Merge pull request #1050 from khotilov/S4toS3
[R-package] Convert to S3; some new DMatrix methods
This commit is contained in:
Tong He
commit
4af55518f2
@ -1,6 +1,16 @@
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# Generated by roxygen2: do not edit by hand
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S3method("[",xgb.DMatrix)
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S3method("dimnames<-",xgb.DMatrix)
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S3method(dim,xgb.DMatrix)
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S3method(dimnames,xgb.DMatrix)
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S3method(getinfo,xgb.DMatrix)
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S3method(predict,xgb.Booster)
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S3method(predict,xgb.Booster.handle)
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S3method(setinfo,xgb.DMatrix)
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S3method(slice,xgb.DMatrix)
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export(getinfo)
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export(print.xgb.DMatrix)
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export(setinfo)
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export(slice)
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export(xgb.DMatrix)
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@ -19,8 +29,6 @@ export(xgb.save)
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export(xgb.save.raw)
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export(xgb.train)
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export(xgboost)
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exportMethods(nrow)
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exportMethods(predict)
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import(methods)
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importClassesFrom(Matrix,dgCMatrix)
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importClassesFrom(Matrix,dgeMatrix)
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@ -1,55 +0,0 @@
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setClass('xgb.DMatrix')
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#' Get information of an xgb.DMatrix object
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#'
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#' Get information of an xgb.DMatrix object
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#'
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#' The information can be one of the following:
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#'
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#' \itemize{
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#' \item \code{label}: label Xgboost learn from ;
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#' \item \code{weight}: to do a weight rescale ;
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#' \item \code{base_margin}: base margin is the base prediction Xgboost will boost from ;
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#' \item \code{nrow}: number of rows of the \code{xgb.DMatrix}.
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#' }
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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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#' dtrain <- xgb.DMatrix(train$data, label=train$label)
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#' labels <- getinfo(dtrain, 'label')
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#' setinfo(dtrain, 'label', 1-labels)
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#' labels2 <- getinfo(dtrain, 'label')
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#' stopifnot(all(labels2 == 1-labels))
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#' @rdname getinfo
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#' @export
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getinfo <- function(object, ...){
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UseMethod("getinfo")
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}
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#' @param object Object of class \code{xgb.DMatrix}
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#' @param name the name of the field to get
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#' @param ... other parameters
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#' @rdname getinfo
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#' @method getinfo xgb.DMatrix
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setMethod("getinfo", signature = "xgb.DMatrix",
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definition = function(object, name) {
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if (typeof(name) != "character") {
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stop("xgb.getinfo: name must be character")
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}
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if (class(object) != "xgb.DMatrix") {
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stop("xgb.setinfo: first argument dtrain must be xgb.DMatrix")
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}
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if (name != "label" && name != "weight" &&
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name != "base_margin" && name != "nrow") {
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stop(paste("xgb.getinfo: unknown info name", name))
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}
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if (name != "nrow"){
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ret <- .Call("XGDMatrixGetInfo_R", object, name, PACKAGE = "xgboost")
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} else {
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ret <- xgb.numrow(object)
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}
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return(ret)
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})
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@ -1,19 +0,0 @@
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setGeneric("nrow")
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#' @title Number of xgb.DMatrix rows
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#' @description \code{nrow} return the number of rows present in the \code{xgb.DMatrix}.
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#' @param x Object of class \code{xgb.DMatrix}
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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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#' dtrain <- xgb.DMatrix(train$data, label=train$label)
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#' stopifnot(nrow(dtrain) == nrow(train$data))
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#'
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#' @export
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setMethod("nrow",
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signature = "xgb.DMatrix",
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definition = function(x) {
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xgb.numrow(x)
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}
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)
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@ -1,18 +0,0 @@
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#' Predict method for eXtreme Gradient Boosting model handle
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#'
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#' Predicted values based on xgb.Booster.handle object.
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#'
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#' @param object Object of class "xgb.Boost.handle"
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#' @param ... Parameters pass to \code{predict.xgb.Booster}
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#'
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setMethod("predict", signature = "xgb.Booster.handle",
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definition = function(object, ...) {
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if (class(object) != "xgb.Booster.handle"){
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stop("predict: model in prediction must be of class xgb.Booster.handle")
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}
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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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@ -1,37 +0,0 @@
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#' Set information of an xgb.DMatrix object
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#'
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#' Set information of an xgb.DMatrix object
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#'
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#' It can be one of the following:
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#'
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#' \itemize{
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#' \item \code{label}: label Xgboost learn from ;
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#' \item \code{weight}: to do a weight rescale ;
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#' \item \code{base_margin}: base margin is the base prediction Xgboost will boost from ;
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#' \item \code{group}.
