219 lines
7.9 KiB
R
219 lines
7.9 KiB
R
# Construct a Booster from cachelist
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# internal utility function
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xgb.Booster <- function(params = list(), cachelist = list(), modelfile = NULL) {
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if (typeof(cachelist) != "list") {
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stop("xgb.Booster: only accepts list of DMatrix as cachelist")
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}
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for (dm in cachelist) {
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if (class(dm) != "xgb.DMatrix") {
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stop("xgb.Booster: only accepts list of DMatrix as cachelist")
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}
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}
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handle <- .Call("XGBoosterCreate_R", cachelist, PACKAGE = "xgboost")
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if (length(params) != 0) {
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for (i in 1:length(params)) {
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p <- params[i]
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.Call("XGBoosterSetParam_R", handle, gsub("\\.", "_", names(p)), as.character(p),
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PACKAGE = "xgboost")
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}
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}
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if (!is.null(modelfile)) {
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if (typeof(modelfile) == "character") {
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.Call("XGBoosterLoadModel_R", handle, modelfile, PACKAGE = "xgboost")
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} else if (typeof(modelfile) == "raw") {
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.Call("XGBoosterLoadModelFromRaw_R", handle, modelfile, PACKAGE = "xgboost")
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} else {
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stop("xgb.Booster: modelfile must be character or raw vector")
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}
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}
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return(structure(handle, class = "xgb.Booster.handle"))
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}
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# Convert xgb.Booster.handle to xgb.Booster
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# internal utility function
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xgb.handleToBooster <- function(handle, raw = NULL)
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{
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bst <- list(handle = handle, raw = raw)
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class(bst) <- "xgb.Booster"
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return(bst)
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}
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# Check whether an xgb.Booster object is complete
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# internal utility function
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xgb.Booster.check <- function(bst, saveraw = TRUE)
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{
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isnull <- is.null(bst$handle)
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if (!isnull) {
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isnull <- .Call("XGCheckNullPtr_R", bst$handle, PACKAGE="xgboost")
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}
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if (isnull) {
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bst$handle <- xgb.Booster(modelfile = bst$raw)
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} else {
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if (is.null(bst$raw) && saveraw)
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bst$raw <- xgb.save.raw(bst$handle)
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}
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return(bst)
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}
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#' Predict method for eXtreme Gradient Boosting model
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#'
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#' Predicted values based on either xgboost model or model handle object.
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#'
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#' @param object Object of class \code{xgb.Booster} or \code{xgb.Booster.handle}
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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 missing Missing is only used when input is dense matrix, pick a float
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#' value that represents missing value. Sometime a data use 0 or other extreme value to represents missing values.
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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
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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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#' @param predleaf whether predict leaf index instead. If set to TRUE, the output will be a matrix object.
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#' @param ... Parameters pass to \code{predict.xgb.Booster}
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#'
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#' @details
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#' The option \code{ntreelimit} purpose is to let the user train a model with lots
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#' of trees but use only the first trees for prediction to avoid overfitting
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#' (without having to train a new model with less trees).
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#'
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#' The option \code{predleaf} purpose is inspired from §3.1 of the paper
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#' \code{Practical Lessons from Predicting Clicks on Ads at Facebook}.
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#' The idea is to use the model as a generator of new features which capture non linear link
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#' from original features.
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#'
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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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#'
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#' bst <- xgboost(data = train$data, label = train$label, max.depth = 2,
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#' eta = 1, nthread = 2, nround = 2,objective = "binary:logistic")
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#' pred <- predict(bst, test$data)
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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,
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outputmargin = FALSE, ntreelimit = NULL, predleaf = FALSE) {
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if (class(object) != "xgb.Booster"){
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stop("predict: model in prediction must be of class xgb.Booster")
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} else {
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object <- xgb.Booster.check(object, saveraw = FALSE)
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}
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if (class(newdata) != "xgb.DMatrix") {
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newdata <- xgb.DMatrix(newdata, missing = missing)
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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$handle, newdata, as.integer(option),
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as.integer(ntreelimit), PACKAGE = "xgboost")
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if (predleaf){
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len <- getinfo(newdata, "nrow")
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if (length(ret) == len){
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ret <- matrix(ret,ncol = 1)
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} else {
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ret <- matrix(ret, ncol = len)
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ret <- t(ret)
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}
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}
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return(ret)
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}
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#' @rdname predict.xgb.Booster
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#' @export
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predict.xgb.Booster.handle <- function(object, ...) {
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bst <- xgb.handleToBooster(object)
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ret <- predict(bst, ...)
