[R] Use new predict function. (#6819)
* Call new C prediction API. * Add `strict_shape`. * Add `iterationrange`. * Update document.
This commit is contained in:
Jiaming Yuan
GitHub
parent
25514e104a
commit
b56614e9b8
@ -263,10 +263,7 @@ cb.reset.parameters <- function(new_params) {
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#' \itemize{
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#' \item \code{best_score} the evaluation score at the best iteration
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#' \item \code{best_iteration} at which boosting iteration the best score has occurred (1-based index)
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#' \item \code{best_ntreelimit} to use with the \code{ntreelimit} parameter in \code{predict}.
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#' It differs from \code{best_iteration} in multiclass or random forest settings.
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#' }
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#'
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#' The Same values are also stored as xgb-attributes:
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#' \itemize{
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#' \item \code{best_iteration} is stored as a 0-based iteration index (for interoperability of binary models)
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@ -498,13 +495,12 @@ cb.cv.predict <- function(save_models = FALSE) {
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rep(NA_real_, N)
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}
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ntreelimit <- NVL(env$basket$best_ntreelimit,
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env$end_iteration * env$num_parallel_tree)
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iterationrange <- c(1, NVL(env$basket$best_iteration, env$end_iteration) + 1)
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if (NVL(env$params[['booster']], '') == 'gblinear') {
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ntreelimit <- 0 # must be 0 for gblinear
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iterationrange <- c(1, 1) # must be 0 for gblinear
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}
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for (fd in env$bst_folds) {
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pr <- predict(fd$bst, fd$watchlist[[2]], ntreelimit = ntreelimit, reshape = TRUE)
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pr <- predict(fd$bst, fd$watchlist[[2]], iterationrange = iterationrange, reshape = TRUE)
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if (is.matrix(pred)) {
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pred[fd$index, ] <- pr
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} else {
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@ -178,7 +178,8 @@ xgb.iter.eval <- function(booster_handle, watchlist, iter, feval = NULL) {
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} else {
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res <- sapply(seq_along(watchlist), function(j) {
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w <- watchlist[[j]]
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preds <- predict(booster_handle, w, outputmargin = TRUE, ntreelimit = 0) # predict using all trees
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## predict using all trees
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preds <- predict(booster_handle, w, outputmargin = TRUE, iterationrange = c(1, 1))
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eval_res <- feval(preds, w)
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out <- eval_res$value
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names(out) <- paste0(evnames[j], "-", eval_res$metric)
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@ -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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@ -101,9 +101,7 @@
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#' parameter or randomly generated.
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#' \item \code{best_iteration} iteration number with the best evaluation metric value
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#' (only available with early stopping).
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#' \item \code{best_ntreelimit} the \code{ntreelimit} value corresponding to the best iteration,
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#' which could further be used in \code{predict} method
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#' (only available with early stopping).
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#' \item \code{best_ntreelimit} and the \code{ntreelimit} Deprecated attributes, use \code{best_iteration} instead.
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#' \item \code{pred} CV prediction values available when \code{prediction} is set.
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#' It is either vector or matrix (see \code{\link{cb.cv.predict}}).
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#' \item \code{models} a list of the CV folds' models. It is only available with the explicit
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@ -171,9 +171,6 @@
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#' explicitly passed.
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#' \item \code{best_iteration} iteration number with the best evaluation metric value
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#' (only available with early stopping).
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#' \item \code{best_ntreelimit} the \code{ntreelimit} value corresponding to the best iteration,
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#' which could further be used in \code{predict} method
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#' (only available with early stopping).
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#' \item \code{best_score} the best evaluation metric value during early stopping.
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#' (only available with early stopping).
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#' \item \code{feature_names} names of the training dataset features
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@ -38,10 +38,7 @@ The following additional fields are assigned to the model's R object:
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\itemize{
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\item \code{best_score} the evaluation score at the best iteration
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\item \code{best_iteration} at which boosting iteration the best score has occurred (1-based index)
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\item \code{best_ntreelimit} to use with the \code{ntreelimit} parameter in \code{predict}.
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It differs from \code{best_iteration} in multiclass or random forest settings.
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}
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The Same values are also stored as xgb-attributes:
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\itemize{
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\item \code{best_iteration} is stored as a 0-based iteration index (for interoperability of binary models)
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@ -17,6 +17,8 @@
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predinteraction = FALSE,
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reshape = FALSE,
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training = FALSE,
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iterationrange = NULL,
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strict_shape = FALSE,
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...