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#' }
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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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#' dtrain <- xgb.DMatrix(train$data, label=train$label)
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#' labels <- getinfo(dtrain, 'label')
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#' setinfo(dtrain, 'label', 1-labels)
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#' labels2 <- getinfo(dtrain, 'label')
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#' stopifnot(all(labels2 == 1-labels))
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#' @rdname setinfo
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#' @export
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setinfo <- function(object, ...){
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UseMethod("setinfo")
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}
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#' @param object Object of class "xgb.DMatrix"
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#' @param name the name of the field to get
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#' @param info the specific field of information to set
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#' @param ... other parameters
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#' @rdname setinfo
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#' @method setinfo xgb.DMatrix
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setMethod("setinfo", signature = "xgb.DMatrix",
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definition = function(object, name, info) {
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xgb.setinfo(object, name, info)
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})
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@ -1,44 +0,0 @@
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setClass('xgb.DMatrix')
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#' Get a new DMatrix containing the specified rows of
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#' orginal xgb.DMatrix object
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#'
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#' Get a new DMatrix containing the specified rows of
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#' orginal xgb.DMatrix object
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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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#' dtrain <- xgb.DMatrix(train$data, label=train$label)
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#' dsub <- slice(dtrain, 1:3)
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#' @rdname slice
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#' @export
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slice <- function(object, ...){
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UseMethod("slice")
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}
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#' @param object Object of class "xgb.DMatrix"
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#' @param idxset a integer vector of indices of rows needed
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#' @param ... other parameters
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#' @rdname slice
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#' @method slice xgb.DMatrix
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setMethod("slice", signature = "xgb.DMatrix",
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definition = function(object, idxset, ...) {
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if (class(object) != "xgb.DMatrix") {
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stop("slice: first argument dtrain must be xgb.DMatrix")
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}
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ret <- .Call("XGDMatrixSliceDMatrix_R", object, idxset,
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PACKAGE = "xgboost")
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attr_list <- attributes(object)
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nr <- xgb.numrow(object)
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len <- sapply(attr_list,length)
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ind <- which(len == nr)
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if (length(ind) > 0) {
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nms <- names(attr_list)[ind]
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for (i in 1:length(ind)) {
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attr(ret,nms[i]) <- attr(object,nms[i])[idxset]
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}
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}
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return(structure(ret, class = "xgb.DMatrix"))
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})
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@ -1,4 +1,4 @@
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#' @importClassesFrom Matrix dgCMatrix dgeMatrix
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#' @importClassesFrom Matrix dgCMatrix dgeMatrix
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#' @import methods
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# depends on matrix
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@ -9,131 +9,10 @@
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library.dynam.unload("xgboost", libpath)
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}
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# set information into dmatrix, this mutate dmatrix
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xgb.setinfo <- function(dmat, name, info) {
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if (class(dmat) != "xgb.DMatrix") {
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stop("xgb.setinfo: first argument dtrain must be xgb.DMatrix")
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}
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if (name == "label") {
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if (length(info) != xgb.numrow(dmat))
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stop("The length of labels must equal to the number of rows in the input data")
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.Call("XGDMatrixSetInfo_R", dmat, name, as.numeric(info),
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PACKAGE = "xgboost")
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return(TRUE)
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}
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if (name == "weight") {
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if (length(info) != xgb.numrow(dmat))
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stop("The length of weights must equal to the number of rows in the input data")
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.Call("XGDMatrixSetInfo_R", dmat, name, as.numeric(info),
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PACKAGE = "xgboost")
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return(TRUE)
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}
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if (name == "base_margin") {
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# if (length(info)!=xgb.numrow(dmat))
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# stop("The length of base margin must equal to the number of rows in the input data")
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.Call("XGDMatrixSetInfo_R", dmat, name, as.numeric(info),
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PACKAGE = "xgboost")
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return(TRUE)
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}
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if (name == "group") {
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if (sum(info) != xgb.numrow(dmat))
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stop("The sum of groups must equal to the number of rows in the input data")
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.Call("XGDMatrixSetInfo_R", dmat, name, as.integer(info),
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PACKAGE = "xgboost")
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return(TRUE)
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}
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stop(paste("xgb.setinfo: unknown info name", name))
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return(FALSE)
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}
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# construct a Booster from cachelist
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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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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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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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## ----the following are low level iteratively function, not needed if
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## ----the following are low level iterative functions, not needed if
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## you do not want to use them ---------------------------------------
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# get dmatrix from data, label
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xgb.get.DMatrix <- function(data, label = NULL, missing = NA, weight = NULL) {
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inClass <- class(data)
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if (inClass == "dgCMatrix" || inClass == "matrix") {
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if (is.null(label)) {
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stop("xgboost: need label when data is a matrix")
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}
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dtrain <- xgb.DMatrix(data, label = label, missing = missing)
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if (!is.null(weight)){
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xgb.setinfo(dtrain, "weight", weight)
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}
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} else {
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if (!is.null(label)) {
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warning("xgboost: label will be ignored.")
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}
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if (inClass == "character") {
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dtrain <- xgb.DMatrix(data)
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} else if (inClass == "xgb.DMatrix") {
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dtrain <- data
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} else if (inClass == "data.frame") {
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stop("xgboost only support numerical matrix input,
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use 'data.matrix' to transform the data.")
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} else {
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stop("xgboost: Invalid input of data")
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}
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}
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return (dtrain)
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}
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xgb.numrow <- function(dmat) {
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nrow <- .Call("XGDMatrixNumRow_R", dmat, PACKAGE="xgboost")
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return(nrow)
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}
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# iteratively update booster with customized statistics
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xgb.iter.boost <- function(booster, dtrain, gpair) {
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if (class(booster) != "xgb.Booster.handle") {
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@ -227,7 +106,7 @@ xgb.cv.mknfold <- function(dall, nfold, param, stratified, folds) {
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"\tConsider providing pre-computed CV-folds through the folds parameter.")
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}
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y <- getinfo(dall, 'label')
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randidx <- sample(1 : xgb.numrow(dall))
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randidx <- sample(1 : nrow(dall))
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if (stratified & length(y) == length(randidx)) {
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y <- y[randidx]
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#
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@ -1,13 +1,65 @@
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setClass("xgb.Booster.handle")
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setClass("xgb.Booster",
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slots = c(handle = "xgb.Booster.handle",
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raw = "raw"))
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# 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 xgboost model object.
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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 "xgb.Boost"
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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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@ -20,6 +72,7 @@ setClass("xgb.Booster",
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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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@ -36,13 +89,14 @@ setClass("xgb.Booster",
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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$label, max.depth = 2,
|
||||
#' eta = 1, nthread = 2, nround = 2,objective = "binary:logistic")
|
||||
#' pred <- predict(bst, test$data)
|
||||
#' @rdname predict.xgb.Booster
|
||||
#' @export
|
||||
setMethod("predict", signature = "xgb.Booster",
|
||||
definition = function(object, newdata, missing = NA,
|
||||
outputmargin = FALSE, ntreelimit = NULL, predleaf = FALSE) {
|
||||
predict.xgb.Booster <- function(object, newdata, missing = NA,
|
||||
outputmargin = FALSE, ntreelimit = NULL, predleaf = FALSE) {
|
||||
if (class(object) != "xgb.Booster"){
|
||||
stop("predict: model in prediction must be of class xgb.Booster")
|
||||
} else {
|
||||
@ -77,4 +131,14 @@ setMethod("predict", signature = "xgb.Booster",
|
||||
}
|
||||
}
|
||||
return(ret)
|
||||
})
|
||||
}
|
||||
|
||||
#' @rdname predict.xgb.Booster
|
||||
#' @export
|
||||
predict.xgb.Booster.handle <- function(object, ...) {
|
||||
|
||||
bst <- xgb.handleToBooster(object)
|
||||
|
||||
ret <- predict(bst, ...)
|
||||
return(ret)
|
||||
}
|
||||
@ -1,9 +1,9 @@
|
||||
#' Contruct xgb.DMatrix object
|
||||
#'
|
||||
#' Contruct xgb.DMatrix object from dense matrix, sparse matrix or local file.
|
||||
#' Contruct xgb.DMatrix object from dense matrix, sparse matrix
|
||||
#' or local file (that was created previously by saving an \code{xgb.DMatrix}).
|
||||
#'
|
||||
#' @param data a \code{matrix} object, a \code{dgCMatrix} object or a character
|
||||
#' indicating the data file.