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return(ret)
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}
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#' Accessors for serializable attributes of a model.
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#'
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#' These methods allow to manipulate key-value attribute strings of an xgboost model.
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#'
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#' @param object Object of class \code{xgb.Booster} or \code{xgb.Booster.handle}.
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#' @param which a non-empty character string specifying which attribute is to be accessed.
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#' @param value a value of an attribute. Non-character values are converted to character.
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#' When length of a \code{value} vector is more than one, only the first element is used.
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#'
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#' @details
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#' Note that the xgboost model attributes are a separate concept from the attributes in R.
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#' Specifically, they refer to key-value strings that can be attached to an xgboost model
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#' and stored within the model's binary representation.
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#' In contrast, any R-attribute assigned to an R-object of \code{xgb.Booster} class
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#' would not be saved by \code{xgb.save}, since xgboost model is an external memory object
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#' and its serialization is handled extrnally.
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#'
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#' Also note that the attribute setter would usually work more efficiently for \code{xgb.Booster.handle}
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#' than for \code{xgb.Booster}, since only just a handle would need to be copied.
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#'
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#' @return
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#' \code{xgb.attr} returns either a string value of an attribute
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#' or \code{NULL} if an attribute wasn't stored in a model.
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#'
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#' @examples
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#' data(agaricus.train, package='xgboost')
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#' train <- agaricus.train
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#'
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#' bst <- xgboost(data = train$data, label = train$label, max.depth = 2,
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#' eta = 1, nthread = 2, nround = 2, objective = "binary:logistic")
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#'
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#' xgb.attr(bst, "my_attribute") <- "my attribute value"
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#' print(xgb.attr(bst, "my_attribute"))
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#'
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#' xgb.save(bst, 'xgb.model')
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#' bst1 <- xgb.load('xgb.model')
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#' print(xgb.attr(bst1, "my_attribute"))
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#'
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#' @rdname xgb.attr
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#' @export
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xgb.attr <- function(object, which) {
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if (is.null(which) | nchar(as.character(which)[1]) == 0) stop("invalid attribute name")
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handle = xgb.get.handle(object, "xgb.attr")
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.Call("XGBoosterGetAttr_R", handle, as.character(which)[1], PACKAGE="xgboost")
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}
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#' @rdname xgb.attr
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#' @export
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`xgb.attr<-` <- function(object, which, value) {
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if (is.null(which) | nchar(as.character(which)[1]) == 0) stop("invalid attribute name")
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handle = xgb.get.handle(object, "xgb.attr")
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# TODO: setting NULL value to remove an attribute
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.Call("XGBoosterSetAttr_R", handle, as.character(which)[1], as.character(value)[1], PACKAGE="xgboost")
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if (is(object, 'xgb.Booster') && !is.null(object$raw)) {
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object$raw <- xgb.save.raw(object$handle)
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}
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object
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}
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# Return a valid handle out of either xgb.Booster.handle or xgb.Booster
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# internal utility function
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xgb.get.handle <- function(object, caller="") {
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handle = switch(class(object),
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xgb.Booster = object$handle,
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xgb.Booster.handle = object,
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stop(caller, ": argument must be either xgb.Booster or xgb.Booster.handle")
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)
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if (is.null(handle) | .Call("XGCheckNullPtr_R", handle, PACKAGE="xgboost")) {
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stop(caller, ": invalid xgb.Booster.handle")
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}
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handle
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}
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