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)
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@ -34,8 +36,7 @@ missing values in data (e.g., sometimes 0 or some other extreme value is used).}
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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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\item{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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\item{ntreelimit}{Deprecated, use \code{iterationrange} instead.}
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\item{predleaf}{whether predict leaf index.}
|
||||
|
||||
@ -52,10 +53,20 @@ or predinteraction flags is TRUE.}
|
||||
\item{training}{whether is the prediction result used for training. For dart booster,
|
||||
training predicting will perform dropout.}
|
||||
|
||||
\item{iterationrange}{Specifies which layer of trees are used in prediction. For
|
||||
example, if a random forest is trained with 100 rounds. Specifying
|
||||
`iteration_range=(1, 21)`, then only the forests built during [1, 21) (half open set)
|
||||
rounds are used in this prediction. It's 1-based index just like R vector. When set
|
||||
to \code{c(1, 1)} XGBoost will use all trees.}
|
||||
|
||||
\item{strict_shape}{Default is \code{FALSE}. When it's set to \code{TRUE}, output
|
||||
type and shape of prediction are invariant to model type.}
|
||||
|
||||
\item{...}{Parameters passed to \code{predict.xgb.Booster}}
|
||||
}
|
||||
\value{
|
||||
For regression or binary classification, it returns a vector of length \code{nrows(newdata)}.
|
||||
The return type is different depending whether \code{strict_shape} is set to \code{TRUE}. By default,
|
||||
for regression or binary classification, it returns a vector of length \code{nrows(newdata)}.
|
||||
For multiclass classification, either a \code{num_class * nrows(newdata)} vector or
|
||||
a \code{(nrows(newdata), num_class)} dimension matrix is returned, depending on
|
||||
the \code{reshape} value.
|
||||
@ -76,18 +87,19 @@ two dimensions. The "+ 1" columns corresponds to bias. Summing this array along
|
||||
produce practically the same result as predict with \code{predcontrib = TRUE}.
|
||||
For a multiclass case, a list of \code{num_class} elements is returned, where each element is
|
||||
such an array.
|
||||
|
||||
When \code{strict_shape} is set to \code{TRUE}, the output is always an array. For
|
||||
normal prediction, the output is a 2-dimension array \code{(num_class, nrow(newdata))}.
|
||||
|
||||
For \code{predcontrib = TRUE}, output is \code{(ncol(newdata) + 1, num_class, nrow(newdata))}
|
||||
For \code{predinteraction = TRUE}, output is \code{(ncol(newdata) + 1, ncol(newdata) + 1, num_class, nrow(newdata))}
|
||||
For \code{predleaf = TRUE}, output is \code{(n_trees_in_forest, num_class, n_iterations, nrow(newdata))}
|
||||
}
|
||||
\description{
|
||||
Predicted values based on either xgboost model or model handle object.
|
||||
}
|
||||
\details{
|
||||
Note that \code{ntreelimit} is not necessarily equal to the number of boosting iterations
|
||||
and it is not necessarily equal to the number of trees in a model.
|
||||
E.g., in a random forest-like model, \code{ntreelimit} would limit the number of trees.
|
||||
But for multiclass classification, while there are multiple trees per iteration,
|
||||
\code{ntreelimit} limits the number of boosting iterations.
|
||||
|
||||
Also note that \code{ntreelimit} would currently do nothing for predictions from gblinear,
|
||||
Note that \code{iterationrange} would currently do nothing for predictions from gblinear,
|
||||
since gblinear doesn't keep its boosting history.