|
||||
#' @param data a \code{matrix} object, a \code{dgCMatrix} object or a character representing a filename
|
||||
#' @param info a list of information of the xgb.DMatrix object
|
||||
#' @param missing Missing is only used when input is dense matrix, pick a float
|
||||
#' value that represents missing value. Sometime a data use 0 or other extreme value to represents missing values.
|
||||
@ -18,27 +18,348 @@
|
||||
#' dtrain <- xgb.DMatrix('xgb.DMatrix.data')
|
||||
#' @export
|
||||
xgb.DMatrix <- function(data, info = list(), missing = NA, ...) {
|
||||
cnames <- NULL
|
||||
if (typeof(data) == "character") {
|
||||
handle <- .Call("XGDMatrixCreateFromFile_R", data, as.integer(FALSE),
|
||||
PACKAGE = "xgboost")
|
||||
} else if (is.matrix(data)) {
|
||||
handle <- .Call("XGDMatrixCreateFromMat_R", data, missing,
|
||||
PACKAGE = "xgboost")
|
||||
cnames <- colnames(data)
|
||||
} else if (class(data) == "dgCMatrix") {
|
||||
handle <- .Call("XGDMatrixCreateFromCSC_R", data@p, data@i, data@x,
|
||||
PACKAGE = "xgboost")
|
||||
cnames <- colnames(data)
|
||||
} else {
|
||||
stop(paste("xgb.DMatrix: does not support to construct from ",
|
||||
typeof(data)))
|
||||
}
|
||||
dmat <- structure(handle, class = "xgb.DMatrix")
|
||||
dmat <- handle
|
||||
attributes(dmat) <- list(.Dimnames = list(NULL, cnames), class = "xgb.DMatrix")
|
||||
#dmat <- list(handle = handle, colnames = cnames)
|
||||
#attr(dmat, 'class') <- "xgb.DMatrix"
|
||||
|
||||
info <- append(info, list(...))
|
||||
if (length(info) == 0)
|
||||
return(dmat)
|
||||
for (i in 1:length(info)) {
|
||||
p <- info[i]
|
||||
xgb.setinfo(dmat, names(p), p[[1]])
|
||||
setinfo(dmat, names(p), p[[1]])
|
||||
}
|
||||
return(dmat)
|
||||
}
|
||||
|
||||
|
||||
# get dmatrix from data, label
|
||||
# internal helper method
|
||||
xgb.get.DMatrix <- function(data, label = NULL, missing = NA, weight = NULL) {
|
||||
inClass <- class(data)
|
||||
if (inClass == "dgCMatrix" || inClass == "matrix") {
|
||||
if (is.null(label)) {
|
||||
stop("xgboost: need label when data is a matrix")
|
||||
}
|
||||
dtrain <- xgb.DMatrix(data, label = label, missing = missing)
|
||||
if (!is.null(weight)){
|
||||
setinfo(dtrain, "weight", weight)
|
||||
}
|
||||
} else {
|
||||
if (!is.null(label)) {
|
||||
warning("xgboost: label will be ignored.")
|
||||
}
|
||||
if (inClass == "character") {
|
||||
dtrain <- xgb.DMatrix(data)
|
||||
} else if (inClass == "xgb.DMatrix") {
|
||||
dtrain <- data
|
||||
} else if (inClass == "data.frame") {
|
||||
stop("xgboost only support numerical matrix input,
|
||||
use 'data.matrix' to transform the data.")
|
||||
} else {
|
||||
stop("xgboost: Invalid input of data")
|
||||
}
|
||||
}
|
||||
return (dtrain)
|
||||
}
|
||||
|
||||
|
||||
#' Dimensions of xgb.DMatrix
|
||||
#'
|
||||
#' Returns a vector of numbers of rows and of columns in an \code{xgb.DMatrix}.
|
||||
#' @param x Object of class \code{xgb.DMatrix}
|
||||
#'
|
||||
#' @details
|
||||
#' Note: since \code{nrow} and \code{ncol} internally use \code{dim}, they can also
|
||||
#' be directly used with an \code{xgb.DMatrix} object.
|
||||
#'
|
||||
#' @examples
|
||||
#' data(agaricus.train, package='xgboost')
|
||||
#' train <- agaricus.train
|
||||
#' dtrain <- xgb.DMatrix(train$data, label=train$label)
|
||||
#'
|
||||
#' stopifnot(nrow(dtrain) == nrow(train$data))
|
||||
#' stopifnot(ncol(dtrain) == ncol(train$data))
|
||||
#' stopifnot(all(dim(dtrain) == dim(train$data)))
|
||||
#'
|
||||
#' @export
|
||||
dim.xgb.DMatrix <- function(x) {
|
||||
c(.Call("XGDMatrixNumRow_R", x, PACKAGE="xgboost"),
|
||||
.Call("XGDMatrixNumCol_R", x, PACKAGE="xgboost"))
|
||||
}
|
||||
|
||||
|
||||
#' Handling of column names of \code{xgb.DMatrix}
|
||||
#'
|
||||
#' Only column names are supported for \code{xgb.DMatrix}, thus setting of
|
||||
#' row names would have no effect and returnten row names would be NULL.
|
||||
#'
|
||||
#' @param x object of class \code{xgb.DMatrix}
|
||||
#' @param value a list of two elements: the first one is ignored
|
||||
#' and the second one is column names
|
||||
#'
|
||||
#' @details
|
||||
#' Generic \code{dimnames} methods are used by \code{colnames}.
|
||||
#' Since row names are irrelevant, it is recommended to use \code{colnames} directly.
