215 lines
6.5 KiB
R
215 lines
6.5 KiB
R
#' @importClassesFrom Matrix dgCMatrix dgeMatrix
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#' @import methods
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# depends on matrix
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.onLoad <- function(libname, pkgname) {
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library.dynam("xgboost", pkgname, libname)
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}
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.onUnload <- function(libpath) {
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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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.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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.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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.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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.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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stop("xgb.Booster: modelfile must be character")
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}
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.Call("XGBoosterLoadModel_R", handle, modelfile, PACKAGE = "xgboost")
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}
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return(structure(handle, class = "xgb.Booster"))
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}
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## ----the following are low level iteratively function, 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) {
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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)
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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 {
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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") {
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stop("xgb.iter.update: first argument must be type xgb.Booster")
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}
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if (class(dtrain) != "xgb.DMatrix") {
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stop("xgb.iter.update: second argument must be type xgb.DMatrix")
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}
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.Call("XGBoosterBoostOneIter_R", booster, dtrain, gpair$grad, gpair$hess,
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PACKAGE = "xgboost")
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return(TRUE)
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}
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# iteratively update booster with dtrain
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xgb.iter.update <- function(booster, dtrain, iter, obj = NULL) {
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if (class(booster) != "xgb.Booster") {
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stop("xgb.iter.update: first argument must be type xgb.Booster")
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}
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if (class(dtrain) != "xgb.DMatrix") {
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stop("xgb.iter.update: second argument must be type xgb.DMatrix")
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}
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if (is.null(obj)) {
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.Call("XGBoosterUpdateOneIter_R", booster, as.integer(iter), dtrain,
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PACKAGE = "xgboost")
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} else {
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pred <- predict(booster, dtrain)
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gpair <- obj(pred, dtrain)
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succ <- xgb.iter.boost(booster, dtrain, gpair)
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}
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return(TRUE)
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}
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# iteratively evaluate one iteration
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xgb.iter.eval <- function(booster, watchlist, iter, feval = NULL) {
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if (class(booster) != "xgb.Booster") {
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stop("xgb.eval: first argument must be type xgb.Booster")
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}
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if (typeof(watchlist) != "list") {
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stop("xgb.eval: only accepts list of DMatrix as watchlist")
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}
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for (w in watchlist) {
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if (class(w) != "xgb.DMatrix") {
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stop("xgb.eval: watch list can only contain xgb.DMatrix")
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}
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}
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if (length(watchlist) != 0) {
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if (is.null(feval)) {
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evnames <- list()
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for (i in 1:length(watchlist)) {
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w <- watchlist[i]
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if (length(names(w)) == 0) {
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stop("xgb.eval: name tag must be presented for every elements in watchlist")
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}
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evnames <- append(evnames, names(w))
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}
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msg <- .Call("XGBoosterEvalOneIter_R", booster, as.integer(iter), watchlist,
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evnames, PACKAGE = "xgboost")
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} else {
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msg <- paste("[", iter, "]", sep="")
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for (j in 1:length(watchlist)) {
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w <- watchlist[j]
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if (length(names(w)) == 0) {
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stop("xgb.eval: name tag must be presented for every elements in watchlist")
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}
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ret <- feval(predict(booster, w[[1]]), w[[1]])
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msg <- paste(msg, "\t", names(w), "-", ret$metric, ":", ret$value, sep="")
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}
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}
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} else {
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msg <- ""
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}
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return(msg)
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}
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#------------------------------------------
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# helper functions for cross validation
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#
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xgb.cv.mknfold <- function(dall, nfold, param) {
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randidx <- sample(1 : xgb.numrow(dall))
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kstep <- length(randidx) / nfold
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idset <- list()
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for (i in 1:nfold) {
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idset[[i]] <- randidx[ ((i-1) * kstep + 1) : min(i * kstep, length(randidx)) ]
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}
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ret <- list()
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for (k in 1:nfold) {
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dtest <- slice(dall, idset[[k]])
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didx = c()
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for (i in 1:nfold) {
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if (i != k) {
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didx <- append(didx, idset[[i]])
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}
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}
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dtrain <- slice(dall, didx)
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bst <- xgb.Booster(param, list(dtrain, dtest))
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watchlist = list(train=dtrain, test=dtest)
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ret[[k]] <- list(dtrain=dtrain, booster=bst, watchlist=watchlist)
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}
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return (ret)
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}
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xgb.cv.aggcv <- function(res, showsd = TRUE) {
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header <- res[[1]]
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ret <- header[1]
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for (i in 2:length(header)) {
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kv <- strsplit(header[i], ":")[[1]]
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ret <- paste(ret, "\t", kv[1], ":", sep="")
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stats <- c()
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stats[1] <- as.numeric(kv[2])
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for (j in 2:length(res)) {
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tkv <- strsplit(res[[j]][i], ":")[[1]]
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stats[j] <- as.numeric(tkv[2])
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}
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ret <- paste(ret, sprintf("%f", mean(stats)), sep="")
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if (showsd) {
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ret <- paste(ret, sprintf("+%f", sd(stats)), sep="")
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}
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}
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return (ret)
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}
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