[R] Use new predict function. (#6819)
* Call new C prediction API. * Add `strict_shape`. * Add `iterationrange`. * Update document.
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Jiaming Yuan
GitHub
@@ -168,8 +168,7 @@ xgb.Booster.complete <- function(object, saveraw = TRUE) {
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#' @param outputmargin whether the prediction should be returned in the for of original untransformed
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#' sum of predictions from boosting iterations' results. E.g., setting \code{outputmargin=TRUE} for
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#' logistic regression would result in predictions for log-odds instead of probabilities.
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#' @param ntreelimit limit the number of model's trees or boosting iterations used in prediction (see Details).
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#' It will use all the trees by default (\code{NULL} value).
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#' @param ntreelimit Deprecated, use \code{iterationrange} instead.
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#' @param predleaf whether predict leaf index.
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#' @param predcontrib whether to return feature contributions to individual predictions (see Details).
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#' @param approxcontrib whether to use a fast approximation for feature contributions (see Details).
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@@ -179,16 +178,19 @@ xgb.Booster.complete <- function(object, saveraw = TRUE) {
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#' or predinteraction flags is TRUE.
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#' @param training whether is the prediction result used for training. For dart booster,
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#' training predicting will perform dropout.
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#' @param iterationrange Specifies which layer of trees are used in prediction. For
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#' example, if a random forest is trained with 100 rounds. Specifying
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#' `iteration_range=(1, 21)`, then only the forests built during [1, 21) (half open set)
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#' rounds are used in this prediction. It's 1-based index just like R vector. When set
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#' to \code{c(1, 1)} XGBoost will use all trees.
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#' @param strict_shape Default is \code{FALSE}. When it's set to \code{TRUE}, output
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#' type and shape of prediction are invariant to model type.
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#'
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#' @param ... Parameters passed to \code{predict.xgb.Booster}
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#'
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#' @details
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#' Note that \code{ntreelimit} is not necessarily equal to the number of boosting iterations
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#' and it is not necessarily equal to the number of trees in a model.
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#' E.g., in a random forest-like model, \code{ntreelimit} would limit the number of trees.
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#' But for multiclass classification, while there are multiple trees per iteration,
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#' \code{ntreelimit} limits the number of boosting iterations.
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#'
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#' Also note that \code{ntreelimit} would currently do nothing for predictions from gblinear,
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#' Note that \code{iterationrange} would currently do nothing for predictions from gblinear,
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#' since gblinear doesn't keep its boosting history.
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#'
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#' One possible practical applications of the \code{predleaf} option is to use the model
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@@ -209,7 +211,8 @@ xgb.Booster.complete <- function(object, saveraw = TRUE) {
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#' of the most important features first. See below about the format of the returned results.
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#'
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#' @return
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#' For regression or binary classification, it returns a vector of length \code{nrows(newdata)}.
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#' The return type is different depending whether \code{strict_shape} is set to \code{TRUE}. By default,
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#' for regression or binary classification, it returns a vector of length \code{nrows(newdata)}.
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#' For multiclass classification, either a \code{num_class * nrows(newdata)} vector or
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#' a \code{(nrows(newdata), num_class)} dimension matrix is returned, depending on
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#' the \code{reshape} value.
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@@ -231,6 +234,13 @@ xgb.Booster.complete <- function(object, saveraw = TRUE) {
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#' For a multiclass case, a list of \code{num_class} elements is returned, where each element is
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#' such an array.
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#'
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#' When \code{strict_shape} is set to \code{TRUE}, the output is always an array. For
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#' normal prediction, the output is a 2-dimension array \code{(num_class, nrow(newdata))}.
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#'
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#' For \code{predcontrib = TRUE}, output is \code{(ncol(newdata) + 1, num_class, nrow(newdata))}
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#' For \code{predinteraction = TRUE}, output is \code{(ncol(newdata) + 1, ncol(newdata) + 1, num_class, nrow(newdata))}
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#' For \code{predleaf = TRUE}, output is \code{(n_trees_in_forest, num_class, n_iterations, nrow(newdata))}
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#'
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#' @seealso
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#' \code{\link{xgb.train}}.