|
||||
#'
|
||||
#' @examples
|
||||
#' data(agaricus.train, package='xgboost')
|
||||
#' train <- agaricus.train
|
||||
#' dtrain <- xgb.DMatrix(train$data, label=train$label)
|
||||
#' dimnames(dtrain)
|
||||
#' colnames(dtrain)
|
||||
#' colnames(dtrain) <- make.names(1:ncol(train$data))
|
||||
#' print(dtrain, verbose=TRUE)
|
||||
#'
|
||||
#' @rdname dimnames.xgb.DMatrix
|
||||
#' @export
|
||||
dimnames.xgb.DMatrix <- function(x) {
|
||||
attr(x, '.Dimnames')
|
||||
}
|
||||
|
||||
#' @rdname dimnames.xgb.DMatrix
|
||||
#' @export
|
||||
`dimnames<-.xgb.DMatrix` <- function(x, value) {
|
||||
if (!is.list(value) || length(value) != 2L)
|
||||
stop("invalid 'dimnames' given: must be a list of two elements")
|
||||
if (!is.null(value[[1L]]))
|
||||
stop("xgb.DMatrix does not have rownames")
|
||||
if (is.null(value[[2]])) {
|
||||
attr(x, '.Dimnames') <- NULL
|
||||
return(x)
|
||||
}
|
||||
if (ncol(x) != length(value[[2]]))
|
||||
stop("can't assign ", length(value[[2]]), " colnames to a ",
|
||||
ncol(x), " column xgb.DMatrix")
|
||||
attr(x, '.Dimnames') <- value
|
||||
x
|
||||
}
|
||||
|
||||
|
||||
#' Get information of an xgb.DMatrix object
|
||||
#'
|
||||
#' Get information of an xgb.DMatrix object
|
||||
#' @param object Object of class \code{xgb.DMatrix}
|
||||
#' @param name the name of the information field to get (see details)
|
||||
#' @param ... other parameters
|
||||
#'
|
||||
#' @details
|
||||
#' The \code{name} field can be one of the following:
|
||||
#'
|
||||
#' \itemize{
|
||||
#' \item \code{label}: label Xgboost learn from ;
|
||||
#' \item \code{weight}: to do a weight rescale ;
|
||||
#' \item \code{base_margin}: base margin is the base prediction Xgboost will boost from ;
|
||||
#' \item \code{nrow}: number of rows of the \code{xgb.DMatrix}.
|
||||
#' }
|
||||
#'
|
||||
#' @examples
|
||||
#' data(agaricus.train, package='xgboost')
|
||||
#' train <- agaricus.train
|
||||
#' dtrain <- xgb.DMatrix(train$data, label=train$label)
|
||||
#'
|
||||
#' labels <- getinfo(dtrain, 'label')
|
||||
#' setinfo(dtrain, 'label', 1-labels)
|
||||
#'
|
||||
#' labels2 <- getinfo(dtrain, 'label')
|
||||
#' stopifnot(all(labels2 == 1-labels))
|
||||
#' @rdname getinfo
|
||||
#' @export
|
||||
getinfo <- function(object, ...) UseMethod("getinfo")
|
||||
|
||||
#' @rdname getinfo
|
||||
#' @export
|
||||
getinfo.xgb.DMatrix <- function(object, name) {
|
||||
if (typeof(name) != "character") {
|
||||
stop("getinfo: name must be character")
|
||||
}
|
||||
if (name != "label" && name != "weight" &&
|
||||
name != "base_margin" && name != "nrow") {
|
||||
stop(paste("getinfo: unknown info name", name))
|
||||
}
|
||||
if (name != "nrow"){
|
||||
ret <- .Call("XGDMatrixGetInfo_R", object, name, PACKAGE = "xgboost")
|
||||
} else {
|
||||
ret <- nrow(object)
|
||||
}
|
||||
return(ret)
|
||||
}
|
||||
|
||||
|
||||
#' Set information of an xgb.DMatrix object
|
||||
#'
|
||||
#' Set information of an xgb.DMatrix object
|
||||
#'
|
||||
#' @param object Object of class "xgb.DMatrix"
|
||||
#' @param name the name of the field to get
|
||||
#' @param info the specific field of information to set
|
||||
#' @param ... other parameters
|
||||
#'
|
||||
#' @details
|
||||
#' The \code{name} field can be one of the following:
|
||||
#'
|
||||
#' \itemize{
|
||||
#' \item \code{label}: label Xgboost learn from ;
|
||||
#' \item \code{weight}: to do a weight rescale ;
|
||||
#' \item \code{base_margin}: base margin is the base prediction Xgboost will boost from ;
|
||||
#' \item \code{group}.
|
||||
#' }
|
||||
#'
|
||||
#' @examples
|
||||
#' data(agaricus.train, package='xgboost')
|
||||
#' train <- agaricus.train
|
||||
#' dtrain <- xgb.DMatrix(train$data, label=train$label)
|
||||
#'
|
||||
#' labels <- getinfo(dtrain, 'label')
|
||||
#' setinfo(dtrain, 'label', 1-labels)
|
||||
#' labels2 <- getinfo(dtrain, 'label')
|
||||
#' stopifnot(all.equal(labels2, 1-labels))
|
||||
#' @rdname setinfo
|
||||
#' @export
|
||||
setinfo <- function(object, ...) UseMethod("setinfo")
|
||||
|
||||
#' @rdname setinfo
|
||||
#' @export
|
||||
setinfo.xgb.DMatrix <- function(object, name, info) {
|
||||
if (name == "label") {
|
||||
if (length(info) != nrow(object))
|
||||
stop("The length of labels must equal to the number of rows in the input data")
|
||||
.Call("XGDMatrixSetInfo_R", object, name, as.numeric(info),
|
||||
PACKAGE = "xgboost")
|
||||
return(TRUE)
|
||||
}
|
||||
if (name == "weight") {
|
||||
if (length(info) != nrow(object))
|
||||
stop("The length of weights must equal to the number of rows in the input data")
|
||||
.Call("XGDMatrixSetInfo_R", object, name, as.numeric(info),
|
||||
PACKAGE = "xgboost")
|
||||
return(TRUE)
|
||||
}
|
||||
if (name == "base_margin") {
|
||||
# if (length(info)!=nrow(object))
|
||||
# stop("The length of base margin must equal to the number of rows in the input data")
|
||||
.Call("XGDMatrixSetInfo_R", object, name, as.numeric(info),
|
||||
PACKAGE = "xgboost")
|
||||
return(TRUE)
|
||||
}
|
||||
if (name == "group") {
|
||||
if (sum(info) != nrow(object))
|
||||
stop("The sum of groups must equal to the number of rows in the input data")
|
||||
.Call("XGDMatrixSetInfo_R", object, name, as.integer(info),
|
||||
PACKAGE = "xgboost")
|
||||
return(TRUE)
|
||||
}
|
||||
stop(paste("setinfo: unknown info name", name))
|
||||
return(FALSE)
|
||||
}
|
||||
|
||||
|
||||
#' Get a new DMatrix containing the specified rows of
|
||||
#' orginal xgb.DMatrix object
|
||||
#'
|
||||
#' Get a new DMatrix containing the specified rows of
|
||||
#' orginal xgb.DMatrix object
