.resize(n);
default_left_[nidx] = static_cast(default_left);
diff --git a/src/tree/updater_quantile_hist.cc b/src/tree/updater_quantile_hist.cc
index 7731f505e..c2aaedafa 100644
--- a/src/tree/updater_quantile_hist.cc
+++ b/src/tree/updater_quantile_hist.cc
@@ -149,6 +149,9 @@ class MultiTargetHistBuilder {
}
void InitData(DMatrix *p_fmat, RegTree const *p_tree) {
+ if (collective::IsDistributed()) {
+ LOG(FATAL) << "Distributed training for vector-leaf is not yet supported.";
+ }
monitor_->Start(__func__);
p_last_fmat_ = p_fmat;
diff --git a/tests/README.md b/tests/README.md
index 4c29f4905..a118e7918 100644
--- a/tests/README.md
+++ b/tests/README.md
@@ -4,15 +4,13 @@ facilities.
# Directories
* ci_build: Test facilities for Jenkins CI and GitHub action.
* cli: Basic test for command line executable `xgboost`. Most of the other command line
- specific tests are in Python test `test_cli.py`
+ specific tests are in Python test `test_cli.py`.
* cpp: Tests for C++ core, using Google test framework.
* python: Tests for Python package, demonstrations and CLI. For how to setup the
dependencies for tests, see conda files in `ci_build`.
* python-gpu: Similar to python tests, but for GPU.
* travis: CI facilities for Travis.
- * distributed: Test for distributed system.
- * benchmark: Legacy benchmark code. There are a number of benchmark projects for
- XGBoost with much better configurations.
+ * test_distributed: Test for distributed systems including spark and dask.
# Others
* pytest.ini: Describes the `pytest` marker for python tests, some markers are generated
diff --git a/tests/benchmark/benchmark_linear.py b/tests/benchmark/benchmark_linear.py
deleted file mode 100644
index cb5141714..000000000
--- a/tests/benchmark/benchmark_linear.py
+++ /dev/null
@@ -1,69 +0,0 @@
-#pylint: skip-file
-import argparse
-import xgboost as xgb
-import numpy as np
-from sklearn.datasets import make_classification
-from sklearn.model_selection import train_test_split
-import time
-import ast
-
-rng = np.random.RandomState(1994)
-
-
-def run_benchmark(args):
-
- try:
- dtest = xgb.DMatrix('dtest.dm')
- dtrain = xgb.DMatrix('dtrain.dm')
-
- if not (dtest.num_col() == args.columns \
- and dtrain.num_col() == args.columns):
- raise ValueError("Wrong cols")
- if not (dtest.num_row() == args.rows * args.test_size \
- and dtrain.num_row() == args.rows * (1-args.test_size)):
- raise ValueError("Wrong rows")
- except:
-
- print("Generating dataset: {} rows * {} columns".format(args.rows, args.columns))
- print("{}/{} test/train split".format(args.test_size, 1.0 - args.test_size))
- tmp = time.time()
- X, y = make_classification(args.rows, n_features=args.columns, n_redundant=0, n_informative=args.columns, n_repeated=0, random_state=7)
- if args.sparsity < 1.0:
- X = np.array([[np.nan if rng.uniform(0, 1) < args.sparsity else x for x in x_row] for x_row in X])
-
- X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=args.test_size, random_state=7)
- print ("Generate Time: %s seconds" % (str(time.time() - tmp)))
- tmp = time.time()
- print ("DMatrix Start")
- dtrain = xgb.DMatrix(X_train, y_train)
- dtest = xgb.DMatrix(X_test, y_test, nthread=-1)
- print ("DMatrix Time: %s seconds" % (str(time.time() - tmp)))
-
- dtest.save_binary('dtest.dm')
- dtrain.save_binary('dtrain.dm')
-
- param = {'objective': 'binary:logistic','booster':'gblinear'}
- if args.params != '':
- param.update(ast.literal_eval(args.params))
-
- param['updater'] = args.updater
- print("Training with '%s'" % param['updater'])
- tmp = time.time()
- xgb.train(param, dtrain, args.iterations, evals=[(dtrain,"train")], early_stopping_rounds = args.columns)
- print ("Train Time: %s seconds" % (str(time.time() - tmp)))
-
-parser = argparse.ArgumentParser()
-parser.add_argument('--updater', default='coord_descent')
-parser.add_argument('--sparsity', type=float, default=0.0)
-parser.add_argument('--lambda', type=float, default=1.0)
-parser.add_argument('--tol', type=float, default=1e-5)
-parser.add_argument('--alpha', type=float, default=1.0)
-parser.add_argument('--rows', type=int, default=1000000)
-parser.add_argument('--iterations', type=int, default=10000)
-parser.add_argument('--columns', type=int, default=50)
-parser.add_argument('--test_size', type=float, default=0.25)
-parser.add_argument('--standardise', type=bool, default=False)
-parser.add_argument('--params', default='', help='Provide additional parameters as a Python dict string, e.g. --params \"{\'max_depth\':2}\"')
-args = parser.parse_args()
-
-run_benchmark(args)
diff --git a/tests/benchmark/benchmark_tree.py b/tests/benchmark/benchmark_tree.py
deleted file mode 100644
index 380e03463..000000000
--- a/tests/benchmark/benchmark_tree.py
+++ /dev/null
@@ -1,86 +0,0 @@
-"""Run benchmark on the tree booster."""
-
-import argparse
-import ast
-import time
-
-import numpy as np
-import xgboost as xgb
-
-RNG = np.random.RandomState(1994)
-
-
-def run_benchmark(args):
- """Runs the benchmark."""
- try:
- dtest = xgb.DMatrix('dtest.dm')
- dtrain = xgb.DMatrix('dtrain.dm')
-
- if not (dtest.num_col() == args.columns
- and dtrain.num_col() == args.columns):
- raise ValueError("Wrong cols")
- if not (dtest.num_row() == args.rows * args.test_size
- and dtrain.num_row() == args.rows * (1 - args.test_size)):
- raise ValueError("Wrong rows")
- except:
- print("Generating dataset: {} rows * {} columns".format(args.rows, args.columns))
- print("{}/{} test/train split".format(args.test_size, 1.0 - args.test_size))
- tmp = time.time()
- X = RNG.rand(args.rows, args.columns)
- y = RNG.randint(0, 2, args.rows)
- if 0.0 < args.sparsity < 1.0:
- X = np.array([[np.nan if RNG.uniform(0, 1) < args.sparsity else x for x in x_row]
- for x_row in X])
-
- train_rows = int(args.rows * (1.0 - args.test_size))
- test_rows = int(args.rows * args.test_size)
- X_train = X[:train_rows, :]
- X_test = X[-test_rows:, :]
- y_train = y[:train_rows]
- y_test = y[-test_rows:]
- print("Generate Time: %s seconds" % (str(time.time() - tmp)))
- del X, y
-
- tmp = time.time()
- print("DMatrix Start")
- dtrain = xgb.DMatrix(X_train, y_train, nthread=-1)
- dtest = xgb.DMatrix(X_test, y_test, nthread=-1)
- print("DMatrix Time: %s seconds" % (str(time.time() - tmp)))
- del X_train, y_train, X_test, y_test
-
- dtest.save_binary('dtest.dm')
- dtrain.save_binary('dtrain.dm')
-
- param = {'objective': 'binary:logistic'}
- if args.params != '':
- param.update(ast.literal_eval(args.params))
-
- param['tree_method'] = args.tree_method
- print("Training with '%s'" % param['tree_method'])
- tmp = time.time()
- xgb.train(param, dtrain, args.iterations, evals=[(dtest, "test")])
- print("Train Time: %s seconds" % (str(time.time() - tmp)))
-
-
-def main():
- """The main function.
-
- Defines and parses command line arguments and calls the benchmark.
- """
- parser = argparse.ArgumentParser()
- parser.add_argument('--tree_method', default='gpu_hist')
- parser.add_argument('--sparsity', type=float, default=0.0)
- parser.add_argument('--rows', type=int, default=1000000)
- parser.add_argument('--columns', type=int, default=50)
- parser.add_argument('--iterations', type=int, default=500)
- parser.add_argument('--test_size', type=float, default=0.25)
- parser.add_argument('--params', default='',
- help='Provide additional parameters as a Python dict string, e.g. --params '
- '\"{\'max_depth\':2}\"')
- args = parser.parse_args()
-
- run_benchmark(args)
-
-
-if __name__ == '__main__':
- main()
diff --git a/tests/benchmark/generate_libsvm.py b/tests/benchmark/generate_libsvm.py
deleted file mode 100644
index be152df39..000000000
--- a/tests/benchmark/generate_libsvm.py
+++ /dev/null
@@ -1,87 +0,0 @@
-"""Generate synthetic data in LIBSVM format."""
-
-import argparse
-import io
-import time
-
-import numpy as np
-from sklearn.datasets import make_classification
-from sklearn.model_selection import train_test_split
-
-RNG = np.random.RandomState(2019)
-
-
-def generate_data(args):
- """Generates the data."""
- print("Generating dataset: {} rows * {} columns".format(args.rows, args.columns))
- print("Sparsity {}".format(args.sparsity))
- print("{}/{} train/test split".format(1.0 - args.test_size, args.test_size))
-
- tmp = time.time()
- n_informative = args.columns * 7 // 10
- n_redundant = args.columns // 10
- n_repeated = args.columns // 10
- print("n_informative: {}, n_redundant: {}, n_repeated: {}".format(n_informative, n_redundant,
- n_repeated))
- x, y = make_classification(n_samples=args.rows, n_features=args.columns,
- n_informative=n_informative, n_redundant=n_redundant,
- n_repeated=n_repeated, shuffle=False, random_state=RNG)
- print("Generate Time: {} seconds".format(time.time() - tmp))
-
- tmp = time.time()
- x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=args.test_size,
- random_state=RNG, shuffle=False)
- print("Train/Test Split Time: {} seconds".format(time.time() - tmp))
-
- tmp = time.time()
- write_file('train.libsvm', x_train, y_train, args.sparsity)
- print("Write Train Time: {} seconds".format(time.time() - tmp))
-
- tmp = time.time()
- write_file('test.libsvm', x_test, y_test, args.sparsity)
- print("Write Test Time: {} seconds".format(time.time() - tmp))
-
-
-def write_file(filename, x_data, y_data, sparsity):
- with open(filename, 'w') as f:
- for x, y in zip(x_data, y_data):
- write_line(f, x, y, sparsity)
-
-
-def write_line(f, x, y, sparsity):
- with io.StringIO() as line:
- line.write(str(y))
- for i, col in enumerate(x):
- if 0.0 < sparsity < 1.0:
- if RNG.uniform(0, 1) > sparsity:
- write_feature(line, i, col)
- else:
- write_feature(line, i, col)
- line.write('\n')
- f.write(line.getvalue())
-
-
-def write_feature(line, index, feature):
- line.write(' ')
- line.write(str(index))
- line.write(':')
- line.write(str(feature))
-
-
-def main():
- """The main function.
-
- Defines and parses command line arguments and calls the generator.
