223 lines
8.1 KiB
R
223 lines
8.1 KiB
R
# 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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# constructing DMatrix
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xgb.DMatrix <- function(data, info=list(), missing=0.0) {
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if (typeof(data) == "character") {
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handle <- .Call("XGDMatrixCreateFromFile_R", data, as.integer(FALSE), PACKAGE="xgboost")
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} else if(is.matrix(data)) {
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handle <- .Call("XGDMatrixCreateFromMat_R", data, missing, PACKAGE="xgboost")
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} else if(class(data) == "dgCMatrix") {
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handle <- .Call("XGDMatrixCreateFromCSC_R", data@p, data@i, data@x, PACKAGE="xgboost")
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} else {
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stop(paste("xgb.DMatrix: does not support to construct from ", typeof(data)))
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}
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dmat <- structure(handle, class="xgb.DMatrix")
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if (length(info) != 0) {
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for (i in 1:length(info)) {
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p <- info[i]
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xgb.setinfo(dmat, names(p), p[[1]])
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}
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}
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return(dmat)
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}
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# get information from dmatrix
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xgb.getinfo <- function(dmat, 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(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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name != "weight" &&
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name != "base_margin" ) {
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stop(paste("xgb.getinfo: unknown info name", name))
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}
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ret <- .Call("XGDMatrixGetInfo_R", dmat, name, PACKAGE="xgboost")
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return(ret)
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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), 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), 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), 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), PACKAGE="xgboost")
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return(TRUE)
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}
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stop(pase("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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.Call("XGBoosterSetParam_R", handle, "seed", "0", 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, names(p), as.character(p), 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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# train a model using given parameters
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xgb.train <- function(params, dtrain, nrounds=10, watchlist=list(), obj=NULL, feval=NULL) {
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if (typeof(params) != "list") {
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stop("xgb.train: first argument params must be list");
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}
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if (class(dtrain) != "xgb.DMatrix") {
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stop("xgb.train: second argument dtrain must be xgb.DMatrix");
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}
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bst <- xgb.Booster(params, append(watchlist,dtrain))
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for (i in 1:nrounds) {
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if (is.null(obj)) {
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succ <- xgb.iter.update(bst, dtrain, i-1)
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} else {
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pred <- xgb.predict(bst, dtrain)
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gpair <- obj(pred, dtrain)
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succ <- xgb.iter.boost(bst, dtrain, gpair)
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}
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if (length(watchlist) != 0) {
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if (is.null(feval)) {
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msg <- xgb.iter.eval(bst, watchlist, i-1)
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cat(msg); cat("\n")
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} else {
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cat("["); cat(i); cat("]");
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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(xgb.predict(bst, w[[1]]), w[[1]])
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cat("\t"); cat(names(w)); cat("-"); cat(ret$metric);
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cat(":"); cat(ret$value)
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}
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cat("\n")
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}
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}
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}
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return(bst)
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}
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# save model or DMatrix to file
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xgb.save <- function(handle, fname) {
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if (typeof(fname) != "character") {
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stop("xgb.save: fname must be character")
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}
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if (class(handle) == "xgb.Booster") {
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.Call("XGBoosterSaveModel_R", handle, fname, PACKAGE="xgboost")
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return(TRUE)
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}
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if (class(handle) == "xgb.DMatrix") {
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.Call("XGDMatrixSaveBinary_R", handle, fname, as.integer(FALSE), PACKAGE="xgboost")
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return(TRUE)
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}
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stop("xgb.save: the input must be either xgb.DMatrix or xgb.Booster")
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return(FALSE)
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}
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# predict
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xgb.predict <- function(booster, dmat, outputmargin = FALSE) {
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if (class(booster) != "xgb.Booster") {
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stop("xgb.predict: first argument must be type xgb.Booster")
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}
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if (class(dmat) != "xgb.DMatrix") {
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stop("xgb.predict: second argument must be type xgb.DMatrix")
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}
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ret <- .Call("XGBoosterPredict_R", booster, dmat, as.integer(outputmargin), PACKAGE="xgboost")
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return(ret)
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}
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# dump model
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xgb.dump <- function(booster, fname, fmap = "") {
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if (class(booster) != "xgb.Booster") {
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stop("xgb.dump: first argument must be type xgb.Booster")
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}
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if (typeof(fname) != "character"){
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stop("xgb.dump: second argument must be type character")
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}
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.Call("XGBoosterDumpModel_R", booster, fname, fmap, PACKAGE="xgboost")
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return(TRUE)
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}
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##--------------------------------------
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# the following are low level iteratively function, not needed
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# if you do not want to use them
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#---------------------------------------
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# iteratively update booster with dtrain
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xgb.iter.update <- function(booster, dtrain, iter) {
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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("XGBoosterUpdateOneIter_R", booster, as.integer(iter), dtrain, PACKAGE="xgboost")
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return(TRUE)
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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, PACKAGE="xgboost")
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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) {
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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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evnames <- list()
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if (length(watchlist) != 0) {
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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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}
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msg <- .Call("XGBoosterEvalOneIter_R", booster, as.integer(iter), watchlist, evnames, PACKAGE="xgboost")
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return(msg)
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}
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