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#'
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@@ -253,7 +263,7 @@ xgb.Booster.complete <- function(object, saveraw = TRUE) {
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#' # use all trees by default
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#' pred <- predict(bst, test$data)
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#' # use only the 1st tree
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#' pred1 <- predict(bst, test$data, ntreelimit = 1)
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#' pred1 <- predict(bst, test$data, iterationrange = c(1, 2))
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#'
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#' # Predicting tree leafs:
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#' # the result is an nsamples X ntrees matrix
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@@ -305,31 +315,14 @@ xgb.Booster.complete <- function(object, saveraw = TRUE) {
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#' all.equal(pred, pred_labels)
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#' # prediction from using only 5 iterations should result
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#' # in the same error as seen in iteration 5:
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#' pred5 <- predict(bst, as.matrix(iris[, -5]), ntreelimit=5)
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#' pred5 <- predict(bst, as.matrix(iris[, -5]), iterationrange=c(1, 6))
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#' sum(pred5 != lb)/length(lb)
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#'
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#'
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#' ## random forest-like model of 25 trees for binary classification:
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#'
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#' set.seed(11)
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#' bst <- xgboost(data = train$data, label = train$label, max_depth = 5,
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#' nthread = 2, nrounds = 1, objective = "binary:logistic",
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#' num_parallel_tree = 25, subsample = 0.6, colsample_bytree = 0.1)
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#' # Inspect the prediction error vs number of trees:
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#' lb <- test$label
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#' dtest <- xgb.DMatrix(test$data, label=lb)
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#' err <- sapply(1:25, function(n) {
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#' pred <- predict(bst, dtest, ntreelimit=n)
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#' sum((pred > 0.5) != lb)/length(lb)
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#' })
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#' plot(err, type='l', ylim=c(0,0.1), xlab='#trees')
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#'
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#' @rdname predict.xgb.Booster
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#' @export
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predict.xgb.Booster <- function(object, newdata, missing = NA, outputmargin = FALSE, ntreelimit = NULL,
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predleaf = FALSE, predcontrib = FALSE, approxcontrib = FALSE, predinteraction = FALSE,
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reshape = FALSE, training = FALSE, ...) {
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reshape = FALSE, training = FALSE, iterationrange = NULL, strict_shape = FALSE, ...) {
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object <- xgb.Booster.complete(object, saveraw = FALSE)
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if (!inherits(newdata, "xgb.DMatrix"))
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newdata <- xgb.DMatrix(newdata, missing = missing)
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@@ -337,81 +330,114 @@ predict.xgb.Booster <- function(object, newdata, missing = NA, outputmargin = FA
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!is.null(colnames(newdata)) &&
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!identical(object[["feature_names"]], colnames(newdata)))
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stop("Feature names stored in `object` and `newdata` are different!")
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if (is.null(ntreelimit))
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ntreelimit <- NVL(object$best_ntreelimit, 0)
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if (NVL(object$params[['booster']], '') == 'gblinear')
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if (NVL(object$params[['booster']], '') == 'gblinear' || is.null(ntreelimit))
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ntreelimit <- 0
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if (ntreelimit < 0)
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stop("ntreelimit cannot be negative")
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option <- 0L + 1L * as.logical(outputmargin) + 2L * as.logical(predleaf) + 4L * as.logical(predcontrib) +
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8L * as.logical(approxcontrib) + 16L * as.logical(predinteraction)
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ret <- .Call(XGBoosterPredict_R, object$handle, newdata, option[1],
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as.integer(ntreelimit), as.integer(training))
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n_ret <- length(ret)
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n_row <- nrow(newdata)
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npred_per_case <- n_ret / n_row
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if (n_ret %% n_row != 0)
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stop("prediction length ", n_ret, " is not multiple of nrows(newdata) ", n_row)
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if (predleaf) {
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ret <- if (n_ret == n_row) {
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matrix(ret, ncol = 1)
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if (ntreelimit != 0 && is.null(iterationrange)) {
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## only ntreelimit, initialize iteration range
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iterationrange <- c(0, 0)
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} else if (ntreelimit == 0 && !is.null(iterationrange)) {
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## only iteration range, handle 1-based indexing
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iterationrange <- c(iterationrange[1] - 1, iterationrange[2] - 1)
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} else if (ntreelimit != 0 && !is.null(iterationrange)) {
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## both are specified, let libgxgboost throw an error
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} else {
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## no limit is supplied, use best
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if (is.null(object$best_iteration)) {
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iterationrange <- c(0, 0)
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} else {
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matrix(ret, nrow = n_row, byrow = TRUE)
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## We don't need to + 1 as R is 1-based index.
