[R] remove 'reshape' argument, let shapes be handled by core cpp library (#10330)
This commit is contained in:
david-cortes
@@ -853,8 +853,7 @@ xgb.cb.cv.predict <- function(save_models = FALSE, outputmargin = FALSE) {
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pr <- predict(
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fd$bst,
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fd$evals[[2L]],
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outputmargin = env$outputmargin,
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reshape = TRUE
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outputmargin = env$outputmargin
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)
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if (is.null(pred)) {
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if (NCOL(pr) > 1L) {
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+2
-3
@@ -199,8 +199,7 @@ xgb.iter.update <- function(bst, dtrain, iter, obj) {
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bst,
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dtrain,
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outputmargin = TRUE,
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training = TRUE,
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reshape = TRUE
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training = TRUE
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)
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gpair <- obj(pred, dtrain)
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n_samples <- dim(dtrain)[1]
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@@ -246,7 +245,7 @@ xgb.iter.eval <- function(bst, evals, iter, feval) {
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res <- sapply(seq_along(evals), function(j) {
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w <- evals[[j]]
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## predict using all trees
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preds <- predict(bst, w, outputmargin = TRUE, reshape = TRUE, iterationrange = "all")
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preds <- predict(bst, w, outputmargin = TRUE, iterationrange = "all")
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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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+75
-104
@@ -112,9 +112,6 @@ xgb.get.handle <- function(object) {
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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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#' @param predinteraction Whether to return contributions of feature interactions to individual predictions (see Details).
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#' @param reshape Whether to reshape the vector of predictions to matrix form when there are several
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#' prediction outputs per case. No effect if `predleaf`, `predcontrib`,
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#' or `predinteraction` is `TRUE`.
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#' @param training Whether the prediction result is used for training. For dart booster,
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#' training predicting will perform dropout.
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#' @param iterationrange Sequence of rounds/iterations from the model to use for prediction, specified by passing
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@@ -128,8 +125,24 @@ xgb.get.handle <- function(object) {
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#' of the iterations (rounds) otherwise.
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#'
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#' If passing "all", will use all of the rounds regardless of whether the model had early stopping or not.
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#' @param strict_shape Default is `FALSE`. When set to `TRUE`, the output
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#' type and shape of predictions are invariant to the model type.
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#' @param strict_shape Whether to always return an array with the same dimensions for the given prediction mode
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#' regardless of the model type - meaning that, for example, both a multi-class and a binary classification
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#' model would generate output arrays with the same number of dimensions, with the 'class' dimension having
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#' size equal to '1' for the binary model.
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#'
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#' If passing `FALSE` (the default), dimensions will be simplified according to the model type, so that a
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#' binary classification model for example would not have a redundant dimension for 'class'.
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#'
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#' See documentation for the return type for the exact shape of the output arrays for each prediction mode.
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#' @param avoid_transpose Whether to output the resulting predictions in the same memory layout in which they
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#' are generated by the core XGBoost library, without transposing them to match the expected output shape.
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#'
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#' Internally, XGBoost uses row-major order for the predictions it generates, while R arrays use column-major
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#' order, hence the result needs to be transposed in order to have the expected shape when represented as
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#' an R array or matrix, which might be a slow operation.
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#'
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#' If passing `TRUE`, then the result will have dimensions in reverse order - for example, rows
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#' will be the last dimensions instead of the first dimension.
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#' @param base_margin Base margin used for boosting from existing model.
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#'
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#' Note that, if `newdata` is an `xgb.DMatrix` object, this argument will
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@@ -180,28 +193,46 @@ xgb.get.handle <- function(object) {
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#' Note that converting a matrix to [xgb.DMatrix()] uses multiple threads too.
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#'
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#' @return
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#' The return type depends on `strict_shape`. If `FALSE` (default):
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#' - For regression or binary classification: A vector of length `nrows(newdata)`.
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#' - For multiclass classification: A vector of length `num_class * nrows(newdata)` or
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#' a `(nrows(newdata), num_class)` matrix, depending on the `reshape` value.
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#' - When `predleaf = TRUE`: A matrix with one column per tree.
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#' - When `predcontrib = TRUE`: When not multiclass, a matrix with
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#' ` num_features + 1` columns. The last "+ 1" column corresponds to the baseline value.
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#' In the multiclass case, a list of `num_class` such matrices.
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#' The contribution values are on the scale of untransformed margin
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#' (e.g., for binary classification, the values are log-odds deviations from the baseline).
