Hendrik/xgboost
1
0
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity

Code: Some Lint fixes

Browse Source
This commit is contained in:
terrytangyuan 2015-10-24 16:43:44 -04:00
parent 3abbd7b4c7
commit 537b34dc6f
6 changed files with 29 additions and 32 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

7
R-package/R/predict.xgb.Booster.R
View File

@ -30,8 +30,8 @@ setClass("xgb.Booster",
#' pred <- predict(bst, test$data)
#' @export
#'
setMethod("predict", signature = "xgb.Booster",
definition = function(object, newdata, missing = NA,
setMethod("predict", signature = "xgb.Booster",
definition = function(object, newdata, missing = NA,
outputmargin = FALSE, ntreelimit = NULL, predleaf = FALSE) {
if (class(object) != "xgb.Booster"){
stop("predict: model in prediction must be of class xgb.Booster")
@ -55,7 +55,7 @@ setMethod("predict", signature = "xgb.Booster",
if (predleaf) {
option <- option + 2
}
ret <- .Call("XGBoosterPredict_R", object$handle, newdata, as.integer(option),
ret <- .Call("XGBoosterPredict_R", object$handle, newdata, as.integer(option),
as.integer(ntreelimit), PACKAGE = "xgboost")
if (predleaf){
len <- getinfo(newdata, "nrow")
@ -68,4 +68,3 @@ setMethod("predict", signature = "xgb.Booster",
}
return(ret)
})

3
R-package/R/predict.xgb.Booster.handle.R
View File

@ -13,7 +13,6 @@ setMethod("predict", signature = "xgb.Booster.handle",
bst <- xgb.handleToBooster(object)
ret = predict(bst, ...)
ret <- predict(bst, ...)
return(ret)
})

2
R-package/R/setinfo.xgb.DMatrix.R
View File

@ -32,7 +32,7 @@ setinfo <- function(object, ...){
#' @param ... other parameters
#' @rdname setinfo
#' @method setinfo xgb.DMatrix
setMethod("setinfo", signature = "xgb.DMatrix",
setMethod("setinfo", signature = "xgb.DMatrix",
definition = function(object, name, info) {
xgb.setinfo(object, name, info)
})

6
R-package/R/slice.xgb.DMatrix.R
View File

@ -23,14 +23,14 @@ slice <- function(object, ...){
#' @param ... other parameters
#' @rdname slice
#' @method slice xgb.DMatrix
setMethod("slice", signature = "xgb.DMatrix",
setMethod("slice", signature = "xgb.DMatrix",
definition = function(object, idxset, ...) {
if (class(object) != "xgb.DMatrix") {
stop("slice: first argument dtrain must be xgb.DMatrix")
}
ret <- .Call("XGDMatrixSliceDMatrix_R", object, idxset,
ret <- .Call("XGDMatrixSliceDMatrix_R", object, idxset,
PACKAGE = "xgboost")
attr_list <- attributes(object)
nr <- xgb.numrow(object)
len <- sapply(attr_list,length)