|
||||
#'
|
||||
#' @param object Object of class "xgb.DMatrix"
|
||||
#' @param idxset a integer vector of indices of rows needed
|
||||
#' @param colset currently not used (columns subsetting is not available)
|
||||
#' @param ... other parameters (currently not used)
|
||||
#'
|
||||
#' @examples
|
||||
#' data(agaricus.train, package='xgboost')
|
||||
#' train <- agaricus.train
|
||||
#' dtrain <- xgb.DMatrix(train$data, label=train$label)
|
||||
#'
|
||||
#' dsub <- slice(dtrain, 1:42)
|
||||
#' labels1 <- getinfo(dsub, 'label')
|
||||
#' dsub <- dtrain[1:42, ]
|
||||
#' labels2 <- getinfo(dsub, 'label')
|
||||
#' all.equal(labels1, labels2)
|
||||
#'
|
||||
#' @rdname slice.xgb.DMatrix
|
||||
#' @export
|
||||
slice <- function(object, ...) UseMethod("slice")
|
||||
|
||||
#' @rdname slice.xgb.DMatrix
|
||||
#' @export
|
||||
slice.xgb.DMatrix <- function(object, idxset, ...) {
|
||||
if (class(object) != "xgb.DMatrix") {
|
||||
stop("slice: first argument dtrain must be xgb.DMatrix")
|
||||
}
|
||||
ret <- .Call("XGDMatrixSliceDMatrix_R", object, idxset, PACKAGE = "xgboost")
|
||||
|
||||
attr_list <- attributes(object)
|
||||
nr <- nrow(object)
|
||||
len <- sapply(attr_list, length)
|
||||
ind <- which(len == nr)
|
||||
if (length(ind) > 0) {
|
||||
nms <- names(attr_list)[ind]
|
||||
for (i in 1:length(ind)) {
|
||||
attr(ret, nms[i]) <- attr(object, nms[i])[idxset]
|
||||
}
|
||||
}
|
||||
return(structure(ret, class = "xgb.DMatrix"))
|
||||
}
|
||||
|
||||
#' @rdname slice.xgb.DMatrix
|
||||
#' @export
|
||||
`[.xgb.DMatrix` <- function(object, idxset, colset=NULL) {
|
||||
slice(object, idxset)
|
||||
}
|
||||
|
||||
|
||||
#' Print xgb.DMatrix
|
||||
#'
|
||||
#' Print information about xgb.DMatrix.
|
||||
#' Currently it displays dimensions and presence of info-fields and colnames.
|
||||
#'
|
||||
#' @param x an xgb.DMatrix object
|
||||
#' @param verbose whether to print colnames (when present)
|
||||
#' @param ... not currently used
|
||||
#'
|
||||
#' @examples
|
||||
#' data(agaricus.train, package='xgboost')
|
||||
#' train <- agaricus.train
|
||||
#' dtrain <- xgb.DMatrix(train$data, label=train$label)
|
||||
#'
|
||||
#' dtrain
|
||||
#' print(dtrain, verbose=TRUE)
|
||||
#' @export
|
||||
print.xgb.DMatrix <- function(x, verbose=FALSE, ...) {
|
||||
cat('xgb.DMatrix dim:', nrow(x), 'x', ncol(x), ' info: ')
|
||||
infos <- c()
|
||||
if(length(getinfo(x, 'label')) > 0) infos <- 'label'
|
||||
if(length(getinfo(x, 'weight')) > 0) infos <- c(infos, 'weight')
|
||||
if(length(getinfo(x, 'base_margin')) > 0) infos <- c(infos, 'base_margin')
|
||||
if (length(infos) == 0) infos <- 'NA'
|
||||
cat(infos)
|
||||
cnames <- colnames(x)
|
||||
cat(' colnames:')
|
||||
if (verbose & !is.null(cnames)) {
|
||||
cat("\n'")
|
||||
cat(cnames, sep="','")
|
||||
cat("'")
|
||||
} else {
|
||||
if (is.null(cnames)) cat(' no')
|
||||
else cat(' yes')
|
||||
}
|
||||
cat("\n")
|
||||
invisible(x)
|
||||
}
|
||||
|
||||
@ -169,11 +169,11 @@ xgb.cv <- function(params=list(), data, nrounds, nfold, label = NULL, missing =
|
||||
num_class <- params[['num_class']]
|
||||
if (is.null(num_class))
|
||||
stop('must set num_class to use softmax')
|
||||
predictValues <- matrix(0,xgb.numrow(dtrain),num_class)
|
||||
predictValues <- matrix(0, nrow(dtrain), num_class)
|
||||
mat_pred <- TRUE
|
||||
}
|
||||
else
|
||||
predictValues <- rep(0,xgb.numrow(dtrain))
|
||||
predictValues <- rep(0, nrow(dtrain))
|
||||
history <- c()
|
||||
print.every.n <- max(as.integer(print.every.n), 1L)
|
||||
for (i in 1:nrounds) {
|
||||
|
||||
29
R-package/man/dim.xgb.DMatrix.Rd
Normal file
29
R-package/man/dim.xgb.DMatrix.Rd
Normal file
@ -0,0 +1,29 @@
|
||||
% Generated by roxygen2: do not edit by hand
|
||||
% Please edit documentation in R/xgb.DMatrix.R
|
||||
\name{dim.xgb.DMatrix}
|
||||
\alias{dim.xgb.DMatrix}
|
||||
\title{Dimensions of xgb.DMatrix}
|
||||
\usage{
|
||||
\method{dim}{xgb.DMatrix}(x)
|
||||
}
|
||||
\arguments{
|
||||
\item{x}{Object of class \code{xgb.DMatrix}}
|
||||
}
|
||||
\description{
|
||||
Returns a vector of numbers of rows and of columns in an \code{xgb.DMatrix}.
|
||||
}
|
||||
\details{
|
||||
Note: since \code{nrow} and \code{ncol} internally use \code{dim}, they can also
|
||||
be directly used with an \code{xgb.DMatrix} object.
|
||||
}
|
||||
\examples{
|
||||
data(agaricus.train, package='xgboost')
|
||||
train <- agaricus.train
|
||||
dtrain <- xgb.DMatrix(train$data, label=train$label)
|
||||
|
||||
stopifnot(nrow(dtrain) == nrow(train$data))
|
||||
stopifnot(ncol(dtrain) == ncol(train$data))
|
||||
stopifnot(all(dim(dtrain) == dim(train$data)))
|
||||
|
||||
}
|
||||
|
||||
36
R-package/man/dimnames.xgb.DMatrix.Rd
Normal file
36
R-package/man/dimnames.xgb.DMatrix.Rd
Normal file
@ -0,0 +1,36 @@
|
||||
% Generated by roxygen2: do not edit by hand
|
||||
% Please edit documentation in R/xgb.DMatrix.R
|
||||
\name{dimnames.xgb.DMatrix}
|
||||
\alias{dimnames.xgb.DMatrix}
|
||||
\alias{dimnames<-.xgb.DMatrix}
|
||||
\title{Handling of column names of \code{xgb.DMatrix}}
|
||||
\usage{
|
||||
\method{dimnames}{xgb.DMatrix}(x)
|
||||
|
||||
\method{dimnames}{xgb.DMatrix}(x) <- value
|
||||
}
|
||||
\arguments{
|
||||
\item{x}{object of class \code{xgb.DMatrix}}
|
||||
|
||||
\item{value}{a list of two elements: the first one is ignored
|
||||
and the second one is column names}
|
||||
}
|
||||
\description{
|
||||
Only column names are supported for \code{xgb.DMatrix}, thus setting of
|
||||
row names would have no effect and returnten row names would be NULL.