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#' - When `predinteraction = TRUE`: When not multiclass, the output is a 3d array of
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#' dimension `c(nrow, num_features + 1, num_features + 1)`. The off-diagonal (in the last two dimensions)
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#' elements represent different feature interaction contributions. The array is symmetric WRT the last
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#' two dimensions. The "+ 1" columns corresponds to the baselines. Summing this array along the last dimension should
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#' produce practically the same result as `predcontrib = TRUE`.
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#' In the multiclass case, a list of `num_class` such arrays.
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#' A numeric vector or array, with corresponding dimensions depending on the prediction mode and on
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#' parameter `strict_shape` as follows:
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#'
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#' When `strict_shape = TRUE`, the output is always an array:
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#' - For normal predictions, the output has dimension `(num_class, nrow(newdata))`.
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#' - For `predcontrib = TRUE`, the dimension is `(ncol(newdata) + 1, num_class, nrow(newdata))`.
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#' - For `predinteraction = TRUE`, the dimension is `(ncol(newdata) + 1, ncol(newdata) + 1, num_class, nrow(newdata))`.
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#' - For `predleaf = TRUE`, the dimension is `(n_trees_in_forest, num_class, n_iterations, nrow(newdata))`.
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#' If passing `strict_shape=FALSE`:\itemize{
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#' \item For regression or binary classification: a vector of length `nrows`.
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#' \item For multi-class and multi-target objectives: a matrix of dimensions `[nrows, ngroups]`.
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#'
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#' Note that objective variant `multi:softmax` defaults towards predicting most likely class (a vector
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#' `nrows`) instead of per-class probabilities.
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#' \item For `predleaf`: a matrix with one column per tree.
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#'
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#' For multi-class / multi-target, they will be arranged so that columns in the output will have
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#' the leafs from one group followed by leafs of the other group (e.g. order will be `group1:feat1`,
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#' `group1:feat2`, ..., `group2:feat1`, `group2:feat2`, ...).
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#' \item For `predcontrib`: when not multi-class / multi-target, a matrix with dimensions
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#' `[nrows, nfeats+1]`. The last "+ 1" column corresponds to the baseline value.
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#'
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#' For multi-class and multi-target objectives, will be an array with dimensions `[nrows, ngroups, nfeats+1]`.
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#'
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#' The contribution values are on the scale of untransformed margin (e.g., for binary classification,
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#' the values are log-odds deviations from the baseline).
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#' \item For `predinteraction`: when not multi-class / multi-target, the output is a 3D array of
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#' dimensions `[nrows, nfeats+1, nfeats+1]`. The off-diagonal (in the last two dimensions)
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#' elements represent different feature interaction contributions. The array is symmetric w.r.t. the last
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#' two dimensions. The "+ 1" columns corresponds to the baselines. Summing this array along the last
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#' dimension should produce practically the same result as `predcontrib = TRUE`.
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#'
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#' For multi-class and multi-target, will be a 4D array with dimensions `[nrows, ngroups, nfeats+1, nfeats+1]`
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#' }
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#'
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#' If passing `strict_shape=FALSE`, the result is always an array:\itemize{
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#' \item For normal predictions, the dimension is `[nrows, ngroups]`.
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#' \item For `predcontrib=TRUE`, the dimension is `[nrows, ngroups, nfeats+1]`.
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#' \item For `predinteraction=TRUE`, the dimension is `[nrows, ngroups, nfeats+1, nfeats+1]`.
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#' \item For `predleaf=TRUE`, the dimension is `[nrows, niter, ngroups, num_parallel_tree]`.
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#' }
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#'
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#' If passing `avoid_transpose=TRUE`, then the dimensions in all cases will be in reverse order - for
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#' example, for `predinteraction`, they will be `[nfeats+1, nfeats+1, ngroups, nrows]`
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#' instead of `[nrows, ngroups, nfeats+1, nfeats+1]`.