- """
- parser = argparse.ArgumentParser()
- parser.add_argument('--rows', type=int, default=1000000)
- parser.add_argument('--columns', type=int, default=50)
- parser.add_argument('--sparsity', type=float, default=0.0)
- parser.add_argument('--test_size', type=float, default=0.01)
- args = parser.parse_args()
-
- generate_data(args)
-
-
-if __name__ == '__main__':
- main()
diff --git a/tests/buildkite/build-jvm-packages.sh b/tests/buildkite/build-jvm-packages.sh
index 12393c561..1998385c5 100755
--- a/tests/buildkite/build-jvm-packages.sh
+++ b/tests/buildkite/build-jvm-packages.sh
@@ -8,13 +8,18 @@ echo "--- Build XGBoost JVM packages scala 2.12"
tests/ci_build/ci_build.sh jvm tests/ci_build/build_jvm_packages.sh \
${SPARK_VERSION}
+echo "--- Stash XGBoost4J JARs (Scala 2.12)"
+buildkite-agent artifact upload "jvm-packages/xgboost4j/target/*.jar"
+buildkite-agent artifact upload "jvm-packages/xgboost4j-spark/target/*.jar"
+buildkite-agent artifact upload "jvm-packages/xgboost4j-flink/target/*.jar"
+buildkite-agent artifact upload "jvm-packages/xgboost4j-example/target/*.jar"
echo "--- Build XGBoost JVM packages scala 2.13"
tests/ci_build/ci_build.sh jvm tests/ci_build/build_jvm_packages.sh \
${SPARK_VERSION} "" "" "true"
-echo "--- Stash XGBoost4J JARs"
+echo "--- Stash XGBoost4J JARs (Scala 2.13)"
buildkite-agent artifact upload "jvm-packages/xgboost4j/target/*.jar"
buildkite-agent artifact upload "jvm-packages/xgboost4j-spark/target/*.jar"
buildkite-agent artifact upload "jvm-packages/xgboost4j-flink/target/*.jar"
diff --git a/tests/buildkite/build-rpkg-win64-gpu.ps1 b/tests/buildkite/build-rpkg-win64-gpu.ps1
deleted file mode 100644
index a6947c270..000000000
--- a/tests/buildkite/build-rpkg-win64-gpu.ps1
+++ /dev/null
@@ -1,21 +0,0 @@
-$ErrorActionPreference = "Stop"
-
-. tests/buildkite/conftest.ps1
-
-Write-Host "--- Build XGBoost R package with CUDA"
-
-nvcc --version
-$arch_flag = "-DGPU_COMPUTE_VER=75"
-
-bash tests/ci_build/build_r_pkg_with_cuda_win64.sh $Env:BUILDKITE_COMMIT
-if ($LASTEXITCODE -ne 0) { throw "Last command failed" }
-
-if ( $is_release_branch -eq 1 ) {
- Write-Host "--- Upload R tarball"
- Get-ChildItem . -Filter xgboost_r_gpu_win64_*.tar.gz |
- Foreach-Object {
- & aws s3 cp $_ s3://xgboost-nightly-builds/$Env:BUILDKITE_BRANCH/ `
- --acl public-read --no-progress
- if ($LASTEXITCODE -ne 0) { throw "Last command failed" }
- }
-}
diff --git a/tests/buildkite/pipeline-win64.yml b/tests/buildkite/pipeline-win64.yml
index d4491148e..83a61981e 100644
--- a/tests/buildkite/pipeline-win64.yml
+++ b/tests/buildkite/pipeline-win64.yml
@@ -13,11 +13,6 @@ steps:
key: build-win64-gpu
agents:
queue: windows-cpu
- - label: ":windows: Build XGBoost R package for Windows with CUDA"
- command: "tests/buildkite/build-rpkg-win64-gpu.ps1"
- key: build-rpkg-win64-gpu
- agents:
- queue: windows-cpu
- wait
diff --git a/tests/ci_build/build_jvm_packages.sh b/tests/ci_build/build_jvm_packages.sh
index 5797a1f61..84b41f2b1 100755
--- a/tests/ci_build/build_jvm_packages.sh
+++ b/tests/ci_build/build_jvm_packages.sh
@@ -24,12 +24,13 @@ if [ "x$gpu_arch" != "x" ]; then
export GPU_ARCH_FLAG=$gpu_arch
fi
-mvn_profile_string=""
if [ "x$use_scala213" != "x" ]; then
- export mvn_profile_string="-Pdefault,scala-2.13"
+ cd ..
+ python dev/change_scala_version.py --scala-version 2.13 --purge-artifacts
+ cd jvm-packages
fi
-mvn --no-transfer-progress package $mvn_profile_string -Dspark.version=${spark_version} $gpu_options
+mvn --no-transfer-progress package -Dspark.version=${spark_version} $gpu_options
set +x
set +e
diff --git a/tests/ci_build/build_r_pkg_with_cuda_win64.sh b/tests/ci_build/build_r_pkg_with_cuda_win64.sh
deleted file mode 100644
index 580358883..000000000
--- a/tests/ci_build/build_r_pkg_with_cuda_win64.sh
+++ /dev/null
@@ -1,36 +0,0 @@
-#!/bin/bash
-set -e
-set -x
-
-if [ "$#" -ne 1 ]
-then
- echo "Build the R package tarball with CUDA code. Usage: $0 [commit hash]"
- exit 1
-fi
-
-commit_hash="$1"
-# Clear all positional args
-set --
-
-source activate
-python tests/ci_build/test_r_package.py --task=pack
-mv xgboost/ xgboost_rpack/
-
-mkdir build
-cd build
-cmake .. -G"Visual Studio 17 2022" -A x64 -DUSE_CUDA=ON -DR_LIB=ON -DLIBR_HOME="c:\\Program Files\\R\\R-4.3.2" -DCMAKE_PREFIX_PATH="C:\\rtools43\\x86_64-w64-mingw32.static.posix\\bin"
-cmake --build . --config Release --parallel
-cd ..
-
-# This super wacky hack is found in cmake/RPackageInstall.cmake.in and
-# cmake/RPackageInstallTargetSetup.cmake. This hack lets us bypass the normal build process of R
-# and have R use xgboost.dll that we've already built.
-rm -v xgboost_rpack/configure
-rm -rfv xgboost_rpack/src
-mkdir -p xgboost_rpack/src
-cp -v lib/xgboost.dll xgboost_rpack/src/
-echo 'all:' > xgboost_rpack/src/Makefile
-echo 'all:' > xgboost_rpack/src/Makefile.win
-mv xgboost_rpack/ xgboost/
-/c/Rtools43/usr/bin/tar -cvf xgboost_r_gpu_win64_${commit_hash}.tar xgboost/
-/c/Rtools43/usr/bin/gzip -9c xgboost_r_gpu_win64_${commit_hash}.tar > xgboost_r_gpu_win64_${commit_hash}.tar.gz
diff --git a/tests/ci_build/deploy_jvm_packages.sh b/tests/ci_build/deploy_jvm_packages.sh
index 5f448ee2a..9531d79a9 100755
--- a/tests/ci_build/deploy_jvm_packages.sh
+++ b/tests/ci_build/deploy_jvm_packages.sh
@@ -27,7 +27,10 @@ rm -rf ../build/
# Deploy to S3 bucket xgboost-maven-repo
mvn --no-transfer-progress package deploy -P default,gpu,release-to-s3 -Dspark.version=${spark_version} -DskipTests
# Deploy scala 2.13 to S3 bucket xgboost-maven-repo
-mvn --no-transfer-progress package deploy -P release-to-s3,default,scala-2.13 -Dspark.version=${spark_version} -DskipTests
+cd ..
+python dev/change_scala_version.py --scala-version 2.13 --purge-artifacts
+cd jvm-packages/
+mvn --no-transfer-progress package deploy -P default,gpu,release-to-s3 -Dspark.version=${spark_version} -DskipTests
set +x
diff --git a/tests/ci_build/lint_python.py b/tests/ci_build/lint_python.py
index 87d76607f..91b748b4c 100644
--- a/tests/ci_build/lint_python.py
+++ b/tests/ci_build/lint_python.py
@@ -18,14 +18,17 @@ class LintersPaths:
"python-package/",
# tests
"tests/python/test_config.py",
+ "tests/python/test_callback.py",
"tests/python/test_data_iterator.py",
"tests/python/test_dmatrix.py",
"tests/python/test_dt.py",
"tests/python/test_demos.py",
+ "tests/python/test_eval_metrics.py",
"tests/python/test_multi_target.py",
"tests/python/test_predict.py",
"tests/python/test_quantile_dmatrix.py",
"tests/python/test_tree_regularization.py",
+ "tests/python/test_training_continuation.py",
"tests/python/test_shap.py",
"tests/python/test_model_io.py",
"tests/python/test_with_pandas.py",
@@ -39,12 +42,15 @@ class LintersPaths:
"demo/dask/",
"demo/rmm_plugin",
"demo/json-model/json_parser.py",
+ "demo/guide-python/continuation.py",
"demo/guide-python/cat_in_the_dat.py",
"demo/guide-python/callbacks.py",
"demo/guide-python/categorical.py",
"demo/guide-python/cat_pipeline.py",
"demo/guide-python/feature_weights.py",
"demo/guide-python/sklearn_parallel.py",
+ "demo/guide-python/sklearn_examples.py",
+ "demo/guide-python/sklearn_evals_result.py",
"demo/guide-python/spark_estimator_examples.py",
"demo/guide-python/external_memory.py",
"demo/guide-python/individual_trees.py",
@@ -86,6 +92,7 @@ class LintersPaths:
"tests/python/test_multi_target.py",
"tests/python-gpu/test_gpu_data_iterator.py",
"tests/python-gpu/load_pickle.py",
+ "tests/python-gpu/test_gpu_training_continuation.py",
"tests/python/test_model_io.py",
"tests/test_distributed/test_with_spark/test_data.py",
"tests/test_distributed/test_gpu_with_spark/test_data.py",
@@ -93,6 +100,7 @@ class LintersPaths:
# demo
"demo/json-model/json_parser.py",
"demo/guide-python/external_memory.py",
+ "demo/guide-python/continuation.py",
"demo/guide-python/callbacks.py",
"demo/guide-python/cat_in_the_dat.py",
"demo/guide-python/categorical.py",
diff --git a/tests/ci_build/test_jvm_cross.sh b/tests/ci_build/test_jvm_cross.sh
index 18265cf01..4e049fce1 100755
--- a/tests/ci_build/test_jvm_cross.sh
+++ b/tests/ci_build/test_jvm_cross.sh
@@ -20,10 +20,11 @@ if [ ! -z "$RUN_INTEGRATION_TEST" ]; then
cd $jvm_packages_dir
fi
-# including maven profiles for different scala versions: 2.12 is the default at the moment.
-for _maven_profile_string in "" "-Pdefault,scala-2.13"; do
- scala_version=$(mvn help:evaluate $_maven_profile_string -Dexpression=scala.version -q -DforceStdout)
- scala_binary_version=$(mvn help:evaluate $_maven_profile_string -Dexpression=scala.binary.version -q -DforceStdout)
+for scala_binary_version in "2.12" "2.13"; do
+ cd ..