|
||||
|
||||
One possible practical applications of the \code{predleaf} option is to use the model
|
||||
@ -120,7 +132,7 @@ bst <- xgboost(data = train$data, label = train$label, max_depth = 2,
|
||||
# use all trees by default
|
||||
pred <- predict(bst, test$data)
|
||||
# use only the 1st tree
|
||||
pred1 <- predict(bst, test$data, ntreelimit = 1)
|
||||
pred1 <- predict(bst, test$data, iterationrange = c(1, 2))
|
||||
|
||||
# Predicting tree leafs:
|
||||
# the result is an nsamples X ntrees matrix
|
||||
@ -172,25 +184,9 @@ 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]), ntreelimit=5)
|
||||
pred5 <- predict(bst, as.matrix(iris[, -5]), iterationrange=c(1, 6))
|
||||
sum(pred5 != lb)/length(lb)
|
||||
|
||||
|
||||
## random forest-like model of 25 trees for binary classification:
|
||||
|
||||
set.seed(11)
|
||||
bst <- xgboost(data = train$data, label = train$label, max_depth = 5,
|
||||
nthread = 2, nrounds = 1, objective = "binary:logistic",
|
||||
num_parallel_tree = 25, subsample = 0.6, colsample_bytree = 0.1)
|
||||
# Inspect the prediction error vs number of trees:
|
||||
lb <- test$label
|
||||
dtest <- xgb.DMatrix(test$data, label=lb)
|
||||
err <- sapply(1:25, function(n) {
|
||||
pred <- predict(bst, dtest, ntreelimit=n)
|
||||
sum((pred > 0.5) != lb)/length(lb)
|
||||
})
|
||||
plot(err, type='l', ylim=c(0,0.1), xlab='#trees')
|
||||
|
||||
}
|
||||
\references{
|
||||
Scott M. Lundberg, Su-In Lee, "A Unified Approach to Interpreting Model Predictions", NIPS Proceedings 2017, \url{https://arxiv.org/abs/1705.07874}
|
||||
|
||||
@ -135,9 +135,7 @@ An object of class \code{xgb.cv.synchronous} with the following elements:
|
||||
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} the \code{ntreelimit} value corresponding to the best iteration,
|
||||
which could further be used in \code{predict} method
|
||||
(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
|
||||
|
||||
@ -187,9 +187,6 @@ An object of class \code{xgb.Booster} with the following elements:
|
||||
explicitly passed.
|
||||
\item \code{best_iteration} iteration number with the best evaluation metric value
|
||||
(only available with early stopping).
|
||||
\item \code{best_ntreelimit} the \code{ntreelimit} value corresponding to the best iteration,
|
||||
which could further be used in \code{predict} method
|
||||
(only available with early stopping).
|
||||
\item \code{best_score} the best evaluation metric value during early stopping.
|
||||
(only available with early stopping).
|
||||
\item \code{feature_names} names of the training dataset features
|
||||
|
||||
@ -30,6 +30,7 @@ extern SEXP XGBoosterSerializeToBuffer_R(SEXP handle);
|
||||
extern SEXP XGBoosterUnserializeFromBuffer_R(SEXP handle, SEXP raw);
|
||||
extern SEXP XGBoosterModelToRaw_R(SEXP);
|
||||
extern SEXP XGBoosterPredict_R(SEXP, SEXP, SEXP, SEXP, SEXP);
|
||||
extern SEXP XGBoosterPredictFromDMatrix_R(SEXP, SEXP, SEXP);
|
||||
extern SEXP XGBoosterSaveModel_R(SEXP, SEXP);
|
||||
extern SEXP XGBoosterSetAttr_R(SEXP, SEXP, SEXP);
|
||||
extern SEXP XGBoosterSetParam_R(SEXP, SEXP, SEXP);
|
||||
@ -63,6 +64,7 @@ static const R_CallMethodDef CallEntries[] = {
