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Fixed most of the lint issues

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This commit is contained in:
terrytangyuan
2015-10-28 23:24:17 -04:00
parent 8bae715994
commit 6024480400
13 changed files with 107 additions and 119 deletions
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22
R-package/R/utils.R
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@@ -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")
}
@@ -122,7 +122,7 @@ xgb.get.DMatrix <- function(data, label = NULL, missing = NA, weight = NULL) {
} else if (inClass == "xgb.DMatrix") {
dtrain <- data
} else if (inClass == "data.frame") {
stop("xgboost only support numerical matrix input,
stop("xgboost only support numerical matrix input,
use 'data.frame' to transform the data.")
} else {
stop("xgboost: Invalid input of data")
@@ -156,12 +156,10 @@ xgb.iter.update <- function(booster, dtrain, iter, obj = NULL) {
}
if (is.null(obj)) {
.Call("XGBoosterUpdateOneIter_R", booster, as.integer(iter), dtrain,
.Call("XGBoosterUpdateOneIter_R", booster, as.integer(iter), dtrain,
PACKAGE = "xgboost")
} else {
pred <- predict(booster, dtrain)
gpair <- obj(pred, dtrain)
succ <- xgb.iter.boost(booster, dtrain, gpair)
}
return(TRUE)
}
@@ -189,9 +187,9 @@ xgb.iter.eval <- function(booster, watchlist, iter, feval = NULL, prediction = F
}
evnames <- append(evnames, names(w))
}
msg <- .Call("XGBoosterEvalOneIter_R", booster, as.integer(iter), watchlist,
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]
@@ -247,7 +245,7 @@ xgb.cv.mknfold <- function(dall, nfold, param, stratified, folds) {
if (length(unique(y)) <= 5) y <- factor(y)
}
folds <- xgb.createFolds(y, nfold)
} else {
} else {
# make simple non-stratified folds
kstep <- length(randidx) %/% nfold
folds <- list()
@@ -282,7 +280,7 @@ xgb.cv.aggcv <- function(res, showsd = TRUE) {
kv <- strsplit(header[i], ":")[[1]]
ret <- paste(ret, "\t", kv[1], ":", sep="")
stats <- c()
stats[1] <- as.numeric(kv[2])
stats[1] <- as.numeric(kv[2])
for (j in 2:length(res)) {
tkv <- strsplit(res[[j]][i], ":")[[1]]
stats[j] <- as.numeric(tkv[2])
@@ -311,8 +309,8 @@ xgb.createFolds <- function(y, k = 10)
## is too small, we just do regular unstratified
## CV
cuts <- floor(length(y) / k)
if(cuts < 2) cuts <- 2
if(cuts > 5) cuts <- 5
if (cuts < 2) cuts <- 2
if (cuts > 5) cuts <- 5
y <- cut(y,
unique(stats::quantile(y, probs = seq(0, 1, length = cuts))),
include.lowest = TRUE)
@@ -324,7 +322,7 @@ xgb.createFolds <- function(y, k = 10)
y <- factor(as.character(y))
numInClass <- table(y)
foldVector <- vector(mode = "integer", length(y))
## For each class, balance the fold allocation as far
## as possible, then resample the remainder.
## The final assignment of folds is also randomized.