+ python dev/change_scala_version.py --scala-version ${scala_binary_version}
+ cd jvm-packages
+ scala_version=$(mvn help:evaluate -Dexpression=scala.version -q -DforceStdout)
# Install XGBoost4J JAR into local Maven repository
mvn --no-transfer-progress install:install-file -Dfile=./xgboost4j/target/xgboost4j_${scala_binary_version}-${xgboost4j_version}.jar -DgroupId=ml.dmlc -DartifactId=xgboost4j_${scala_binary_version} -Dversion=${xgboost4j_version} -Dpackaging=jar
diff --git a/tests/cpp/tree/test_quantile_hist.cc b/tests/cpp/tree/test_quantile_hist.cc
index 6327703ed..cf806536a 100644
--- a/tests/cpp/tree/test_quantile_hist.cc
+++ b/tests/cpp/tree/test_quantile_hist.cc
@@ -253,6 +253,5 @@ void TestColumnSplit(bst_target_t n_targets) {
TEST(QuantileHist, ColumnSplit) { TestColumnSplit(1); }
-TEST(QuantileHist, ColumnSplitMultiTarget) { TestColumnSplit(3); }
-
+TEST(QuantileHist, DISABLED_ColumnSplitMultiTarget) { TestColumnSplit(3); }
} // namespace xgboost::tree
diff --git a/tests/cpp/tree/test_tree_stat.cc b/tests/cpp/tree/test_tree_stat.cc
index 7f3e3bc94..dab0f3f4a 100644
--- a/tests/cpp/tree/test_tree_stat.cc
+++ b/tests/cpp/tree/test_tree_stat.cc
@@ -1,18 +1,21 @@
/**
- * Copyright 2020-2023 by XGBoost Contributors
+ * Copyright 2020-2024, XGBoost Contributors
*/
#include
-#include // for Context
-#include // for ObjInfo
-#include
-#include
+#include // for Context
+#include // for ObjInfo
+#include // for RegTree
+#include // for TreeUpdater
-#include // for unique_ptr
+#include // for unique_ptr
#include "../../../src/tree/param.h" // for TrainParam
#include "../helpers.h"
namespace xgboost {
+/**
+ * @brief Test the tree statistic (like sum Hessian) is correct.
+ */
class UpdaterTreeStatTest : public ::testing::Test {
protected:
std::shared_ptr p_dmat_;
@@ -28,13 +31,12 @@ class UpdaterTreeStatTest : public ::testing::Test {
gpairs_.Data()->Copy(g);
}
- void RunTest(std::string updater) {
+ void RunTest(Context const* ctx, std::string updater) {
tree::TrainParam param;
ObjInfo task{ObjInfo::kRegression};
param.Init(Args{});
- Context ctx(updater == "grow_gpu_hist" ? MakeCUDACtx(0) : MakeCUDACtx(DeviceOrd::CPUOrdinal()));
- auto up = std::unique_ptr{TreeUpdater::Create(updater, &ctx, &task)};
+ auto up = std::unique_ptr{TreeUpdater::Create(updater, ctx, &task)};
up->Configure(Args{});
RegTree tree{1u, kCols};
std::vector> position(1);
@@ -51,77 +53,136 @@ class UpdaterTreeStatTest : public ::testing::Test {
};
#if defined(XGBOOST_USE_CUDA) || defined(XGBOOST_USE_HIP)
-TEST_F(UpdaterTreeStatTest, GpuHist) { this->RunTest("grow_gpu_hist"); }
-#endif // defined(XGBOOST_USE_CUDA) || defined(XGBOOST_USE_HIP)
+TEST_F(UpdaterTreeStatTest, GpuHist) {
+ auto ctx = MakeCUDACtx(0);
+ this->RunTest(&ctx, "grow_gpu_hist");
+}
-TEST_F(UpdaterTreeStatTest, Hist) { this->RunTest("grow_quantile_histmaker"); }
+TEST_F(UpdaterTreeStatTest, GpuApprox) {
+ auto ctx = MakeCUDACtx(0);
+ this->RunTest(&ctx, "grow_gpu_approx");
+}
+#endif // defined(XGBOOST_USE_CUDA)
-TEST_F(UpdaterTreeStatTest, Exact) { this->RunTest("grow_colmaker"); }
+TEST_F(UpdaterTreeStatTest, Hist) {
+ Context ctx;
+ this->RunTest(&ctx, "grow_quantile_histmaker");
+}
-TEST_F(UpdaterTreeStatTest, Approx) { this->RunTest("grow_histmaker"); }
+TEST_F(UpdaterTreeStatTest, Exact) {
+ Context ctx;
+ this->RunTest(&ctx, "grow_colmaker");
+}
-class UpdaterEtaTest : public ::testing::Test {
+TEST_F(UpdaterTreeStatTest, Approx) {
+ Context ctx;
+ this->RunTest(&ctx, "grow_histmaker");
+}
+
+/**
+ * @brief Test changing learning rate doesn't change internal splits.
+ */
+class TestSplitWithEta : public ::testing::Test {
protected:
- std::shared_ptr p_dmat_;
- linalg::Matrix gpairs_;
- size_t constexpr static kRows = 10;
- size_t constexpr static kCols = 10;
- size_t constexpr static kClasses = 10;
+ void Run(Context const* ctx, bst_target_t n_targets, std::string name) {
+ auto Xy = RandomDataGenerator{512, 64, 0.2}.Targets(n_targets).GenerateDMatrix(true);
- void SetUp() override {
- p_dmat_ = RandomDataGenerator(kRows, kCols, .5f).GenerateDMatrix(true, false, kClasses);
- auto g = GenerateRandomGradients(kRows);
- gpairs_.Reshape(kRows, 1);
- gpairs_.Data()->Copy(g);
- }
+ auto gen_tree = [&](float eta) {
+ auto tree =
+ std::make_unique(n_targets, static_cast(Xy->Info().num_col_));
+ std::vector trees{tree.get()};
+ ObjInfo task{ObjInfo::kRegression};
+ std::unique_ptr updater{TreeUpdater::Create(name, ctx, &task)};
+ updater->Configure({});
- void RunTest(std::string updater) {
- ObjInfo task{ObjInfo::kClassification};
+ auto grad = GenerateRandomGradients(ctx, Xy->Info().num_row_, n_targets);
+ CHECK_EQ(grad.Shape(1), n_targets);
+ tree::TrainParam param;
+ param.Init(Args{{"learning_rate", std::to_string(eta)}});
+ HostDeviceVector position;
- Context ctx(updater == "grow_gpu_hist" ? MakeCUDACtx(0) : MakeCUDACtx(DeviceOrd::CPUOrdinal()));
-
- float eta = 0.4;
- auto up_0 = std::unique_ptr{TreeUpdater::Create(updater, &ctx, &task)};
- up_0->Configure(Args{});
- tree::TrainParam param0;
- param0.Init(Args{{"eta", std::to_string(eta)}});
-
- auto up_1 = std::unique_ptr{TreeUpdater::Create(updater, &ctx, &task)};
- up_1->Configure(Args{{"eta", "1.0"}});
- tree::TrainParam param1;
- param1.Init(Args{{"eta", "1.0"}});
-
- for (size_t iter = 0; iter < 4; ++iter) {
- RegTree tree_0{1u, kCols};
- {
- std::vector> position(1);
- up_0->Update(¶m0, &gpairs_, p_dmat_.get(), position, {&tree_0});
+ updater->Update(¶m, &grad, Xy.get(), common::Span{&position, 1}, trees);
+ CHECK_EQ(tree->NumTargets(), n_targets);
+ if (n_targets > 1) {
+ CHECK(tree->IsMultiTarget());
}
+ return tree;
+ };
- RegTree tree_1{1u, kCols};
- {
- std::vector> position(1);
- up_1->Update(¶m1, &gpairs_, p_dmat_.get(), position, {&tree_1});
- }
- tree_0.WalkTree([&](bst_node_t nidx) {
- if (tree_0[nidx].IsLeaf()) {
- EXPECT_NEAR(tree_1[nidx].LeafValue() * eta, tree_0[nidx].LeafValue(), kRtEps);
+ auto eta_ratio = 8.0f;
+ auto p_tree0 = gen_tree(0.1f);
+ auto p_tree1 = gen_tree(0.1f * eta_ratio);
+ // Just to make sure we are not testing a stump.
+ CHECK_GE(p_tree0->NumExtraNodes(), 32);
+
+ bst_node_t n_nodes{0};
+ p_tree0->WalkTree([&](bst_node_t nidx) {
+ if (p_tree0->IsLeaf(nidx)) {
+ CHECK(p_tree1->IsLeaf(nidx));
+ if (p_tree0->IsMultiTarget()) {
+ CHECK(p_tree1->IsMultiTarget());
+ auto leaf_0 = p_tree0->GetMultiTargetTree()->LeafValue(nidx);
+ auto leaf_1 = p_tree1->GetMultiTargetTree()->LeafValue(nidx);
+ CHECK_EQ(leaf_0.Size(), leaf_1.Size());
+ for (std::size_t i = 0; i < leaf_0.Size(); ++i) {
+ CHECK_EQ(leaf_0(i) * eta_ratio, leaf_1(i));
+ }
+ CHECK(std::isnan(p_tree0->SplitCond(nidx)));
+ CHECK(std::isnan(p_tree1->SplitCond(nidx)));
+ } else {
+ // NON-mt tree reuses split cond for leaf value.