25
R-package/R/utils.R
View File

@ -17,28 +17,28 @@ xgb.setinfo <- function(dmat, name, info) {
if (name == "label") {
if (length(info)!=xgb.numrow(dmat))
stop("The length of labels must equal to the number of rows in the input data")
.Call("XGDMatrixSetInfo_R", dmat, name, as.numeric(info),
.Call("XGDMatrixSetInfo_R", dmat, name, as.numeric(info),
PACKAGE = "xgboost")
return(TRUE)
}
if (name == "weight") {
if (length(info)!=xgb.numrow(dmat))
stop("The length of weights must equal to the number of rows in the input data")
.Call("XGDMatrixSetInfo_R", dmat, name, as.numeric(info),
.Call("XGDMatrixSetInfo_R", dmat, name, as.numeric(info),
PACKAGE = "xgboost")
return(TRUE)
}
if (name == "base_margin") {
# if (length(info)!=xgb.numrow(dmat))
# stop("The length of base margin must equal to the number of rows in the input data")
.Call("XGDMatrixSetInfo_R", dmat, name, as.numeric(info),
.Call("XGDMatrixSetInfo_R", dmat, name, as.numeric(info),
PACKAGE = "xgboost")
return(TRUE)
}
if (name == "group") {
if (sum(info)!=xgb.numrow(dmat))
stop("The sum of groups must equal to the number of rows in the input data")
.Call("XGDMatrixSetInfo_R", dmat, name, as.integer(info),
.Call("XGDMatrixSetInfo_R", dmat, name, as.integer(info),
PACKAGE = "xgboost")
return(TRUE)
}
@ -68,7 +68,7 @@ xgb.Booster <- function(params = list(), cachelist = list(), modelfile = NULL) {
if (typeof(modelfile) == "character") {
.Call("XGBoosterLoadModel_R", handle, modelfile, PACKAGE = "xgboost")
} else if (typeof(modelfile) == "raw") {
.Call("XGBoosterLoadModelFromRaw_R", handle, modelfile, PACKAGE = "xgboost")
.Call("XGBoosterLoadModelFromRaw_R", handle, modelfile, PACKAGE = "xgboost")
} else {
stop("xgb.Booster: modelfile must be character or raw vector")
}
@ -142,8 +142,7 @@ xgb.iter.boost <- function(booster, dtrain, gpair) {
if (class(dtrain) != "xgb.DMatrix") {
stop("xgb.iter.update: second argument must be type xgb.DMatrix")
}
.Call("XGBoosterBoostOneIter_R", booster, dtrain, gpair$grad, gpair$hess,
PACKAGE = "xgboost")
.Call("XGBoosterBoostOneIter_R", booster, dtrain, gpair$grad, gpair$hess, PACKAGE = "xgboost")
return(TRUE)
}
@ -159,7 +158,7 @@ xgb.iter.update <- function(booster, dtrain, iter, obj = NULL) {
if (is.null(obj)) {
.Call("XGBoosterUpdateOneIter_R", booster, as.integer(iter), dtrain,
PACKAGE = "xgboost")
} else {
} else {
pred <- predict(booster, dtrain)
gpair <- obj(pred, dtrain)
succ <- xgb.iter.boost(booster, dtrain, gpair)
@ -192,7 +191,7 @@ xgb.iter.eval <- function(booster, watchlist, iter, feval = NULL, prediction = F
}
msg <- .Call("XGBoosterEvalOneIter_R", booster, as.integer(iter), watchlist,
evnames, PACKAGE = "xgboost")
} else {
} else {
msg <- paste("[", iter, "]", sep="")
for (j in 1:length(watchlist)) {
w <- watchlist[j]
@ -253,10 +252,10 @@ xgb.cv.mknfold <- function(dall, nfold, param, stratified, folds) {
kstep <- length(randidx) %/% nfold
folds <- list()
for (i in 1:(nfold-1)) {
folds[[i]] = randidx[1:kstep]
randidx = setdiff(randidx, folds[[i]])
folds[[i]] <- randidx[1:kstep]
randidx <- setdiff(randidx, folds[[i]])
}
folds[[nfold]] = randidx
folds[[nfold]] <- randidx
}
}
ret <- list()
@ -270,7 +269,7 @@ xgb.cv.mknfold <- function(dall, nfold, param, stratified, folds) {
}
dtrain <- slice(dall, didx)
bst <- xgb.Booster(param, list(dtrain, dtest))
watchlist = list(train=dtrain, test=dtest)
watchlist <- list(train=dtrain, test=dtest)
ret[[k]] <- list(dtrain=dtrain, booster=bst, watchlist=watchlist, index=folds[[k]])
}
return (ret)

18
R-package/R/xgb.cv.R
View File

@ -91,15 +91,15 @@
#' print(history)
#' @export
#'
xgb.cv <- function(params=list(), data, nrounds, nfold, label = NULL, missing = NA,
prediction = FALSE, showsd = TRUE, metrics=list(),
xgb.cv <- function(params=list(), data, nrounds, nfold, label = NULL, missing = NA,
prediction = FALSE, showsd = TRUE, metrics=list(),
obj = NULL, feval = NULL, stratified = TRUE, folds = NULL, verbose = T, print.every.n=1L,
early.stop.round = NULL, maximize = NULL, ...) {
if (typeof(params) != "list") {
stop("xgb.cv: first argument params must be list")
}
if(!is.null(folds)) {
if(class(folds)!="list" | length(folds) < 2) {
if(class(folds) != "list" | length(folds) < 2) {
stop("folds must be a list with 2 or more elements that are vectors of indices for each CV-fold")
}
nfold <- length(folds)
@ -108,22 +108,22 @@ xgb.cv <- function(params=list(), data, nrounds, nfold, label = NULL, missing =
stop("nfold must be bigger than 1")
}
dtrain <- xgb.get.DMatrix(data, label, missing)
dot.params = list(...)
nms.params = names(params)
nms.dot.params = names(dot.params)
if (length(intersect(nms.params,nms.dot.params))>0)
dot.params <- list(...)
nms.params <- names(params)
nms.dot.params <- names(dot.params)
if (length(intersect(nms.params,nms.dot.params)) > 0)
stop("Duplicated defined term in parameters. Please check your list of params.")
params <- append(params, dot.params)
params <- append(params, list(silent=1))
for (mc in metrics) {
params <- append(params, list("eval_metric"=mc))
}
# customized objective and evaluation metric interface
if (!is.null(params$objective) && !is.null(obj))
stop("xgb.cv: cannot assign two different objectives")
if (!is.null(params$objective))
if (class(params$objective)=='function') {
if (class(params$objective) == 'function') {
obj = params$objective
params[['objective']] = NULL
}