38 lines
1.4 KiB
R
38 lines
1.4 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 ntreelimit limit number of trees used in prediction, this parameter is only valid for gbtree, but not for gblinear.
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#' set it to be value bigger than 0. It will use all trees by default.
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#' @examples
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#' data(iris)
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#' bst <- xgboost(as.matrix(iris[,1:4]),as.numeric(iris[,5]), nrounds = 2)
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#' pred <- predict(bst, as.matrix(iris[,1:4]))
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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, outputmargin = FALSE, ntreelimit = NULL) {
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if (class(newdata) != "xgb.DMatrix") {
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newdata <- xgb.DMatrix(newdata)
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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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ret <- .Call("XGBoosterPredict_R", object, newdata, as.integer(outputmargin), as.integer(ntreelimit), PACKAGE = "xgboost")
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return(ret)
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})
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