|
||||
}
|
||||
\details{
|
||||
Generic \code{dimnames} methods are used by \code{colnames}.
|
||||
Since row names are irrelevant, it is recommended to use \code{colnames} directly.
|
||||
}
|
||||
\examples{
|
||||
data(agaricus.train, package='xgboost')
|
||||
train <- agaricus.train
|
||||
dtrain <- xgb.DMatrix(train$data, label=train$label)
|
||||
dimnames(dtrain)
|
||||
colnames(dtrain)
|
||||
colnames(dtrain) <- make.names(1:ncol(train$data))
|
||||
print(dtrain, verbose=TRUE)
|
||||
|
||||
}
|
||||
|
||||
@ -1,27 +1,26 @@
|
||||
% Generated by roxygen2: do not edit by hand
|
||||
% Please edit documentation in R/getinfo.xgb.DMatrix.R
|
||||
\docType{methods}
|
||||
% Please edit documentation in R/xgb.DMatrix.R
|
||||
\name{getinfo}
|
||||
\alias{getinfo}
|
||||
\alias{getinfo,xgb.DMatrix-method}
|
||||
\alias{getinfo.xgb.DMatrix}
|
||||
\title{Get information of an xgb.DMatrix object}
|
||||
\usage{
|
||||
getinfo(object, ...)
|
||||
|
||||
\S4method{getinfo}{xgb.DMatrix}(object, name)
|
||||
\method{getinfo}{xgb.DMatrix}(object, name)
|
||||
}
|
||||
\arguments{
|
||||
\item{object}{Object of class \code{xgb.DMatrix}}
|
||||
|
||||
\item{...}{other parameters}
|
||||
|
||||
\item{name}{the name of the field to get}
|
||||
\item{name}{the name of the information field to get (see details)}
|
||||
}
|
||||
\description{
|
||||
Get information of an xgb.DMatrix object
|
||||
}
|
||||
\details{
|
||||
The information can be one of the following:
|
||||
The \code{name} field can be one of the following:
|
||||
|
||||
\itemize{
|
||||
\item \code{label}: label Xgboost learn from ;
|
||||
@ -34,8 +33,10 @@ The information can be one of the following:
|
||||
data(agaricus.train, package='xgboost')
|
||||
train <- agaricus.train
|
||||
dtrain <- xgb.DMatrix(train$data, label=train$label)
|
||||
|
||||
labels <- getinfo(dtrain, 'label')
|
||||
setinfo(dtrain, 'label', 1-labels)
|
||||
|
||||
labels2 <- getinfo(dtrain, 'label')
|
||||
stopifnot(all(labels2 == 1-labels))
|
||||
}
|
||||
|
||||
@ -1,23 +0,0 @@
|
||||
% Generated by roxygen2: do not edit by hand
|
||||
% Please edit documentation in R/nrow.xgb.DMatrix.R
|
||||
\docType{methods}
|
||||
\name{nrow,xgb.DMatrix-method}
|
||||
\alias{nrow,xgb.DMatrix-method}
|
||||
\title{Number of xgb.DMatrix rows}
|
||||
\usage{
|
||||
\S4method{nrow}{xgb.DMatrix}(x)
|
||||
}
|
||||
\arguments{
|
||||
\item{x}{Object of class \code{xgb.DMatrix}}
|
||||
}
|
||||
\description{
|
||||
\code{nrow} return the number of rows present in the \code{xgb.DMatrix}.
|
||||
}
|
||||
\examples{
|
||||
data(agaricus.train, package='xgboost')
|
||||
train <- agaricus.train
|
||||
dtrain <- xgb.DMatrix(train$data, label=train$label)
|
||||
stopifnot(nrow(dtrain) == nrow(train$data))
|
||||
|
||||
}
|
||||
|
||||
@ -1,18 +0,0 @@
|
||||
% Generated by roxygen2: do not edit by hand
|
||||
% Please edit documentation in R/predict.xgb.Booster.handle.R
|
||||
\docType{methods}
|
||||
\name{predict,xgb.Booster.handle-method}
|
||||
\alias{predict,xgb.Booster.handle-method}
|
||||
\title{Predict method for eXtreme Gradient Boosting model handle}
|
||||
\usage{
|
||||
\S4method{predict}{xgb.Booster.handle}(object, ...)
|
||||
}
|
||||
\arguments{
|
||||
\item{object}{Object of class "xgb.Boost.handle"}
|
||||
|
||||
\item{...}{Parameters pass to \code{predict.xgb.Booster}}
|
||||
}
|
||||
\description{
|
||||
Predicted values based on xgb.Booster.handle object.
|
||||
}
|
||||
|
||||
@ -1,15 +1,17 @@
|
||||
% Generated by roxygen2: do not edit by hand
|
||||
% Please edit documentation in R/predict.xgb.Booster.R
|
||||
\docType{methods}
|
||||
\name{predict,xgb.Booster-method}
|
||||
\alias{predict,xgb.Booster-method}
|
||||
% Please edit documentation in R/xgb.Booster.R
|
||||
\name{predict.xgb.Booster}
|
||||
\alias{predict.xgb.Booster}
|
||||
\alias{predict.xgb.Booster.handle}
|
||||
\title{Predict method for eXtreme Gradient Boosting model}
|
||||
\usage{
|
||||
\S4method{predict}{xgb.Booster}(object, newdata, missing = NA,
|
||||
\method{predict}{xgb.Booster}(object, newdata, missing = NA,
|
||||
outputmargin = FALSE, ntreelimit = NULL, predleaf = FALSE)
|
||||
|
||||
\method{predict}{xgb.Booster.handle}(object, ...)