+ auto leaf_0 = p_tree0->SplitCond(nidx);
+ auto leaf_1 = p_tree1->SplitCond(nidx);
+ CHECK_EQ(leaf_0 * eta_ratio, leaf_1);
}
- return true;
- });
- }
+ } else {
+ CHECK(!p_tree1->IsLeaf(nidx));
+ CHECK_EQ(p_tree0->SplitCond(nidx), p_tree1->SplitCond(nidx));
+ }
+ n_nodes++;
+ return true;
+ });
+ ASSERT_EQ(n_nodes, p_tree0->NumExtraNodes() + 1);
}
};
-TEST_F(UpdaterEtaTest, Hist) { this->RunTest("grow_quantile_histmaker"); }
+TEST_F(TestSplitWithEta, HistMulti) {
+ Context ctx;
+ bst_target_t n_targets{3};
+ this->Run(&ctx, n_targets, "grow_quantile_histmaker");
+}
-TEST_F(UpdaterEtaTest, Exact) { this->RunTest("grow_colmaker"); }
+TEST_F(TestSplitWithEta, Hist) {
+ Context ctx;
+ bst_target_t n_targets{1};
+ this->Run(&ctx, n_targets, "grow_quantile_histmaker");
+}
-TEST_F(UpdaterEtaTest, Approx) { this->RunTest("grow_histmaker"); }
+TEST_F(TestSplitWithEta, Approx) {
+ Context ctx;
+ bst_target_t n_targets{1};
+ this->Run(&ctx, n_targets, "grow_histmaker");
+}
+
+TEST_F(TestSplitWithEta, Exact) {
+ Context ctx;
+ bst_target_t n_targets{1};
+ this->Run(&ctx, n_targets, "grow_colmaker");
+}
#if defined(XGBOOST_USE_CUDA) || defined(XGBOOST_USE_HIP)
-TEST_F(UpdaterEtaTest, GpuHist) { this->RunTest("grow_gpu_hist"); }
-#endif // defined(XGBOOST_USE_CUDA) || defined(XGBOOST_USE_HIP)
+TEST_F(TestSplitWithEta, GpuHist) {
+ auto ctx = MakeCUDACtx(0);
+ bst_target_t n_targets{1};
+ this->Run(&ctx, n_targets, "grow_gpu_hist");
+}
+
+TEST_F(TestSplitWithEta, GpuApprox) {
+ auto ctx = MakeCUDACtx(0);
+ bst_target_t n_targets{1};
+ this->Run(&ctx, n_targets, "grow_gpu_approx");
+}
+#endif // defined(XGBOOST_USE_CUDA)
class TestMinSplitLoss : public ::testing::Test {
std::shared_ptr dmat_;
diff --git a/tests/python-gpu/test_gpu_training_continuation.py b/tests/python-gpu/test_gpu_training_continuation.py
index a67d2f26b..6f948890d 100644
--- a/tests/python-gpu/test_gpu_training_continuation.py
+++ b/tests/python-gpu/test_gpu_training_continuation.py
@@ -1,54 +1,12 @@
-import json
-
import numpy as np
+import pytest
-import xgboost as xgb
+from xgboost.testing.continuation import run_training_continuation_model_output
rng = np.random.RandomState(1994)
class TestGPUTrainingContinuation:
- def test_training_continuation(self):
- kRows = 64
- kCols = 32
- X = np.random.randn(kRows, kCols)
- y = np.random.randn(kRows)
- dtrain = xgb.DMatrix(X, y)
- params = {
- "tree_method": "gpu_hist",
- "max_depth": "2",
- "gamma": "0.1",
- "alpha": "0.01",
- }
- bst_0 = xgb.train(params, dtrain, num_boost_round=64)
- dump_0 = bst_0.get_dump(dump_format="json")
-
- bst_1 = xgb.train(params, dtrain, num_boost_round=32)
- bst_1 = xgb.train(params, dtrain, num_boost_round=32, xgb_model=bst_1)
- dump_1 = bst_1.get_dump(dump_format="json")
-
- def recursive_compare(obj_0, obj_1):
- if isinstance(obj_0, float):
- assert np.isclose(obj_0, obj_1, atol=1e-6)
- elif isinstance(obj_0, str):
- assert obj_0 == obj_1
- elif isinstance(obj_0, int):
- assert obj_0 == obj_1
- elif isinstance(obj_0, dict):
- keys_0 = list(obj_0.keys())
- keys_1 = list(obj_1.keys())
- values_0 = list(obj_0.values())
- values_1 = list(obj_1.values())
- for i in range(len(obj_0.items())):
- assert keys_0[i] == keys_1[i]
- if list(obj_0.keys())[i] != "missing":
- recursive_compare(values_0[i], values_1[i])
- else:
- for i in range(len(obj_0)):
- recursive_compare(obj_0[i], obj_1[i])
-
- assert len(dump_0) == len(dump_1)
- for i in range(len(dump_0)):
- obj_0 = json.loads(dump_0[i])
- obj_1 = json.loads(dump_1[i])
- recursive_compare(obj_0, obj_1)
+ @pytest.mark.parametrize("tree_method", ["hist", "approx"])
+ def test_model_output(self, tree_method: str) -> None:
+ run_training_continuation_model_output("cuda", tree_method)
diff --git a/tests/python/test_callback.py b/tests/python/test_callback.py
index 3a7501e48..d2e7cb5c4 100644
--- a/tests/python/test_callback.py
+++ b/tests/python/test_callback.py
@@ -16,13 +16,14 @@ class TestCallbacks:
@classmethod
def setup_class(cls):
from sklearn.datasets import load_breast_cancer
+
X, y = load_breast_cancer(return_X_y=True)
cls.X = X
cls.y = y
- split = int(X.shape[0]*0.8)
- cls.X_train = X[: split, ...]
- cls.y_train = y[: split, ...]
+ split = int(X.shape[0] * 0.8)
+ cls.X_train = X[:split, ...]
+ cls.y_train = y[:split, ...]
cls.X_valid = X[split:, ...]
cls.y_valid = y[split:, ...]
@@ -31,31 +32,32 @@ class TestCallbacks:
D_train: xgb.DMatrix,
D_valid: xgb.DMatrix,
rounds: int,
- verbose_eval: Union[bool, int]
+ verbose_eval: Union[bool, int],
):
def check_output(output: str) -> None:
if int(verbose_eval) == 1:
# Should print each iteration info
- assert len(output.split('\n')) == rounds
+ assert len(output.split("\n")) == rounds
elif int(verbose_eval) > rounds:
# Should print first and latest iteration info
- assert len(output.split('\n')) == 2
+ assert len(output.split("\n")) == 2
else:
# Should print info by each period additionaly to first and latest
# iteration
num_periods = rounds // int(verbose_eval)
# Extra information is required for latest iteration
is_extra_info_required = num_periods * int(verbose_eval) < (rounds - 1)
- assert len(output.split('\n')) == (
+ assert len(output.split("\n")) == (
1 + num_periods + int(is_extra_info_required)
)
evals_result: xgb.callback.TrainingCallback.EvalsLog = {}
- params = {'objective': 'binary:logistic', 'eval_metric': 'error'}
+ params = {"objective": "binary:logistic", "eval_metric": "error"}
with tm.captured_output() as (out, err):
xgb.train(
- params, D_train,
- evals=[(D_train, 'Train'), (D_valid, 'Valid')],
+ params,
+ D_train,
+ evals=[(D_train, "Train"), (D_valid, "Valid")],
num_boost_round=rounds,
evals_result=evals_result,
verbose_eval=verbose_eval,
@@ -73,14 +75,16 @@ class TestCallbacks:
D_valid = xgb.DMatrix(self.X_valid, self.y_valid)
evals_result = {}
rounds = 10
- xgb.train({'objective': 'binary:logistic',
- 'eval_metric': 'error'}, D_train,
- evals=[(D_train, 'Train'), (D_valid, 'Valid')],
- num_boost_round=rounds,
- evals_result=evals_result,
- verbose_eval=True)
- assert len(evals_result['Train']['error']) == rounds
- assert len(evals_result['Valid']['error']) == rounds
+ xgb.train(
+ {"objective": "binary:logistic", "eval_metric": "error"},
+ D_train,
+ evals=[(D_train, "Train"), (D_valid, "Valid")],
+ num_boost_round=rounds,
+ evals_result=evals_result,
+ verbose_eval=True,
+ )
+ assert len(evals_result["Train"]["error"]) == rounds
+ assert len(evals_result["Valid"]["error"]) == rounds
self.run_evaluation_monitor(D_train, D_valid, rounds, True)
self.run_evaluation_monitor(D_train, D_valid, rounds, 2)
@@ -93,72 +97,83 @@ class TestCallbacks:
evals_result = {}
rounds = 30
early_stopping_rounds = 5
- booster = xgb.train({'objective': 'binary:logistic',
- 'eval_metric': 'error'}, D_train,
- evals=[(D_train, 'Train'), (D_valid, 'Valid')],
- num_boost_round=rounds,
- evals_result=evals_result,
- verbose_eval=True,
- early_stopping_rounds=early_stopping_rounds)
- dump = booster.get_dump(dump_format='json')
+ booster = xgb.train(
+ {"objective": "binary:logistic", "eval_metric": "error"},
+ D_train,
+ evals=[(D_train, "Train"), (D_valid, "Valid")],
+ num_boost_round=rounds,
+ evals_result=evals_result,
+ verbose_eval=True,
+ early_stopping_rounds=early_stopping_rounds,
+ )
+ dump = booster.get_dump(dump_format="json")
assert len(dump) - booster.best_iteration == early_stopping_rounds + 1
def test_early_stopping_custom_eval(self):
D_train = xgb.DMatrix(self.X_train, self.y_train)
D_valid = xgb.DMatrix(self.X_valid, self.y_valid)
early_stopping_rounds = 5
- booster = xgb.train({'objective': 'binary:logistic',
- 'eval_metric': 'error',
- 'tree_method': 'hist'}, D_train,
- evals=[(D_train, 'Train'), (D_valid, 'Valid')],
- feval=tm.eval_error_metric,
- num_boost_round=1000,
- early_stopping_rounds=early_stopping_rounds,
- verbose_eval=False)
- dump = booster.get_dump(dump_format='json')
+ booster = xgb.train(
+ {
+ "objective": "binary:logistic",
+ "eval_metric": "error",
+ "tree_method": "hist",
+ },
+ D_train,
+ evals=[(D_train, "Train"), (D_valid, "Valid")],
+ feval=tm.eval_error_metric,
+ num_boost_round=1000,
+ early_stopping_rounds=early_stopping_rounds,
+ verbose_eval=False,
+ )
+ dump = booster.get_dump(dump_format="json")
assert len(dump) - booster.best_iteration == early_stopping_rounds + 1
def test_early_stopping_customize(self):
D_train = xgb.DMatrix(self.X_train, self.y_train)
D_valid = xgb.DMatrix(self.X_valid, self.y_valid)
early_stopping_rounds = 5
- early_stop = xgb.callback.EarlyStopping(rounds=early_stopping_rounds,
- metric_name='CustomErr',
- data_name='Train')
+ early_stop = xgb.callback.EarlyStopping(
+ rounds=early_stopping_rounds, metric_name="CustomErr", data_name="Train"
+ )