|
||||
{"XGBoosterUnserializeFromBuffer_R", (DL_FUNC) &XGBoosterUnserializeFromBuffer_R, 2},
|
||||
{"XGBoosterModelToRaw_R", (DL_FUNC) &XGBoosterModelToRaw_R, 1},
|
||||
{"XGBoosterPredict_R", (DL_FUNC) &XGBoosterPredict_R, 5},
|
||||
{"XGBoosterPredictFromDMatrix_R", (DL_FUNC) &XGBoosterPredictFromDMatrix_R, 3},
|
||||
{"XGBoosterSaveModel_R", (DL_FUNC) &XGBoosterSaveModel_R, 2},
|
||||
{"XGBoosterSetAttr_R", (DL_FUNC) &XGBoosterSetAttr_R, 3},
|
||||
{"XGBoosterSetParam_R", (DL_FUNC) &XGBoosterSetParam_R, 3},
|
||||
|
||||
@ -374,6 +374,45 @@ SEXP XGBoosterPredict_R(SEXP handle, SEXP dmat, SEXP option_mask,
|
||||
return ret;
|
||||
}
|
||||
|
||||
SEXP XGBoosterPredictFromDMatrix_R(SEXP handle, SEXP dmat, SEXP json_config) {
|
||||
SEXP r_out_shape;
|
||||
SEXP r_out_result;
|
||||
SEXP r_out;
|
||||
|
||||
R_API_BEGIN();
|
||||
char const *c_json_config = CHAR(asChar(json_config));
|
||||
|
||||
bst_ulong out_dim;
|
||||
bst_ulong const *out_shape;
|
||||
float const *out_result;
|
||||
CHECK_CALL(XGBoosterPredictFromDMatrix(R_ExternalPtrAddr(handle),
|
||||
R_ExternalPtrAddr(dmat), c_json_config,
|
||||
&out_shape, &out_dim, &out_result));
|
||||
|
||||
r_out_shape = PROTECT(allocVector(INTSXP, out_dim));
|
||||
size_t len = 1;
|
||||
for (size_t i = 0; i < out_dim; ++i) {
|
||||
INTEGER(r_out_shape)[i] = out_shape[i];
|
||||
len *= out_shape[i];
|
||||
}
|
||||
r_out_result = PROTECT(allocVector(REALSXP, len));
|
||||
|
||||
#pragma omp parallel for
|
||||
for (omp_ulong i = 0; i < len; ++i) {
|
||||
REAL(r_out_result)[i] = out_result[i];
|
||||
}
|
||||
|
||||
r_out = PROTECT(allocVector(VECSXP, 2));
|
||||
|
||||
SET_VECTOR_ELT(r_out, 0, r_out_shape);
|
||||
SET_VECTOR_ELT(r_out, 1, r_out_result);
|
||||
|
||||
R_API_END();
|
||||
UNPROTECT(3);
|
||||
|
||||
return r_out;
|
||||
}
|
||||
|
||||
SEXP XGBoosterLoadModel_R(SEXP handle, SEXP fname) {
|
||||
R_API_BEGIN();
|
||||
CHECK_CALL(XGBoosterLoadModel(R_ExternalPtrAddr(handle), CHAR(asChar(fname))));
|
||||
|
||||
@ -164,7 +164,7 @@ XGB_DLL SEXP XGBoosterBoostOneIter_R(SEXP handle, SEXP dtrain, SEXP grad, SEXP h
|
||||
XGB_DLL SEXP XGBoosterEvalOneIter_R(SEXP handle, SEXP iter, SEXP dmats, SEXP evnames);
|
||||
|
||||
/*!
|
||||
* \brief make prediction based on dmat
|
||||
* \brief (Deprecated) make prediction based on dmat
|
||||
* \param handle handle
|
||||
* \param dmat data matrix
|
||||
* \param option_mask output_margin:1 predict_leaf:2
|
||||
@ -173,6 +173,16 @@ XGB_DLL SEXP XGBoosterEvalOneIter_R(SEXP handle, SEXP iter, SEXP dmats, SEXP evn
|
||||
*/
|
||||
XGB_DLL SEXP XGBoosterPredict_R(SEXP handle, SEXP dmat, SEXP option_mask,
|
||||
SEXP ntree_limit, SEXP training);
|
||||
|
||||
/*!
|
||||
* \brief Run prediction on DMatrix, replacing `XGBoosterPredict_R`
|
||||
* \param handle handle
|
||||
* \param dmat data matrix
|
||||
* \param json_config See `XGBoosterPredictFromDMatrix` in xgboost c_api.h
|
||||
*
|
||||
* \return A list containing 2 vectors, first one for shape while second one for prediction result.
|
||||
*/
|
||||
XGB_DLL SEXP XGBoosterPredictFromDMatrix_R(SEXP handle, SEXP dmat, SEXP json_config);
|
||||
/*!