|
||||
}
|
||||
\arguments{
|
||||
\item{object}{Object of class "xgb.Boost"}
|
||||
\item{object}{Object of class \code{xgb.Booster} or \code{xgb.Booster.handle}}
|
||||
|
||||
\item{newdata}{takes \code{matrix}, \code{dgCMatrix}, local data file or
|
||||
\code{xgb.DMatrix}.}
|
||||
@ -27,9 +29,11 @@ only valid for gbtree, but not for gblinear. set it to be value bigger
|
||||
than 0. It will use all trees by default.}
|
||||
|
||||
\item{predleaf}{whether predict leaf index instead. If set to TRUE, the output will be a matrix object.}
|
||||
|
||||
\item{...}{Parameters pass to \code{predict.xgb.Booster}}
|
||||
}
|
||||
\description{
|
||||
Predicted values based on xgboost model object.
|
||||
Predicted values based on either xgboost model or model handle object.
|
||||
}
|
||||
\details{
|
||||
The option \code{ntreelimit} purpose is to let the user train a model with lots
|
||||
@ -46,6 +50,7 @@ data(agaricus.train, package='xgboost')
|
||||
data(agaricus.test, package='xgboost')
|
||||
train <- agaricus.train
|
||||
test <- agaricus.test
|
||||
|
||||
bst <- xgboost(data = train$data, label = train$label, max.depth = 2,
|
||||
eta = 1, nthread = 2, nround = 2,objective = "binary:logistic")
|
||||
pred <- predict(bst, test$data)
|
||||
28
R-package/man/print.xgb.DMatrix.Rd
Normal file
28
R-package/man/print.xgb.DMatrix.Rd
Normal file
@ -0,0 +1,28 @@
|
||||
% Generated by roxygen2: do not edit by hand
|
||||
% Please edit documentation in R/xgb.DMatrix.R
|
||||
\name{print.xgb.DMatrix}
|
||||
\alias{print.xgb.DMatrix}
|
||||
\title{Print xgb.DMatrix}
|
||||
\usage{
|
||||
print.xgb.DMatrix(x, verbose = FALSE, ...)
|
||||
}
|
||||
\arguments{
|
||||
\item{x}{an xgb.DMatrix object}
|
||||
|
||||
\item{verbose}{whether to print colnames (when present)}
|
||||
|
||||
\item{...}{not currently used}
|
||||
}
|
||||
\description{
|
||||
Print information about xgb.DMatrix.
|
||||
Currently it displays dimensions and presence of info-fields and colnames.
|
||||
}
|
||||
\examples{
|
||||
data(agaricus.train, package='xgboost')
|
||||
train <- agaricus.train
|
||||
dtrain <- xgb.DMatrix(train$data, label=train$label)
|
||||
|
||||
dtrain
|
||||
print(dtrain, verbose=TRUE)
|
||||
}
|
||||
|
||||
@ -1,14 +1,13 @@
|
||||
% Generated by roxygen2: do not edit by hand
|
||||
% Please edit documentation in R/setinfo.xgb.DMatrix.R
|
||||
\docType{methods}
|
||||
% Please edit documentation in R/xgb.DMatrix.R
|
||||
\name{setinfo}
|
||||
\alias{setinfo}
|
||||
\alias{setinfo,xgb.DMatrix-method}
|
||||
\alias{setinfo.xgb.DMatrix}
|
||||
\title{Set information of an xgb.DMatrix object}
|
||||
\usage{
|
||||
setinfo(object, ...)
|
||||
|
||||
\S4method{setinfo}{xgb.DMatrix}(object, name, info)
|
||||
\method{setinfo}{xgb.DMatrix}(object, name, info)
|
||||
}
|
||||
\arguments{
|
||||
\item{object}{Object of class "xgb.DMatrix"}
|
||||
@ -23,7 +22,7 @@ setinfo(object, ...)
|
||||
Set information of an xgb.DMatrix object
|
||||
}
|
||||
\details{
|
||||
It can be one of the following:
|
||||
The \code{name} field can be one of the following:
|
||||
|
||||
\itemize{
|
||||
\item \code{label}: label Xgboost learn from ;
|
||||
@ -36,9 +35,10 @@ It can be one of the following:
|
||||
data(agaricus.train, package='xgboost')
|
||||
train <- agaricus.train
|
||||
dtrain <- xgb.DMatrix(train$data, label=train$label)
|
||||
|
||||
labels <- getinfo(dtrain, 'label')
|
||||
setinfo(dtrain, 'label', 1-labels)
|
||||
labels2 <- getinfo(dtrain, 'label')
|
||||
stopifnot(all(labels2 == 1-labels))
|
||||
stopifnot(all.equal(labels2, 1-labels))
|
||||
}
|
||||
|
||||
|
||||
@ -1,22 +1,26 @@
|
||||
% Generated by roxygen2: do not edit by hand
|
||||
% Please edit documentation in R/slice.xgb.DMatrix.R
|
||||
\docType{methods}
|
||||
% Please edit documentation in R/xgb.DMatrix.R
|
||||
\name{slice}
|
||||
\alias{[.xgb.DMatrix}
|
||||
\alias{slice}
|
||||
\alias{slice,xgb.DMatrix-method}
|
||||
\alias{slice.xgb.DMatrix}
|
||||
\title{Get a new DMatrix containing the specified rows of
|
||||
orginal xgb.DMatrix object}
|
||||
\usage{
|
||||
slice(object, ...)
|
||||
|
||||
\S4method{slice}{xgb.DMatrix}(object, idxset, ...)
|
||||
\method{slice}{xgb.DMatrix}(object, idxset, ...)
|
||||
|
||||
\method{[}{xgb.DMatrix}(object, idxset, colset = NULL)
|
||||
}
|
||||
\arguments{
|
||||
\item{object}{Object of class "xgb.DMatrix"}
|
||||
|
||||
\item{...}{other parameters}
|
||||
\item{...}{other parameters (currently not used)}
|
||||
|
||||
\item{idxset}{a integer vector of indices of rows needed}
|
||||
|
||||
\item{colset}{currently not used (columns subsetting is not available)}
|
||||
}
|
||||
\description{
|
||||
Get a new DMatrix containing the specified rows of
|
||||
@ -26,6 +30,12 @@ orginal xgb.DMatrix object
|
||||
data(agaricus.train, package='xgboost')
|
||||
train <- agaricus.train
|
||||
dtrain <- xgb.DMatrix(train$data, label=train$label)
|
||||
dsub <- slice(dtrain, 1:3)
|
||||
|
||||
dsub <- slice(dtrain, 1:42)
|
||||
labels1 <- getinfo(dsub, 'label')
|
||||
dsub <- dtrain[1:42, ]
|
||||
labels2 <- getinfo(dsub, 'label')
|
||||
all.equal(labels1, labels2)
|
||||
|
||||
}
|
||||
|
||||
@ -7,8 +7,7 @@
|
||||
xgb.DMatrix(data, info = list(), missing = NA, ...)