# Specify which dataset and which metric should be used for early stopping.
booster = xgb.train(
- {'objective': 'binary:logistic',
- 'eval_metric': ['error', 'rmse'],
- 'tree_method': 'hist'}, D_train,
- evals=[(D_train, 'Train'), (D_valid, 'Valid')],
+ {
+ "objective": "binary:logistic",
+ "eval_metric": ["error", "rmse"],
+ "tree_method": "hist",
+ },
+ D_train,
+ evals=[(D_train, "Train"), (D_valid, "Valid")],
feval=tm.eval_error_metric,
num_boost_round=1000,
callbacks=[early_stop],
- verbose_eval=False)
- dump = booster.get_dump(dump_format='json')
+ verbose_eval=False,
+ )
+ dump = booster.get_dump(dump_format="json")
assert len(dump) - booster.best_iteration == early_stopping_rounds + 1
- assert len(early_stop.stopping_history['Train']['CustomErr']) == len(dump)
+ assert len(early_stop.stopping_history["Train"]["CustomErr"]) == len(dump)
rounds = 100
early_stop = xgb.callback.EarlyStopping(
rounds=early_stopping_rounds,
- metric_name='CustomErr',
- data_name='Train',
+ metric_name="CustomErr",
+ data_name="Train",
min_delta=100,
save_best=True,
)
booster = xgb.train(
{
- 'objective': 'binary:logistic',
- 'eval_metric': ['error', 'rmse'],
- 'tree_method': 'hist'
+ "objective": "binary:logistic",
+ "eval_metric": ["error", "rmse"],
+ "tree_method": "hist",
},
D_train,
- evals=[(D_train, 'Train'), (D_valid, 'Valid')],
+ evals=[(D_train, "Train"), (D_valid, "Valid")],
feval=tm.eval_error_metric,
num_boost_round=rounds,
callbacks=[early_stop],
- verbose_eval=False
+ verbose_eval=False,
)
# No iteration can be made with min_delta == 100
assert booster.best_iteration == 0
@@ -166,18 +181,20 @@ class TestCallbacks:
def test_early_stopping_skl(self):
from sklearn.datasets import load_breast_cancer
+
X, y = load_breast_cancer(return_X_y=True)
early_stopping_rounds = 5
cls = xgb.XGBClassifier(
- early_stopping_rounds=early_stopping_rounds, eval_metric='error'
+ early_stopping_rounds=early_stopping_rounds, eval_metric="error"
)
cls.fit(X, y, eval_set=[(X, y)])
booster = cls.get_booster()
- dump = booster.get_dump(dump_format='json')
+ dump = booster.get_dump(dump_format="json")
assert len(dump) - booster.best_iteration == early_stopping_rounds + 1
def test_early_stopping_custom_eval_skl(self):
from sklearn.datasets import load_breast_cancer
+
X, y = load_breast_cancer(return_X_y=True)
early_stopping_rounds = 5
early_stop = xgb.callback.EarlyStopping(rounds=early_stopping_rounds)
@@ -186,11 +203,12 @@ class TestCallbacks:
)
cls.fit(X, y, eval_set=[(X, y)])
booster = cls.get_booster()
- dump = booster.get_dump(dump_format='json')
+ dump = booster.get_dump(dump_format="json")
assert len(dump) - booster.best_iteration == early_stopping_rounds + 1
def test_early_stopping_save_best_model(self):
from sklearn.datasets import load_breast_cancer
+
X, y = load_breast_cancer(return_X_y=True)
n_estimators = 100
early_stopping_rounds = 5
@@ -200,11 +218,11 @@ class TestCallbacks:
cls = xgb.XGBClassifier(
n_estimators=n_estimators,
eval_metric=tm.eval_error_metric_skl,
- callbacks=[early_stop]
+ callbacks=[early_stop],
)
cls.fit(X, y, eval_set=[(X, y)])
booster = cls.get_booster()
- dump = booster.get_dump(dump_format='json')
+ dump = booster.get_dump(dump_format="json")
assert len(dump) == booster.best_iteration + 1
early_stop = xgb.callback.EarlyStopping(
@@ -220,8 +238,9 @@ class TestCallbacks:
cls.fit(X, y, eval_set=[(X, y)])
# No error
- early_stop = xgb.callback.EarlyStopping(rounds=early_stopping_rounds,
- save_best=False)
+ early_stop = xgb.callback.EarlyStopping(
+ rounds=early_stopping_rounds, save_best=False
+ )
xgb.XGBClassifier(
booster="gblinear",
n_estimators=10,
@@ -231,14 +250,17 @@ class TestCallbacks:
def test_early_stopping_continuation(self):
from sklearn.datasets import load_breast_cancer
+
X, y = load_breast_cancer(return_X_y=True)
- cls = xgb.XGBClassifier(eval_metric=tm.eval_error_metric_skl)
+
early_stopping_rounds = 5
early_stop = xgb.callback.EarlyStopping(
rounds=early_stopping_rounds, save_best=True
)
- with pytest.warns(UserWarning):
- cls.fit(X, y, eval_set=[(X, y)], callbacks=[early_stop])
+ cls = xgb.XGBClassifier(
+ eval_metric=tm.eval_error_metric_skl, callbacks=[early_stop]
+ )
+ cls.fit(X, y, eval_set=[(X, y)])
booster = cls.get_booster()
assert booster.num_boosted_rounds() == booster.best_iteration + 1
@@ -256,21 +278,10 @@ class TestCallbacks:
)
cls.fit(X, y, eval_set=[(X, y)])
booster = cls.get_booster()
- assert booster.num_boosted_rounds() == \
- booster.best_iteration + early_stopping_rounds + 1
-
- def test_deprecated(self):
- from sklearn.datasets import load_breast_cancer
- X, y = load_breast_cancer(return_X_y=True)
- early_stopping_rounds = 5
- early_stop = xgb.callback.EarlyStopping(
- rounds=early_stopping_rounds, save_best=True
- )
- clf = xgb.XGBClassifier(
- eval_metric=tm.eval_error_metric_skl, callbacks=[early_stop]
- )
- with pytest.raises(ValueError, match=r".*set_params.*"):
- clf.fit(X, y, eval_set=[(X, y)], callbacks=[early_stop])
+ assert (
+ booster.num_boosted_rounds()
+ == booster.best_iteration + early_stopping_rounds + 1
+ )
def run_eta_decay(self, tree_method):
"""Test learning rate scheduler, used by both CPU and GPU tests."""
@@ -343,7 +354,7 @@ class TestCallbacks:
callbacks=[scheduler([0, 0, 0, 0])],
evals_result=evals_result,
)
- eval_errors_2 = list(map(float, evals_result['eval']['error']))
+ eval_errors_2 = list(map(float, evals_result["eval"]["error"]))
assert isinstance(bst, xgb.core.Booster)
# validation error should not decrease, if eta/learning_rate = 0
assert eval_errors_2[0] == eval_errors_2[-1]
@@ -361,7 +372,7 @@ class TestCallbacks:
callbacks=[scheduler(eta_decay)],
evals_result=evals_result,
)
- eval_errors_3 = list(map(float, evals_result['eval']['error']))
+ eval_errors_3 = list(map(float, evals_result["eval"]["error"]))
assert isinstance(bst, xgb.core.Booster)
diff --git a/tests/python/test_early_stopping.py b/tests/python/test_early_stopping.py
index 7695c6861..a275a8077 100644
--- a/tests/python/test_early_stopping.py
+++ b/tests/python/test_early_stopping.py
@@ -15,23 +15,23 @@ class TestEarlyStopping:
from sklearn.model_selection import train_test_split
digits = load_digits(n_class=2)
- X = digits['data']
- y = digits['target']
+ X = digits["data"]
+ y = digits["target"]
X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0)
- clf1 = xgb.XGBClassifier(learning_rate=0.1)
- clf1.fit(X_train, y_train, early_stopping_rounds=5, eval_metric="auc",
- eval_set=[(X_test, y_test)])
- clf2 = xgb.XGBClassifier(learning_rate=0.1)
- clf2.fit(X_train, y_train, early_stopping_rounds=4, eval_metric="auc",
- eval_set=[(X_test, y_test)])
+ clf1 = xgb.XGBClassifier(
+ learning_rate=0.1, early_stopping_rounds=5, eval_metric="auc"
+ )
+ clf1.fit(X_train, y_train, eval_set=[(X_test, y_test)])
+ clf2 = xgb.XGBClassifier(
+ learning_rate=0.1, early_stopping_rounds=4, eval_metric="auc"
+ )
+ clf2.fit(X_train, y_train, eval_set=[(X_test, y_test)])
# should be the same
assert clf1.best_score == clf2.best_score
assert clf1.best_score != 1
# check overfit
clf3 = xgb.XGBClassifier(
- learning_rate=0.1,
- eval_metric="auc",
- early_stopping_rounds=10
+ learning_rate=0.1, eval_metric="auc", early_stopping_rounds=10
)
clf3.fit(X_train, y_train, eval_set=[(X_test, y_test)])
base_score = get_basescore(clf3)
@@ -39,9 +39,9 @@ class TestEarlyStopping:
clf3 = xgb.XGBClassifier(
learning_rate=0.1,
- base_score=.5,
+ base_score=0.5,
eval_metric="auc",
- early_stopping_rounds=10
+ early_stopping_rounds=10,
)
clf3.fit(X_train, y_train, eval_set=[(X_test, y_test)])
diff --git a/tests/python/test_eval_metrics.py b/tests/python/test_eval_metrics.py
index 92726014b..cbb3dc88d 100644
--- a/tests/python/test_eval_metrics.py
+++ b/tests/python/test_eval_metrics.py
@@ -9,37 +9,41 @@ rng = np.random.RandomState(1337)
class TestEvalMetrics:
- xgb_params_01 = {'nthread': 1, 'eval_metric': 'error'}
+ xgb_params_01 = {"nthread": 1, "eval_metric": "error"}
- xgb_params_02 = {'nthread': 1, 'eval_metric': ['error']}
+ xgb_params_02 = {"nthread": 1, "eval_metric": ["error"]}
- xgb_params_03 = {'nthread': 1, 'eval_metric': ['rmse', 'error']}
+ xgb_params_03 = {"nthread": 1, "eval_metric": ["rmse", "error"]}
- xgb_params_04 = {'nthread': 1, 'eval_metric': ['error', 'rmse']}
+ xgb_params_04 = {"nthread": 1, "eval_metric": ["error", "rmse"]}
def evalerror_01(self, preds, dtrain):
labels = dtrain.get_label()
- return 'error', float(sum(labels != (preds > 0.0))) / len(labels)
+ return "error", float(sum(labels != (preds > 0.0))) / len(labels)
def evalerror_02(self, preds, dtrain):
labels = dtrain.get_label()
- return [('error', float(sum(labels != (preds > 0.0))) / len(labels))]
+ return [("error", float(sum(labels != (preds > 0.0))) / len(labels))]
@pytest.mark.skipif(**tm.no_sklearn())
def evalerror_03(self, preds, dtrain):
from sklearn.metrics import mean_squared_error
labels = dtrain.get_label()
- return [('rmse', mean_squared_error(labels, preds)),
- ('error', float(sum(labels != (preds > 0.0))) / len(labels))]
+ return [
+ ("rmse", mean_squared_error(labels, preds)),
+ ("error", float(sum(labels != (preds > 0.0))) / len(labels)),
+ ]
@pytest.mark.skipif(**tm.no_sklearn())
def evalerror_04(self, preds, dtrain):
from sklearn.metrics import mean_squared_error
labels = dtrain.get_label()
- return [('error', float(sum(labels != (preds > 0.0))) / len(labels)),
- ('rmse', mean_squared_error(labels, preds))]
+ return [
+ ("error", float(sum(labels != (preds > 0.0))) / len(labels)),
+ ("rmse", mean_squared_error(labels, preds)),
+ ]
@pytest.mark.skipif(**tm.no_sklearn())
def test_eval_metrics(self):
@@ -50,15 +54,15 @@ class TestEvalMetrics:
from sklearn.datasets import load_digits
digits = load_digits(n_class=2)
- X = digits['data']
- y = digits['target']
+ X = digits["data"]
+ y = digits["target"]
Xt, Xv, yt, yv = train_test_split(X, y, test_size=0.2, random_state=0)