|
||||
* \brief load model from existing file
|
||||
* \param handle handle
|
||||
|
||||
@ -34,6 +34,10 @@ test_that("train and predict binary classification", {
|
||||
err_pred1 <- sum((pred1 > 0.5) != train$label) / length(train$label)
|
||||
err_log <- 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", {
|
||||
@ -143,6 +147,9 @@ test_that("train and predict softprob", {
|
||||
pred_labels <- max.col(mpred) - 1
|
||||
err <- sum(pred_labels != lb) / length(lb)
|
||||
expect_equal(bst$evaluation_log[1, train_merror], err, tolerance = 5e-6)
|
||||
|
||||
mpred1 <- predict(bst, as.matrix(iris[, -5]), reshape = TRUE, iterationrange = c(1, 2))
|
||||
expect_equal(mpred, mpred1)
|
||||
})
|
||||
|
||||
test_that("train and predict softmax", {
|
||||
@ -182,10 +189,8 @@ test_that("train and predict RF", {
|
||||
pred_err_20 <- sum((pred > 0.5) != lb) / length(lb)
|
||||
expect_equal(pred_err_20, pred_err)
|
||||
|
||||
#pred <- predict(bst, train$data, ntreelimit = 1)
|
||||
#pred_err_1 <- sum((pred > 0.5) != lb)/length(lb)
|
||||
#expect_lt(pred_err, pred_err_1)
|
||||
#expect_lt(pred_err, 0.08)
|
||||
pred1 <- predict(bst, train$data, iterationrange = c(1, 2))
|
||||
expect_equal(pred, pred1)
|
||||
})
|
||||
|
||||
test_that("train and predict RF with softprob", {
|
||||
@ -385,3 +390,57 @@ test_that("Configuration works", {
|
||||
reloaded_config <- xgb.config(bst)
|
||||
expect_equal(config, reloaded_config);
|
||||
})
|
||||
|
||||
test_that("strict_shape works", {
|
||||
n_rounds <- 2
|
||||
|
||||
test_strict_shape <- function(bst, X, n_groups) {
|
||||
predt <- predict(bst, X, strict_shape = TRUE)
|
||||
margin <- predict(bst, X, outputmargin = TRUE, strict_shape = TRUE)
|
||||
contri <- predict(bst, X, predcontrib = TRUE, strict_shape = TRUE)
|
||||
interact <- predict(bst, X, predinteraction = TRUE, strict_shape = TRUE)
|
||||
leaf <- predict(bst, X, predleaf = TRUE, strict_shape = TRUE)
|
||||
|
||||
n_rows <- nrow(X)
|
||||
n_cols <- ncol(X)
|
||||
|
||||
expect_equal(dim(predt), c(n_groups, n_rows))
|
||||
expect_equal(dim(margin), c(n_groups, n_rows))
|
||||
expect_equal(dim(contri), c(n_cols + 1, n_groups, n_rows))
|
||||
expect_equal(dim(interact), c(n_cols + 1, n_cols + 1, n_groups, n_rows))
|
||||
expect_equal(dim(leaf), c(1, n_groups, n_rounds, n_rows))
|
||||
|
||||
if (n_groups != 1) {
|
||||
for (g in seq_len(n_groups)) {
|
||||
expect_lt(max(abs(colSums(contri[, g, ]) - margin[g, ])), 1e-5)
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
test_iris <- function() {
|
||||
y <- as.numeric(iris$Species) - 1
|
||||
X <- as.matrix(iris[, -5])
|
||||
|
||||
bst <- xgboost(data = X, label = y,
|
||||
max_depth = 2, nrounds = n_rounds,
|
||||
objective = "multi:softprob", num_class = 3, eval_metric = "merror")
|
||||
|
||||
test_strict_shape(bst, X, 3)
|
||||
}
|
||||
|
||||
|
||||
test_agaricus <- function() {
|
||||
data(agaricus.train, package = 'xgboost')
|
||||
X <- agaricus.train$data
|
||||
y <- agaricus.train$label
|
||||
|
||||
bst <- xgboost(data = X, label = y, max_depth = 2,
|
||||
nrounds = n_rounds, objective = "binary:logistic",
|
||||
eval_metric = 'error', eval_metric = 'auc', eval_metric = "logloss")
|
||||
|
||||
test_strict_shape(bst, X, 1)
|
||||
}
|
||||
|
||||
test_iris()
|
||||
test_agaricus()
|
||||
})
|
||||
|
||||
@ -6,7 +6,7 @@ Prediction
|
||||
|
||||
There are a number of prediction functions in XGBoost with various parameters. This
|
||||
document attempts to clarify some of confusions around prediction with a focus on the
|
||||
Python binding.
|
||||
Python binding, R package is similar when ``strict_shape`` is specified (see below).
|
||||
|
||||
******************
|
||||
Prediction Options
|
||||
@ -58,6 +58,13 @@ After 1.4 release, we added a new parameter called ``strict_shape``, one can set
|
||||
``apply`` method in scikit learn interface, this is set to False by default.
