55 lines
1.9 KiB
R
55 lines
1.9 KiB
R
setClass("xgb.Booster")
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#' Predict method for eXtreme Gradient Boosting model
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#'
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#' Predicted values based on xgboost model object.
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#'
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#' @param object Object of class "xgb.Boost"
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#' @param newdata takes \code{matrix}, \code{dgCMatrix}, local data file or
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#' \code{xgb.DMatrix}.
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#' @param outputmargin whether the prediction should be shown in the original
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#' value of sum of functions, when outputmargin=TRUE, the prediction is
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#' untransformed margin value. In logistic regression, outputmargin=T will
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#' output value before logistic transformation.
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#' @param predleaf whether predict leaf index instead
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#' @param ntreelimit limit number of trees used in prediction, this parameter is
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#' only valid for gbtree, but not for gblinear. set it to be value bigger
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#' than 0. It will use all trees by default.
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#' @examples
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#' data(agaricus.train, package='xgboost')
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#' data(agaricus.test, package='xgboost')
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#' train <- agaricus.train
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#' test <- agaricus.test
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#' bst <- xgboost(data = train$data, label = train$label, max.depth = 2,
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#' eta = 1, nround = 2,objective = "binary:logistic")
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#' pred <- predict(bst, test$data)
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#' @export
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#'
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setMethod("predict", signature = "xgb.Booster",
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definition = function(object, newdata, missing = NULL, outputmargin = FALSE, ntreelimit = NULL, predleaf = FALSE) {
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if (class(newdata) != "xgb.DMatrix") {
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if (is.null(missing)) {
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newdata <- xgb.DMatrix(newdata)
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} else {
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newdata <- xgb.DMatrix(newdata, missing = missing)
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}
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}
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if (is.null(ntreelimit)) {
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ntreelimit <- 0
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} else {
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if (ntreelimit < 1){
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stop("predict: ntreelimit must be equal to or greater than 1")
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}
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}
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option = 0
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if (outputmargin) {
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option <- option + 1
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}
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if (predleaf) {
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option <- option + 2
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}
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ret <- .Call("XGBoosterPredict_R", object, newdata, as.integer(option), as.integer(ntreelimit), PACKAGE = "xgboost")
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return(ret)
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})
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