dtrain = xgb.DMatrix(Xt, label=yt)
dvalid = xgb.DMatrix(Xv, label=yv)
- watchlist = [(dtrain, 'train'), (dvalid, 'val')]
+ watchlist = [(dtrain, "train"), (dvalid, "val")]
gbdt_01 = xgb.train(self.xgb_params_01, dtrain, num_boost_round=10)
gbdt_02 = xgb.train(self.xgb_params_02, dtrain, num_boost_round=10)
@@ -66,26 +70,54 @@ class TestEvalMetrics:
assert gbdt_01.predict(dvalid)[0] == gbdt_02.predict(dvalid)[0]
assert gbdt_01.predict(dvalid)[0] == gbdt_03.predict(dvalid)[0]
- gbdt_01 = xgb.train(self.xgb_params_01, dtrain, 10, watchlist,
- early_stopping_rounds=2)
- gbdt_02 = xgb.train(self.xgb_params_02, dtrain, 10, watchlist,
- early_stopping_rounds=2)
- gbdt_03 = xgb.train(self.xgb_params_03, dtrain, 10, watchlist,
- early_stopping_rounds=2)
- gbdt_04 = xgb.train(self.xgb_params_04, dtrain, 10, watchlist,
- early_stopping_rounds=2)
+ gbdt_01 = xgb.train(
+ self.xgb_params_01, dtrain, 10, watchlist, early_stopping_rounds=2
+ )
+ gbdt_02 = xgb.train(
+ self.xgb_params_02, dtrain, 10, watchlist, early_stopping_rounds=2
+ )
+ gbdt_03 = xgb.train(
+ self.xgb_params_03, dtrain, 10, watchlist, early_stopping_rounds=2
+ )
+ gbdt_04 = xgb.train(
+ self.xgb_params_04, dtrain, 10, watchlist, early_stopping_rounds=2
+ )
assert gbdt_01.predict(dvalid)[0] == gbdt_02.predict(dvalid)[0]
assert gbdt_01.predict(dvalid)[0] == gbdt_03.predict(dvalid)[0]
assert gbdt_03.predict(dvalid)[0] != gbdt_04.predict(dvalid)[0]
- gbdt_01 = xgb.train(self.xgb_params_01, dtrain, 10, watchlist,
- early_stopping_rounds=2, feval=self.evalerror_01)
- gbdt_02 = xgb.train(self.xgb_params_02, dtrain, 10, watchlist,
- early_stopping_rounds=2, feval=self.evalerror_02)
- gbdt_03 = xgb.train(self.xgb_params_03, dtrain, 10, watchlist,
- early_stopping_rounds=2, feval=self.evalerror_03)
- gbdt_04 = xgb.train(self.xgb_params_04, dtrain, 10, watchlist,
- early_stopping_rounds=2, feval=self.evalerror_04)
+ gbdt_01 = xgb.train(
+ self.xgb_params_01,
+ dtrain,
+ 10,
+ watchlist,
+ early_stopping_rounds=2,
+ feval=self.evalerror_01,
+ )
+ gbdt_02 = xgb.train(
+ self.xgb_params_02,
+ dtrain,
+ 10,
+ watchlist,
+ early_stopping_rounds=2,
+ feval=self.evalerror_02,
+ )
+ gbdt_03 = xgb.train(
+ self.xgb_params_03,
+ dtrain,
+ 10,
+ watchlist,
+ early_stopping_rounds=2,
+ feval=self.evalerror_03,
+ )
+ gbdt_04 = xgb.train(
+ self.xgb_params_04,
+ dtrain,
+ 10,
+ watchlist,
+ early_stopping_rounds=2,
+ feval=self.evalerror_04,
+ )
assert gbdt_01.predict(dvalid)[0] == gbdt_02.predict(dvalid)[0]
assert gbdt_01.predict(dvalid)[0] == gbdt_03.predict(dvalid)[0]
assert gbdt_03.predict(dvalid)[0] != gbdt_04.predict(dvalid)[0]
@@ -93,6 +125,7 @@ class TestEvalMetrics:
@pytest.mark.skipif(**tm.no_sklearn())
def test_gamma_deviance(self):
from sklearn.metrics import mean_gamma_deviance
+
rng = np.random.RandomState(1994)
n_samples = 100
n_features = 30
@@ -101,8 +134,13 @@ class TestEvalMetrics:
y = rng.randn(n_samples)
y = y - y.min() * 100
- reg = xgb.XGBRegressor(tree_method="hist", objective="reg:gamma", n_estimators=10)
- reg.fit(X, y, eval_metric="gamma-deviance")
+ reg = xgb.XGBRegressor(
+ tree_method="hist",
+ objective="reg:gamma",
+ n_estimators=10,
+ eval_metric="gamma-deviance",
+ )
+ reg.fit(X, y)
booster = reg.get_booster()
score = reg.predict(X)
@@ -113,16 +151,26 @@ class TestEvalMetrics:
@pytest.mark.skipif(**tm.no_sklearn())
def test_gamma_lik(self) -> None:
import scipy.stats as stats
+
rng = np.random.default_rng(1994)
n_samples = 32
n_features = 10
- X = rng.normal(0, 1, size=n_samples * n_features).reshape((n_samples, n_features))
+ X = rng.normal(0, 1, size=n_samples * n_features).reshape(
+ (n_samples, n_features)
+ )
alpha, loc, beta = 5.0, 11.1, 22
- y = stats.gamma.rvs(alpha, loc=loc, scale=beta, size=n_samples, random_state=rng)
- reg = xgb.XGBRegressor(tree_method="hist", objective="reg:gamma", n_estimators=64)
- reg.fit(X, y, eval_metric="gamma-nloglik", eval_set=[(X, y)])
+ y = stats.gamma.rvs(
+ alpha, loc=loc, scale=beta, size=n_samples, random_state=rng
+ )
+ reg = xgb.XGBRegressor(
+ tree_method="hist",
+ objective="reg:gamma",
+ n_estimators=64,
+ eval_metric="gamma-nloglik",
+ )
+ reg.fit(X, y, eval_set=[(X, y)])
score = reg.predict(X)
@@ -134,7 +182,7 @@ class TestEvalMetrics:
# XGBoost uses the canonical link function of gamma in evaluation function.
# so \theta = - (1.0 / y)
# dispersion is hardcoded as 1.0, so shape (a in scipy parameter) is also 1.0
- beta = - (1.0 / (- (1.0 / y))) # == y
+ beta = -(1.0 / (-(1.0 / y))) # == y
nloglik_stats = -stats.gamma.logpdf(score, a=1.0, scale=beta)
np.testing.assert_allclose(nloglik, np.mean(nloglik_stats), rtol=1e-3)
@@ -153,7 +201,7 @@ class TestEvalMetrics:
n_features,
n_informative=n_features,
n_redundant=0,
- random_state=rng
+ random_state=rng,
)
Xy = xgb.DMatrix(X, y)
booster = xgb.train(
@@ -197,7 +245,7 @@ class TestEvalMetrics:
n_informative=n_features,
n_redundant=0,
n_classes=n_classes,
- random_state=rng
+ random_state=rng,
)
if weighted:
weights = rng.randn(n_samples)
@@ -242,20 +290,25 @@ class TestEvalMetrics:
def run_pr_auc_binary(self, tree_method):
from sklearn.datasets import make_classification
from sklearn.metrics import auc, precision_recall_curve
+
X, y = make_classification(128, 4, n_classes=2, random_state=1994)
- clf = xgb.XGBClassifier(tree_method=tree_method, n_estimators=1)
- clf.fit(X, y, eval_metric="aucpr", eval_set=[(X, y)])
+ clf = xgb.XGBClassifier(
+ tree_method=tree_method, n_estimators=1, eval_metric="aucpr"
+ )
+ clf.fit(X, y, eval_set=[(X, y)])
evals_result = clf.evals_result()["validation_0"]["aucpr"][-1]
y_score = clf.predict_proba(X)[:, 1] # get the positive column
precision, recall, _ = precision_recall_curve(y, y_score)
prauc = auc(recall, precision)
- # Interpolation results are slightly different from sklearn, but overall should be
- # similar.
+ # Interpolation results are slightly different from sklearn, but overall should
+ # be similar.
np.testing.assert_allclose(prauc, evals_result, rtol=1e-2)
- clf = xgb.XGBClassifier(tree_method=tree_method, n_estimators=10)
- clf.fit(X, y, eval_metric="aucpr", eval_set=[(X, y)])
+ clf = xgb.XGBClassifier(
+ tree_method=tree_method, n_estimators=10, eval_metric="aucpr"
+ )
+ clf.fit(X, y, eval_set=[(X, y)])
evals_result = clf.evals_result()["validation_0"]["aucpr"][-1]
np.testing.assert_allclose(0.99, evals_result, rtol=1e-2)
@@ -264,16 +317,21 @@ class TestEvalMetrics:
def run_pr_auc_multi(self, tree_method):
from sklearn.datasets import make_classification
+
X, y = make_classification(
64, 16, n_informative=8, n_classes=3, random_state=1994
)
- clf = xgb.XGBClassifier(tree_method=tree_method, n_estimators=1)
- clf.fit(X, y, eval_metric="aucpr", eval_set=[(X, y)])
+ clf = xgb.XGBClassifier(
+ tree_method=tree_method, n_estimators=1, eval_metric="aucpr"
+ )
+ clf.fit(X, y, eval_set=[(X, y)])
evals_result = clf.evals_result()["validation_0"]["aucpr"][-1]
- # No available implementation for comparison, just check that XGBoost converges to
- # 1.0
- clf = xgb.XGBClassifier(tree_method=tree_method, n_estimators=10)
- clf.fit(X, y, eval_metric="aucpr", eval_set=[(X, y)])
+ # No available implementation for comparison, just check that XGBoost converges
+ # to 1.0
+ clf = xgb.XGBClassifier(
+ tree_method=tree_method, n_estimators=10, eval_metric="aucpr"
+ )
+ clf.fit(X, y, eval_set=[(X, y)])
evals_result = clf.evals_result()["validation_0"]["aucpr"][-1]
np.testing.assert_allclose(1.0, evals_result, rtol=1e-2)
@@ -282,9 +340,13 @@ class TestEvalMetrics:
def run_pr_auc_ltr(self, tree_method):
from sklearn.datasets import make_classification
+
X, y = make_classification(128, 4, n_classes=2, random_state=1994)
ltr = xgb.XGBRanker(
- tree_method=tree_method, n_estimators=16, objective="rank:pairwise"
+ tree_method=tree_method,
+ n_estimators=16,
+ objective="rank:pairwise",
+ eval_metric="aucpr",
)
groups = np.array([32, 32, 64])
ltr.fit(
@@ -293,7 +355,6 @@ class TestEvalMetrics:
group=groups,
eval_set=[(X, y)],
eval_group=[groups],
- eval_metric="aucpr",
)
results = ltr.evals_result()["validation_0"]["aucpr"]
assert results[-1] >= 0.99
diff --git a/tests/python/test_training_continuation.py b/tests/python/test_training_continuation.py
index 6b2f96301..9e9f37ea5 100644
--- a/tests/python/test_training_continuation.py
+++ b/tests/python/test_training_continuation.py
@@ -6,6 +6,7 @@ import pytest
import xgboost as xgb
from xgboost import testing as tm
+from xgboost.testing.continuation import run_training_continuation_model_output
rng = np.random.RandomState(1337)
@@ -15,54 +16,51 @@ class TestTrainingContinuation:
def generate_parameters(self):
xgb_params_01_binary = {
- 'nthread': 1,
+ "nthread": 1,
}
xgb_params_02_binary = {
- 'nthread': 1,
- 'num_parallel_tree': self.num_parallel_tree
+ "nthread": 1,
+ "num_parallel_tree": self.num_parallel_tree,
}
xgb_params_03_binary = {
- 'nthread': 1,
- 'num_class': 5,
- 'num_parallel_tree': self.num_parallel_tree
+ "nthread": 1,
+ "num_class": 5,
+ "num_parallel_tree": self.num_parallel_tree,
}
- return [
- xgb_params_01_binary, xgb_params_02_binary, xgb_params_03_binary
- ]
+ return [xgb_params_01_binary, xgb_params_02_binary, xgb_params_03_binary]
- def run_training_continuation(self, xgb_params_01, xgb_params_02,
- xgb_params_03):
+ def run_training_continuation(self, xgb_params_01, xgb_params_02, xgb_params_03):
from sklearn.datasets import load_digits
from sklearn.metrics import mean_squared_error
digits_2class = load_digits(n_class=2)
digits_5class = load_digits(n_class=5)
- X_2class = digits_2class['data']
- y_2class = digits_2class['target']
+ X_2class = digits_2class["data"]
+ y_2class = digits_2class["target"]
- X_5class = digits_5class['data']
- y_5class = digits_5class['target']
+ X_5class = digits_5class["data"]
+ y_5class = digits_5class["target"]
dtrain_2class = xgb.DMatrix(X_2class, label=y_2class)
dtrain_5class = xgb.DMatrix(X_5class, label=y_5class)