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iterationrange <- c(0, as.integer(object$best_iteration))
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}
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} else if (predcontrib) {
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n_col1 <- ncol(newdata) + 1
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n_group <- npred_per_case / n_col1
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cnames <- if (!is.null(colnames(newdata))) c(colnames(newdata), "BIAS") else NULL
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ret <- if (n_ret == n_row) {
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matrix(ret, ncol = 1, dimnames = list(NULL, cnames))
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} else if (n_group == 1) {
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matrix(ret, nrow = n_row, byrow = TRUE, dimnames = list(NULL, cnames))
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} else {
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arr <- aperm(
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a = array(
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data = ret,
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dim = c(n_col1, n_group, n_row),
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dimnames = list(cnames, NULL, NULL)
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),
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perm = c(2, 3, 1) # [group, row, col]
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)
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lapply(seq_len(n_group), function(g) arr[g, , ])
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}
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## Handle the 0 length values.
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box <- function(val) {
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if (length(val) == 0) {
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cval <- vector(, 1)
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cval[0] <- val
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return(cval)
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}
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return (val)
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}
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## We set strict_shape to TRUE then drop the dimensions conditionally
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args <- list(
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training = box(training),
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strict_shape = box(TRUE),
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iteration_begin = box(as.integer(iterationrange[1])),
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iteration_end = box(as.integer(iterationrange[2])),
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ntree_limit = box(as.integer(ntreelimit)),
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type = box(as.integer(0))
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)
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set_type <- function(type) {
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if (args$type != 0) {
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stop("One type of prediction at a time.")
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}
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return(box(as.integer(type)))
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}
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if (outputmargin) {
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args$type <- set_type(1)
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}
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if (predcontrib) {
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args$type <- set_type(if (approxcontrib) 3 else 2)
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}
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if (predinteraction) {
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args$type <- set_type(if (approxcontrib) 5 else 4)
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}
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if (predleaf) {
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args$type <- set_type(6)
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}
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predts <- .Call(
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XGBoosterPredictFromDMatrix_R, object$handle, newdata, jsonlite::toJSON(args, auto_unbox = TRUE)
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)
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names(predts) <- c("shape", "results")
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shape <- predts$shape
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ret <- predts$results
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n_row <- nrow(newdata)
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if (n_row != shape[1]) {
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stop("Incorrect predict shape.")
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}
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arr <- array(data = ret, dim = rev(shape))
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cnames <- if (!is.null(colnames(newdata))) c(colnames(newdata), "BIAS") else NULL
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if (predcontrib) {
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dimnames(arr) <- list(cnames, NULL, NULL)
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if (!strict_shape) {
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arr <- aperm(a = arr, perm = c(2, 3, 1)) # [group, row, col]
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}
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} else if (predinteraction) {
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n_col1 <- ncol(newdata) + 1
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n_group <- npred_per_case / n_col1^2
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cnames <- if (!is.null(colnames(newdata))) c(colnames(newdata), "BIAS") else NULL
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ret <- if (n_ret == n_row) {
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matrix(ret, ncol = 1, dimnames = list(NULL, cnames))
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} else if (n_group == 1) {
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aperm(
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a = array(
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data = ret,
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dim = c(n_col1, n_col1, n_row),
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dimnames = list(cnames, cnames, NULL)
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),
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perm = c(3, 1, 2)
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)
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} else {
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arr <- aperm(
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a = array(
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data = ret,
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dim = c(n_col1, n_col1, n_group, n_row),
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dimnames = list(cnames, cnames, NULL, NULL)
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),
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perm = c(3, 4, 1, 2) # [group, row, col1, col2]
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)
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lapply(seq_len(n_group), function(g) arr[g, , , ])
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dimnames(arr) <- list(cnames, cnames, NULL, NULL)
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if (!strict_shape) {
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arr <- aperm(a = arr, perm = c(3, 4, 1, 2)) # [group, row, col, col]
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}
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} else if (reshape && npred_per_case > 1) {
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ret <- matrix(ret, nrow = n_row, byrow = TRUE)
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}
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return(ret)
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if (!strict_shape) {
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n_groups <- shape[2]
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if (predleaf) {
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arr <- matrix(arr, nrow = n_row, byrow = TRUE)
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} else if (predcontrib && n_groups != 1) {
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arr <- lapply(seq_len(n_groups), function(g) arr[g, , ])
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} else if (predinteraction && n_groups != 1) {
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arr <- lapply(seq_len(n_groups), function(g) arr[g, , , ])
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} else if (!reshape && n_groups != 1) {
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arr <- ret
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} else if (reshape && n_groups != 1) {
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arr <- matrix(arr, ncol = 3, byrow = TRUE)
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}
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arr <- drop(arr)
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if (length(dim(arr)) == 1) {
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arr <- as.vector(arr)
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} else if (length(dim(arr)) == 2) {
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arr <- as.matrix(arr)
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}
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}
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return(arr)
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}
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#' @rdname predict.xgb.Booster
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