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#' @seealso [xgb.train()]
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#' @references
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#' 1. Scott M. Lundberg, Su-In Lee, "A Unified Approach to Interpreting Model Predictions",
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@@ -279,8 +310,6 @@ xgb.get.handle <- function(object) {
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#' # predict for softmax returns num_class probability numbers per case:
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#' pred <- predict(bst, as.matrix(iris[, -5]))
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#' str(pred)
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#' # reshape it to a num_class-columns matrix
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#' pred <- matrix(pred, ncol = num_class, byrow = TRUE)
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#' # convert the probabilities to softmax labels
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#' pred_labels <- max.col(pred) - 1
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#' # the following should result in the same error as seen in the last iteration
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@@ -311,8 +340,11 @@ xgb.get.handle <- function(object) {
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#' @export
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predict.xgb.Booster <- function(object, newdata, missing = NA, outputmargin = FALSE,
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predleaf = FALSE, predcontrib = FALSE, approxcontrib = FALSE, predinteraction = FALSE,
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reshape = FALSE, training = FALSE, iterationrange = NULL, strict_shape = FALSE,
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training = FALSE, iterationrange = NULL, strict_shape = FALSE, avoid_transpose = FALSE,
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validate_features = FALSE, base_margin = NULL, ...) {
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if (NROW(list(...))) {
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warning("Passed unused prediction arguments: ", paste(names(list(...)), collapse = ", "), ".")
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}
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if (validate_features) {
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newdata <- validate.features(object, newdata)
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}
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@@ -415,10 +447,9 @@ predict.xgb.Booster <- function(object, newdata, missing = NA, outputmargin = FA
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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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strict_shape = as.logical(strict_shape),
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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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type = box(as.integer(0))
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@@ -445,96 +476,36 @@ predict.xgb.Booster <- function(object, newdata, missing = NA, outputmargin = FA
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json_conf <- jsonlite::toJSON(args, auto_unbox = TRUE)
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if (is_dmatrix) {
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predts <- .Call(
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arr <- .Call(
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XGBoosterPredictFromDMatrix_R, xgb.get.handle(object), newdata, json_conf
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)
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} else if (use_as_dense_matrix) {
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predts <- .Call(
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arr <- .Call(
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XGBoosterPredictFromDense_R, xgb.get.handle(object), newdata, missing, json_conf, base_margin
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)
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} else if (use_as_csr_matrix) {
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predts <- .Call(
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arr <- .Call(
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XGBoosterPredictFromCSR_R, xgb.get.handle(object), csr_data, missing, json_conf, base_margin
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)
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} else if (use_as_df) {
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predts <- .Call(
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arr <- .Call(
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XGBoosterPredictFromColumnar_R, xgb.get.handle(object), newdata, missing, json_conf, base_margin
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)
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}
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names(predts) <- c("shape", "results")
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shape <- predts$shape
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arr <- predts$results
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n_ret <- length(arr)
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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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.Call(XGSetArrayDimInplace_R, arr, rev(shape))
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cnames <- if (!is.null(colnames(newdata))) c(colnames(newdata), "(Intercept)") else NULL
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n_groups <- shape[2]
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## Needed regardless of whether strict shape is being used.
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if (predcontrib) {
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.Call(XGSetArrayDimNamesInplace_R, arr, list(cnames, NULL, NULL))
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} else if (predinteraction) {
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.Call(XGSetArrayDimNamesInplace_R, arr, list(cnames, cnames, NULL, NULL))
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}
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if (strict_shape) {
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return(arr) # strict shape is calculated by libxgboost uniformly.
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if ((predcontrib || predinteraction) && !is.null(colnames(newdata))) {
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cnames <- c(colnames(newdata), "(Intercept)")
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dim_names <- vector(mode = "list", length = length(dim(arr)))
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dim_names[[1L]] <- cnames
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if (predinteraction) dim_names[[2L]] <- cnames
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.Call(XGSetArrayDimNamesInplace_R, arr, dim_names)
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}
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if (predleaf) {
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## Predict leaf
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if (n_ret == n_row) {
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.Call(XGSetArrayDimInplace_R, arr, c(n_row, 1L))
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} else {
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arr <- matrix(arr, nrow = n_row, byrow = TRUE)
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}
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} else if (predcontrib) {
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## Predict contribution
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arr <- aperm(a = arr, perm = c(2, 3, 1)) # [group, row, col]
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if (n_ret == n_row) {
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.Call(XGSetArrayDimInplace_R, arr, c(n_row, 1L))
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.Call(XGSetArrayDimNamesInplace_R, arr, list(NULL, cnames))
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} else if (n_groups != 1) {
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## turns array into list of matrices
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arr <- lapply(seq_len(n_groups), function(g) arr[g, , ])