- gbdt_01 = xgb.train(xgb_params_01, dtrain_2class,
- num_boost_round=10)
+ gbdt_01 = xgb.train(xgb_params_01, dtrain_2class, num_boost_round=10)
ntrees_01 = len(gbdt_01.get_dump())
assert ntrees_01 == 10
- gbdt_02 = xgb.train(xgb_params_01, dtrain_2class,
- num_boost_round=0)
- gbdt_02.save_model('xgb_tc.json')
+ gbdt_02 = xgb.train(xgb_params_01, dtrain_2class, num_boost_round=0)
+ gbdt_02.save_model("xgb_tc.json")
- gbdt_02a = xgb.train(xgb_params_01, dtrain_2class,
- num_boost_round=10, xgb_model=gbdt_02)
- gbdt_02b = xgb.train(xgb_params_01, dtrain_2class,
- num_boost_round=10, xgb_model="xgb_tc.json")
+ gbdt_02a = xgb.train(
+ xgb_params_01, dtrain_2class, num_boost_round=10, xgb_model=gbdt_02
+ )
+ gbdt_02b = xgb.train(
+ xgb_params_01, dtrain_2class, num_boost_round=10, xgb_model="xgb_tc.json"
+ )
ntrees_02a = len(gbdt_02a.get_dump())
ntrees_02b = len(gbdt_02b.get_dump())
assert ntrees_02a == 10
@@ -76,20 +74,21 @@ class TestTrainingContinuation:
res2 = mean_squared_error(y_2class, gbdt_02b.predict(dtrain_2class))
assert res1 == res2
- gbdt_03 = xgb.train(xgb_params_01, dtrain_2class,
- num_boost_round=3)
- gbdt_03.save_model('xgb_tc.json')
+ gbdt_03 = xgb.train(xgb_params_01, dtrain_2class, num_boost_round=3)
+ gbdt_03.save_model("xgb_tc.json")
- gbdt_03a = xgb.train(xgb_params_01, dtrain_2class,
- num_boost_round=7, xgb_model=gbdt_03)
- gbdt_03b = xgb.train(xgb_params_01, dtrain_2class,
- num_boost_round=7, xgb_model="xgb_tc.json")
+ gbdt_03a = xgb.train(
+ xgb_params_01, dtrain_2class, num_boost_round=7, xgb_model=gbdt_03
+ )
+ gbdt_03b = xgb.train(
+ xgb_params_01, dtrain_2class, num_boost_round=7, xgb_model="xgb_tc.json"
+ )
ntrees_03a = len(gbdt_03a.get_dump())
ntrees_03b = len(gbdt_03b.get_dump())
assert ntrees_03a == 10
assert ntrees_03b == 10
- os.remove('xgb_tc.json')
+ os.remove("xgb_tc.json")
res1 = mean_squared_error(y_2class, gbdt_03a.predict(dtrain_2class))
res2 = mean_squared_error(y_2class, gbdt_03b.predict(dtrain_2class))
@@ -113,16 +112,14 @@ class TestTrainingContinuation:
y_2class,
gbdt_04.predict(
dtrain_2class, iteration_range=(0, gbdt_04.num_boosted_rounds())
- )
+ ),
)
assert res1 == res2
- gbdt_05 = xgb.train(xgb_params_03, dtrain_5class,
- num_boost_round=7)
- gbdt_05 = xgb.train(xgb_params_03,
- dtrain_5class,
- num_boost_round=3,
- xgb_model=gbdt_05)
+ gbdt_05 = xgb.train(xgb_params_03, dtrain_5class, num_boost_round=7)
+ gbdt_05 = xgb.train(
+ xgb_params_03, dtrain_5class, num_boost_round=3, xgb_model=gbdt_05
+ )
res1 = gbdt_05.predict(dtrain_5class)
res2 = gbdt_05.predict(
@@ -149,8 +146,8 @@ class TestTrainingContinuation:
from sklearn.datasets import load_breast_cancer
X, y = load_breast_cancer(return_X_y=True)
- clf = xgb.XGBClassifier(n_estimators=2)
- clf.fit(X, y, eval_set=[(X, y)], eval_metric="logloss")
+ clf = xgb.XGBClassifier(n_estimators=2, eval_metric="logloss")
+ clf.fit(X, y, eval_set=[(X, y)])
assert tm.non_increasing(clf.evals_result()["validation_0"]["logloss"])
with tempfile.TemporaryDirectory() as tmpdir:
@@ -160,5 +157,10 @@ class TestTrainingContinuation:
clf = xgb.XGBClassifier(n_estimators=2)
# change metric to error
- clf.fit(X, y, eval_set=[(X, y)], eval_metric="error")
+ clf.set_params(eval_metric="error")
+ clf.fit(X, y, eval_set=[(X, y)], xgb_model=loaded)
assert tm.non_increasing(clf.evals_result()["validation_0"]["error"])
+
+ @pytest.mark.parametrize("tree_method", ["hist", "approx", "exact"])
+ def test_model_output(self, tree_method: str) -> None:
+ run_training_continuation_model_output("cpu", tree_method)
diff --git a/tests/python/test_with_sklearn.py b/tests/python/test_with_sklearn.py
index 47f1778d6..9047cee6e 100644
--- a/tests/python/test_with_sklearn.py
+++ b/tests/python/test_with_sklearn.py
@@ -30,8 +30,8 @@ def test_binary_classification():
kf = KFold(n_splits=2, shuffle=True, random_state=rng)
for cls in (xgb.XGBClassifier, xgb.XGBRFClassifier):
for train_index, test_index in kf.split(X, y):
- clf = cls(random_state=42)
- xgb_model = clf.fit(X[train_index], y[train_index], eval_metric=['auc', 'logloss'])
+ clf = cls(random_state=42, eval_metric=['auc', 'logloss'])
+ xgb_model = clf.fit(X[train_index], y[train_index])
preds = xgb_model.predict(X[test_index])
labels = y[test_index]
err = sum(1 for i in range(len(preds))
@@ -101,10 +101,11 @@ def test_best_iteration():
def train(booster: str, forest: Optional[int]) -> None:
rounds = 4
cls = xgb.XGBClassifier(
- n_estimators=rounds, num_parallel_tree=forest, booster=booster
- ).fit(
- X, y, eval_set=[(X, y)], early_stopping_rounds=3
- )
+ n_estimators=rounds,
+ num_parallel_tree=forest,
+ booster=booster,
+ early_stopping_rounds=3,
+ ).fit(X, y, eval_set=[(X, y)])
assert cls.best_iteration == rounds - 1
# best_iteration is used by default, assert that under gblinear it's
@@ -112,9 +113,9 @@ def test_best_iteration():
cls.predict(X)
num_parallel_tree = 4
- train('gbtree', num_parallel_tree)
- train('dart', num_parallel_tree)
- train('gblinear', None)
+ train("gbtree", num_parallel_tree)
+ train("dart", num_parallel_tree)
+ train("gblinear", None)
def test_ranking():
@@ -258,6 +259,7 @@ def test_stacking_classification():
X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=42)
clf.fit(X_train, y_train).score(X_test, y_test)
+
@pytest.mark.skipif(**tm.no_pandas())
def test_feature_importances_weight():
from sklearn.datasets import load_digits
@@ -474,7 +476,8 @@ def run_housing_rf_regression(tree_method):
rfreg = xgb.XGBRFRegressor()
with pytest.raises(NotImplementedError):
- rfreg.fit(X, y, early_stopping_rounds=10)
+ rfreg.set_params(early_stopping_rounds=10)
+ rfreg.fit(X, y)
def test_rf_regression():
@@ -574,7 +577,7 @@ def test_classification_with_custom_objective():
return logregobj(y, p)
cls.set_params(objective=wrapped)
- cls.predict(X) # no throw
+ cls.predict(X) # no throw
cls.fit(X, y)
assert is_called[0]
@@ -844,51 +847,65 @@ def run_validation_weights(model):
y_train, y_test = y[:1600], y[1600:]
# instantiate model
- param_dist = {'objective': 'binary:logistic', 'n_estimators': 2,
- 'random_state': 123}
+ param_dist = {
+ "objective": "binary:logistic",
+ "n_estimators": 2,
+ "random_state": 123,
+ }
clf = model(**param_dist)
# train it using instance weights only in the training set
weights_train = np.random.choice([1, 2], len(X_train))
- clf.fit(X_train, y_train,
- sample_weight=weights_train,
- eval_set=[(X_test, y_test)],
- eval_metric='logloss',
- verbose=False)
-
+ clf.set_params(eval_metric="logloss")
+ clf.fit(
+ X_train,
+ y_train,
+ sample_weight=weights_train,
+ eval_set=[(X_test, y_test)],
+ verbose=False,
+ )
# evaluate logloss metric on test set *without* using weights
evals_result_without_weights = clf.evals_result()
- logloss_without_weights = evals_result_without_weights[
- "validation_0"]["logloss"]
+ logloss_without_weights = evals_result_without_weights["validation_0"]["logloss"]
# now use weights for the test set
np.random.seed(0)
weights_test = np.random.choice([1, 2], len(X_test))
- clf.fit(X_train, y_train,
- sample_weight=weights_train,
- eval_set=[(X_test, y_test)],
- sample_weight_eval_set=[weights_test],
- eval_metric='logloss',
- verbose=False)
+ clf.set_params(eval_metric="logloss")
+ clf.fit(
+ X_train,
+ y_train,
+ sample_weight=weights_train,
+ eval_set=[(X_test, y_test)],
+ sample_weight_eval_set=[weights_test],
+ verbose=False,
+ )
evals_result_with_weights = clf.evals_result()
logloss_with_weights = evals_result_with_weights["validation_0"]["logloss"]
# check that the logloss in the test set is actually different when using
# weights than when not using them
- assert all((logloss_with_weights[i] != logloss_without_weights[i]
- for i in [0, 1]))
+ assert all((logloss_with_weights[i] != logloss_without_weights[i] for i in [0, 1]))
with pytest.raises(ValueError):
# length of eval set and sample weight doesn't match.
- clf.fit(X_train, y_train, sample_weight=weights_train,
- eval_set=[(X_train, y_train), (X_test, y_test)],
- sample_weight_eval_set=[weights_train])
+ clf.fit(
+ X_train,
+ y_train,
+ sample_weight=weights_train,
+ eval_set=[(X_train, y_train), (X_test, y_test)],
+ sample_weight_eval_set=[weights_train],
+ )
with pytest.raises(ValueError):
cls = xgb.XGBClassifier()
- cls.fit(X_train, y_train, sample_weight=weights_train,
- eval_set=[(X_train, y_train), (X_test, y_test)],
- sample_weight_eval_set=[weights_train])
+ cls.fit(
+ X_train,
+ y_train,
+ sample_weight=weights_train,
+ eval_set=[(X_train, y_train), (X_test, y_test)],
+ sample_weight_eval_set=[weights_train],
+ )
def test_validation_weights():
@@ -960,8 +977,7 @@ def test_XGBClassifier_resume():
# file name of stored xgb model
model1.save_model(model1_path)
- model2 = xgb.XGBClassifier(
- learning_rate=0.3, random_state=0, n_estimators=8)
+ model2 = xgb.XGBClassifier(learning_rate=0.3, random_state=0, n_estimators=8)
model2.fit(X, Y, xgb_model=model1_path)
pred2 = model2.predict(X)
@@ -972,8 +988,7 @@ def test_XGBClassifier_resume():
# file name of 'Booster' instance Xgb model
model1.get_booster().save_model(model1_booster_path)
- model2 = xgb.XGBClassifier(
- learning_rate=0.3, random_state=0, n_estimators=8)
+ model2 = xgb.XGBClassifier(learning_rate=0.3, random_state=0, n_estimators=8)
model2.fit(X, Y, xgb_model=model1_booster_path)
pred2 = model2.predict(X)
@@ -1279,12 +1294,16 @@ def test_estimator_reg(estimator, check):
):
estimator.fit(X, y)
return
- if os.environ["PYTEST_CURRENT_TEST"].find("check_estimators_overwrite_params") != -1:
+ if (
+ os.environ["PYTEST_CURRENT_TEST"].find("check_estimators_overwrite_params")
+ != -1
+ ):
# A hack to pass the scikit-learn parameter mutation tests. XGBoost regressor
- # returns actual internal default values for parameters in `get_params`, but those
- # are set as `None` in sklearn interface to avoid duplication. So we fit a dummy
- # model and obtain the default parameters here for the mutation tests.