|
||||
}
|
||||
\arguments{
|
||||
\item{data}{a \code{matrix} object, a \code{dgCMatrix} object or a character
|
||||
indicating the data file.}
|
||||
\item{data}{a \code{matrix} object, a \code{dgCMatrix} object or a character representing a filename}
|
||||
|
||||
\item{info}{a list of information of the xgb.DMatrix object}
|
||||
|
||||
@ -18,7 +17,8 @@ value that represents missing value. Sometime a data use 0 or other extreme valu
|
||||
\item{...}{other information to pass to \code{info}.}
|
||||
}
|
||||
\description{
|
||||
Contruct xgb.DMatrix object from dense matrix, sparse matrix or local file.
|
||||
Contruct xgb.DMatrix object from dense matrix, sparse matrix
|
||||
or local file (that was created previously by saving an \code{xgb.DMatrix}).
|
||||
}
|
||||
\examples{
|
||||
data(agaricus.train, package='xgboost')
|
||||
|
||||
@ -195,6 +195,14 @@ SEXP XGDMatrixNumRow_R(SEXP handle) {
|
||||
return ScalarInteger(static_cast<int>(nrow));
|
||||
}
|
||||
|
||||
SEXP XGDMatrixNumCol_R(SEXP handle) {
|
||||
bst_ulong ncol;
|
||||
R_API_BEGIN();
|
||||
CHECK_CALL(XGDMatrixNumCol(R_ExternalPtrAddr(handle), &ncol));
|
||||
R_API_END();
|
||||
return ScalarInteger(static_cast<int>(ncol));
|
||||
}
|
||||
|
||||
// functions related to booster
|
||||
void _BoosterFinalizer(SEXP ext) {
|
||||
if (R_ExternalPtrAddr(ext) == NULL) return;
|
||||
|
||||
@ -83,10 +83,16 @@ XGB_DLL SEXP XGDMatrixGetInfo_R(SEXP handle, SEXP field);
|
||||
|
||||
/*!
|
||||
* \brief return number of rows
|
||||
* \param handle a instance of data matrix
|
||||
* \param handle an instance of data matrix
|
||||
*/
|
||||
XGB_DLL SEXP XGDMatrixNumRow_R(SEXP handle);
|
||||
|
||||
/*!
|
||||
* \brief return number of columns
|
||||
* \param handle an instance of data matrix
|
||||
*/
|
||||
XGB_DLL SEXP XGDMatrixNumCol_R(SEXP handle);
|
||||
|
||||
/*!
|
||||
* \brief create xgboost learner
|
||||
* \param dmats a list of dmatrix handles that will be cached
|
||||
|
||||
67
R-package/tests/testthat/test_dmatrix.R
Normal file
67
R-package/tests/testthat/test_dmatrix.R
Normal file
@ -0,0 +1,67 @@
|
||||
require(xgboost)
|
||||
|
||||
context("testing xgb.DMatrix functionality")
|
||||
|
||||
data(agaricus.test, package='xgboost')
|
||||
test_data <- agaricus.test$data[1:100,]
|
||||
test_label <- agaricus.test$label[1:100]
|
||||
|
||||
test_that("xgb.DMatrix: basic construction, saving, loading", {
|
||||
# from sparse matrix
|
||||
dtest1 <- xgb.DMatrix(test_data, label=test_label)
|
||||
# from dense matrix
|
||||
dtest2 <- xgb.DMatrix(as.matrix(test_data), label=test_label)
|
||||
expect_equal(getinfo(dtest1, 'label'), getinfo(dtest2, 'label'))
|
||||
|
||||
# save to a local file
|
||||
tmp_file <- tempfile('xgb.DMatrix_')
|
||||
expect_true(xgb.DMatrix.save(dtest1, tmp_file))
|
||||
# read from a local file
|
||||
dtest3 <- xgb.DMatrix(tmp_file)
|
||||
unlink(tmp_file)
|
||||
expect_equal(getinfo(dtest1, 'label'), getinfo(dtest3, 'label'))
|
||||
})
|
||||
|
||||
test_that("xgb.DMatrix: getinfo & setinfo", {
|
||||
dtest <- xgb.DMatrix(test_data)
|
||||
expect_true(setinfo(dtest, 'label', test_label))
|
||||
labels <- getinfo(dtest, 'label')
|
||||
expect_equal(test_label, getinfo(dtest, 'label'))
|
||||
|
||||
expect_true(length(getinfo(dtest, 'weight')) == 0)
|
||||
expect_true(length(getinfo(dtest, 'base_margin')) == 0)
|
||||
|
||||
expect_true(setinfo(dtest, 'weight', test_label))
|
||||
expect_true(setinfo(dtest, 'base_margin', test_label))
|
||||
expect_true(setinfo(dtest, 'group', c(50,50)))
|
||||
expect_error(setinfo(dtest, 'group', test_label))
|
||||
|
||||
# providing character values will give a warning
|
||||
expect_warning( setinfo(dtest, 'weight', rep('a', nrow(test_data))) )
|
||||
|
||||
# any other label should error
|
||||
expect_error(setinfo(dtest, 'asdf', test_label))
|
||||
})
|
||||
|
||||
test_that("xgb.DMatrix: slice, dim", {
|
||||
dtest <- xgb.DMatrix(test_data, label=test_label)
|
||||
expect_equal(dim(dtest), dim(test_data))
|
||||
dsub1 <- slice(dtest, 1:42)
|
||||
expect_equal(nrow(dsub1), 42)
|
||||
expect_equal(ncol(dsub1), ncol(test_data))
|
||||
|
||||
dsub2 <- dtest[1:42,]
|
||||
expect_equal(dim(dtest), dim(test_data))
|
||||
expect_equal(getinfo(dsub1, 'label'), getinfo(dsub2, 'label'))
|
||||
})
|
||||
|
||||
test_that("xgb.DMatrix: colnames", {
|
||||
dtest <- xgb.DMatrix(test_data, label=test_label)
|
||||
expect_equal(colnames(dtest), colnames(test_data))
|
||||
expect_error( colnames(dtest) <- 'asdf')
|
||||
new_names <- make.names(1:ncol(test_data))
|
||||
expect_silent( colnames(dtest) <- new_names)
|
||||
expect_equal(colnames(dtest), new_names)
|
||||
expect_silent(colnames(dtest) <- NULL)
|
||||
expect_null(colnames(dtest))
|
||||
})
|
||||
Loading…
x
Reference in New Issue
Block a user