|
||||
|
||||
|
||||
For R package, when ``strict_shape`` is specified, an ``array`` is returned, with the same
|
||||
value as Python except R array is column-major while Python numpy array is row-major, so
|
||||
all the dimensions are reversed. For example, for a Python ``predict_leaf`` output
|
||||
obtained by having ``strict_shape=True`` has 4 dimensions: ``(n_samples, n_iterations,
|
||||
n_classes, n_trees_in_forest)``, while R with ``strict_shape=TRUE`` outputs
|
||||
``(n_trees_in_forest, n_classes, n_iterations, n_samples)``.
|
||||
|
||||
Other than these prediction types, there's also a parameter called ``iteration_range``,
|
||||
which is similar to model slicing. But instead of actually splitting up the model into
|
||||
multiple stacks, it simply returns the prediction formed by the trees within range.
|
||||
|
||||
@ -111,9 +111,8 @@ def _convert_ntree_limit(
|
||||
raise ValueError(
|
||||
"Only one of `iteration_range` and `ntree_limit` can be non zero."
|
||||
)
|
||||
num_parallel_tree, num_groups = _get_booster_layer_trees(booster)
|
||||
num_parallel_tree, _ = _get_booster_layer_trees(booster)
|
||||
num_parallel_tree = max([num_parallel_tree, 1])
|
||||
num_groups = max([num_groups, 1])
|
||||
iteration_range = (0, ntree_limit // num_parallel_tree)
|
||||
return iteration_range
|
||||
|
||||
|
||||
@ -662,9 +662,21 @@ XGB_DLL int XGBoosterPredictFromDMatrix(BoosterHandle handle,
|
||||
auto *learner = static_cast<Learner*>(handle);
|
||||
auto& entry = learner->GetThreadLocal().prediction_entry;
|
||||
auto p_m = *static_cast<std::shared_ptr<DMatrix> *>(dmat);
|
||||
auto type = PredictionType(get<Integer const>(config["type"]));
|
||||
auto iteration_begin = get<Integer const>(config["iteration_begin"]);
|
||||
auto iteration_end = get<Integer const>(config["iteration_end"]);
|
||||
|
||||
auto const& j_config = get<Object const>(config);
|
||||
auto type = PredictionType(get<Integer const>(j_config.at("type")));
|
||||
auto iteration_begin = get<Integer const>(j_config.at("iteration_begin"));
|
||||
auto iteration_end = get<Integer const>(j_config.at("iteration_end"));
|
||||
|
||||
auto ntree_limit_it = j_config.find("ntree_limit");
|
||||
if (ntree_limit_it != j_config.cend() && !IsA<Null>(ntree_limit_it->second) &&
|
||||
get<Integer const>(ntree_limit_it->second) != 0) {
|
||||
CHECK(iteration_end == 0) <<
|
||||
"Only one of the `ntree_limit` or `iteration_range` can be specified.";
|
||||
LOG(WARNING) << "`ntree_limit` is deprecated, use `iteration_range` instead.";
|
||||
iteration_end = GetIterationFromTreeLimit(get<Integer const>(ntree_limit_it->second), learner);
|
||||
}
|
||||
|
||||
bool approximate = type == PredictionType::kApproxContribution ||
|
||||
type == PredictionType::kApproxInteraction;
|
||||
bool contribs = type == PredictionType::kContribution ||
|
||||
|
||||
@ -48,7 +48,7 @@ inline void CalcPredictShape(bool strict_shape, PredictionType type, size_t rows
|
||||
*out_dim = 2;
|
||||
shape.resize(*out_dim);
|
||||
shape.front() = rows;
|
||||
shape.back() = groups;
|
||||
shape.back() = std::min(groups, chunksize);
|
||||
}
|
||||
break;
|
||||
}
|
||||
|
||||
@ -587,7 +587,6 @@ void QuantileHistMaker::Builder<GradientSumT>::InitSampling(const DMatrix& fmat,
|
||||
|
||||
#if XGBOOST_CUSTOMIZE_GLOBAL_PRNG
|
||||
std::bernoulli_distribution coin_flip(param_.subsample);
|
||||
size_t used = 0, unused = 0;
|
||||
for (size_t i = 0; i < info.num_row_; ++i) {
|
||||
if (!(gpair_ref[i].GetHess() >= 0.0f && coin_flip(rnd)) || gpair_ref[i].GetGrad() == 0.0f) {
|
||||
gpair_ref[i] = GradientPair(0);
|
||||
|
||||
Loading…
x
Reference in New Issue
Block a user