+ # returns actual internal default values for parameters in `get_params`, but
+ # those are set as `None` in sklearn interface to avoid duplication. So we fit
+ # a dummy model and obtain the default parameters here for the mutation tests.
from sklearn.datasets import make_regression
+
X, y = make_regression(n_samples=2, n_features=1)
estimator.set_params(**xgb.XGBRegressor().fit(X, y).get_params())
@@ -1325,6 +1344,7 @@ def test_categorical():
def test_evaluation_metric():
from sklearn.datasets import load_diabetes, load_digits
from sklearn.metrics import mean_absolute_error
+
X, y = load_diabetes(return_X_y=True)
n_estimators = 16
@@ -1341,17 +1361,6 @@ def test_evaluation_metric():
for line in lines:
assert line.find("mean_absolute_error") != -1
- def metric(predt: np.ndarray, Xy: xgb.DMatrix):
- y = Xy.get_label()
- return "m", np.abs(predt - y).sum()
-
- with pytest.warns(UserWarning):
- reg = xgb.XGBRegressor(
- tree_method="hist",
- n_estimators=1,
- )
- reg.fit(X, y, eval_set=[(X, y)], eval_metric=metric)
-
def merror(y_true: np.ndarray, predt: np.ndarray):
n_samples = y_true.shape[0]
assert n_samples == predt.size
diff --git a/tests/test_distributed/test_gpu_with_dask/test_gpu_with_dask.py b/tests/test_distributed/test_gpu_with_dask/test_gpu_with_dask.py
index 1bc6bbccd..1b58b2657 100644
--- a/tests/test_distributed/test_gpu_with_dask/test_gpu_with_dask.py
+++ b/tests/test_distributed/test_gpu_with_dask/test_gpu_with_dask.py
@@ -363,12 +363,12 @@ class TestDistributedGPU:
device="cuda",
eval_metric="error",
n_estimators=100,
+ early_stopping_rounds=early_stopping_rounds,
)
cls.client = local_cuda_client
cls.fit(
X,
y,
- early_stopping_rounds=early_stopping_rounds,
eval_set=[(valid_X, valid_y)],
)
booster = cls.get_booster()
diff --git a/tests/test_distributed/test_with_dask/test_with_dask.py b/tests/test_distributed/test_with_dask/test_with_dask.py
index 79df025fe..150e698d3 100644
--- a/tests/test_distributed/test_with_dask/test_with_dask.py
+++ b/tests/test_distributed/test_with_dask/test_with_dask.py
@@ -937,8 +937,10 @@ def run_empty_dmatrix_auc(client: "Client", device: str, n_workers: int) -> None
valid_X = dd.from_array(valid_X_, chunksize=n_samples)
valid_y = dd.from_array(valid_y_, chunksize=n_samples)
- cls = xgb.dask.DaskXGBClassifier(device=device, n_estimators=2)
- cls.fit(X, y, eval_metric=["auc", "aucpr"], eval_set=[(valid_X, valid_y)])
+ cls = xgb.dask.DaskXGBClassifier(
+ device=device, n_estimators=2, eval_metric=["auc", "aucpr"]
+ )
+ cls.fit(X, y, eval_set=[(valid_X, valid_y)])
# multiclass
X_, y_ = make_classification(
@@ -966,8 +968,10 @@ def run_empty_dmatrix_auc(client: "Client", device: str, n_workers: int) -> None
valid_X = dd.from_array(valid_X_, chunksize=n_samples)
valid_y = dd.from_array(valid_y_, chunksize=n_samples)
- cls = xgb.dask.DaskXGBClassifier(device=device, n_estimators=2)
- cls.fit(X, y, eval_metric=["auc", "aucpr"], eval_set=[(valid_X, valid_y)])
+ cls = xgb.dask.DaskXGBClassifier(
+ device=device, n_estimators=2, eval_metric=["auc", "aucpr"]
+ )
+ cls.fit(X, y, eval_set=[(valid_X, valid_y)])
def test_empty_dmatrix_auc() -> None:
@@ -994,11 +998,11 @@ def run_auc(client: "Client", device: str) -> None:
valid_X = dd.from_array(valid_X_, chunksize=10)
valid_y = dd.from_array(valid_y_, chunksize=10)
- cls = xgb.XGBClassifier(device=device, n_estimators=2)
- cls.fit(X_, y_, eval_metric="auc", eval_set=[(valid_X_, valid_y_)])
+ cls = xgb.XGBClassifier(device=device, n_estimators=2, eval_metric="auc")
+ cls.fit(X_, y_, eval_set=[(valid_X_, valid_y_)])
- dcls = xgb.dask.DaskXGBClassifier(device=device, n_estimators=2)
- dcls.fit(X, y, eval_metric="auc", eval_set=[(valid_X, valid_y)])
+ dcls = xgb.dask.DaskXGBClassifier(device=device, n_estimators=2, eval_metric="auc")
+ dcls.fit(X, y, eval_set=[(valid_X, valid_y)])
approx = dcls.evals_result()["validation_0"]["auc"]
exact = cls.evals_result()["validation_0"]["auc"]
@@ -1267,16 +1271,16 @@ def test_dask_ranking(client: "Client") -> None:
qid_valid = qid_valid.astype(np.uint32)
qid_test = qid_test.astype(np.uint32)
- rank = xgb.dask.DaskXGBRanker(n_estimators=2500)
+ rank = xgb.dask.DaskXGBRanker(
+ n_estimators=2500, eval_metric=["ndcg"], early_stopping_rounds=10
+ )
rank.fit(
x_train,
y_train,
qid=qid_train,
eval_set=[(x_test, y_test), (x_train, y_train)],
eval_qid=[qid_test, qid_train],
- eval_metric=["ndcg"],
verbose=True,
- early_stopping_rounds=10,
)
assert rank.n_features_in_ == 46
assert rank.best_score > 0.98
@@ -2150,13 +2154,15 @@ class TestDaskCallbacks:
valid_X, valid_y = load_breast_cancer(return_X_y=True)
valid_X, valid_y = da.from_array(valid_X), da.from_array(valid_y)
cls = xgb.dask.DaskXGBClassifier(
- objective="binary:logistic", tree_method="hist", n_estimators=1000
+ objective="binary:logistic",
+ tree_method="hist",
+ n_estimators=1000,
+ early_stopping_rounds=early_stopping_rounds,
)
cls.client = client
cls.fit(
X,
y,
- early_stopping_rounds=early_stopping_rounds,
eval_set=[(valid_X, valid_y)],
)
booster = cls.get_booster()
@@ -2165,15 +2171,17 @@ class TestDaskCallbacks:
# Specify the metric
cls = xgb.dask.DaskXGBClassifier(
- objective="binary:logistic", tree_method="hist", n_estimators=1000
+ objective="binary:logistic",
+ tree_method="hist",
+ n_estimators=1000,
+ early_stopping_rounds=early_stopping_rounds,
+ eval_metric="error",
)
cls.client = client
cls.fit(
X,
y,
- early_stopping_rounds=early_stopping_rounds,
eval_set=[(valid_X, valid_y)],
- eval_metric="error",
)
assert tm.non_increasing(cls.evals_result()["validation_0"]["error"])
booster = cls.get_booster()
@@ -2215,12 +2223,12 @@ class TestDaskCallbacks:
tree_method="hist",
n_estimators=1000,
eval_metric=tm.eval_error_metric_skl,
+ early_stopping_rounds=early_stopping_rounds,
)
cls.client = client
cls.fit(
X,
y,
- early_stopping_rounds=early_stopping_rounds,
eval_set=[(valid_X, valid_y)],
)
booster = cls.get_booster()
@@ -2234,21 +2242,22 @@ class TestDaskCallbacks:
X, y = load_breast_cancer(return_X_y=True)
X, y = da.from_array(X), da.from_array(y)
- cls = xgb.dask.DaskXGBClassifier(
- objective="binary:logistic", tree_method="hist", n_estimators=10
- )
- cls.client = client
-
with tempfile.TemporaryDirectory() as tmpdir:
- cls.fit(
- X,
- y,
+ cls = xgb.dask.DaskXGBClassifier(
+ objective="binary:logistic",
+ tree_method="hist",
+ n_estimators=10,
callbacks=[
xgb.callback.TrainingCheckPoint(
directory=Path(tmpdir), interval=1, name="model"
)
],
)
+ cls.client = client
+ cls.fit(
+ X,
+ y,
+ )
for i in range(1, 10):
assert os.path.exists(
os.path.join(
diff --git a/tests/test_distributed/test_with_spark/test_spark_local.py b/tests/test_distributed/test_with_spark/test_spark_local.py
index 406174542..b8c16ef1c 100644
--- a/tests/test_distributed/test_with_spark/test_spark_local.py
+++ b/tests/test_distributed/test_with_spark/test_spark_local.py
@@ -311,24 +311,20 @@ def clf_with_weight(
y_val = np.array([0, 1])
w_train = np.array([1.0, 2.0])
w_val = np.array([1.0, 2.0])
- cls2 = XGBClassifier()
+ cls2 = XGBClassifier(eval_metric="logloss", early_stopping_rounds=1)
cls2.fit(
X_train,
y_train,
eval_set=[(X_val, y_val)],
- early_stopping_rounds=1,
- eval_metric="logloss",
)
- cls3 = XGBClassifier()
+ cls3 = XGBClassifier(eval_metric="logloss", early_stopping_rounds=1)
cls3.fit(
X_train,
y_train,
sample_weight=w_train,
eval_set=[(X_val, y_val)],
sample_weight_eval_set=[w_val],
- early_stopping_rounds=1,
- eval_metric="logloss",
)
cls_df_train_with_eval_weight = spark.createDataFrame(