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} else {
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## remove the first axis (group)
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newdim <- dim(arr)[2:3]
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newdn <- dimnames(arr)[2:3]
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arr <- arr[1, , ]
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.Call(XGSetArrayDimInplace_R, arr, newdim)
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.Call(XGSetArrayDimNamesInplace_R, arr, newdn)
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}
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} else if (predinteraction) {
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## Predict interaction
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arr <- aperm(a = arr, perm = c(3, 4, 1, 2)) # [group, row, col, col]
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if (n_ret == n_row) {
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.Call(XGSetArrayDimInplace_R, arr, c(n_row, 1L))
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.Call(XGSetArrayDimNamesInplace_R, arr, list(NULL, cnames))
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} else if (n_groups != 1) {
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## turns array into list of matrices
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arr <- lapply(seq_len(n_groups), function(g) arr[g, , , ])
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} else {
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## remove the first axis (group)
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arr <- arr[1, , , , drop = FALSE]
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newdim <- dim(arr)[2:4]
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newdn <- dimnames(arr)[2:4]
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.Call(XGSetArrayDimInplace_R, arr, newdim)
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.Call(XGSetArrayDimNamesInplace_R, arr, newdn)
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}
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} else {
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## Normal prediction
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if (reshape && n_groups != 1) {
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arr <- matrix(arr, ncol = n_groups, byrow = TRUE)
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} else {
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.Call(XGSetArrayDimInplace_R, arr, NULL)
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}
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if (!avoid_transpose && is.array(arr)) {
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arr <- aperm(arr)
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}
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return(arr)
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}
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+35
-13
@@ -294,8 +294,10 @@ xgb.shap.data <- function(data, shap_contrib = NULL, features = NULL, top_n = 1,
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if (is.null(features) && (is.null(model) || !inherits(model, "xgb.Booster")))
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stop("when features are not provided, one must provide an xgb.Booster model to rank the features")
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last_dim <- function(v) dim(v)[length(dim(v))]
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if (!is.null(shap_contrib) &&
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(!is.matrix(shap_contrib) || nrow(shap_contrib) != nrow(data) || ncol(shap_contrib) != ncol(data) + 1))
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(!is.array(shap_contrib) || nrow(shap_contrib) != nrow(data) || last_dim(shap_contrib) != ncol(data) + 1))
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stop("shap_contrib is not compatible with the provided data")
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if (is.character(features) && is.null(colnames(data)))
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@@ -318,19 +320,39 @@ xgb.shap.data <- function(data, shap_contrib = NULL, features = NULL, top_n = 1,
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colnames(data) <- paste0("X", seq_len(ncol(data)))
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}
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if (!is.null(shap_contrib)) {
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if (is.list(shap_contrib)) { # multiclass: either choose a class or merge
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shap_contrib <- if (!is.null(target_class)) shap_contrib[[target_class + 1]] else Reduce("+", lapply(shap_contrib, abs))
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}
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shap_contrib <- shap_contrib[idx, ]
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if (is.null(colnames(shap_contrib))) {
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colnames(shap_contrib) <- paste0("X", seq_len(ncol(data)))
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}
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} else {
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shap_contrib <- predict(model, newdata = data, predcontrib = TRUE, approxcontrib = approxcontrib)
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if (is.list(shap_contrib)) { # multiclass: either choose a class or merge
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shap_contrib <- if (!is.null(target_class)) shap_contrib[[target_class + 1]] else Reduce("+", lapply(shap_contrib, abs))
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reshape_3d_shap_contrib <- function(shap_contrib, target_class) {
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# multiclass: either choose a class or merge
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if (is.list(shap_contrib)) {
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if (!is.null(target_class)) {
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shap_contrib <- shap_contrib[[target_class + 1]]
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} else {
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shap_contrib <- Reduce("+", lapply(shap_contrib, abs))
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}
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} else if (length(dim(shap_contrib)) > 2) {
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if (!is.null(target_class)) {
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orig_shape <- dim(shap_contrib)
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shap_contrib <- shap_contrib[, target_class + 1, , drop = TRUE]
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if (!is.matrix(shap_contrib)) {
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shap_contrib <- matrix(shap_contrib, orig_shape[c(1L, 3L)])
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}
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} else {
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shap_contrib <- apply(abs(shap_contrib), c(1L, 3L), sum)
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}
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}
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return(shap_contrib)
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}
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if (is.null(shap_contrib)) {
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shap_contrib <- predict(
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model,
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newdata = data,
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predcontrib = TRUE,
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approxcontrib = approxcontrib
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)
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}
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shap_contrib <- reshape_3d_shap_contrib(shap_contrib, target_class)
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if (is.null(colnames(shap_contrib))) {
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colnames(shap_contrib) <- paste0("X", seq_len(ncol(data)))
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}
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if (is.null(features)) {
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