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[R] many minor changes to increase the robustness of the R code (#2404)

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* many minor changes to increase robustness of R code

* fixing which mistake in xgb.model.dt.tree.R and a few cosmetics
This commit is contained in:
Bernie Gray
2017-06-15 23:56:23 -04:00
committed by Vadim Khotilovich
parent 0db37c05bd
commit cd7659937b
8 changed files with 23 additions and 23 deletions
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16
R-package/R/utils.R
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@@ -185,7 +185,7 @@ generate.cv.folds <- function(nfold, nrows, stratified, label, params) {
"\tConsider providing pre-computed CV-folds through the 'folds=' parameter.\n")
}
# shuffle
rnd_idx <- sample(1:nrows)
rnd_idx <- sample.int(nrows)
if (stratified &&
length(label) == length(rnd_idx)) {
y <- label[rnd_idx]
@@ -211,9 +211,9 @@ generate.cv.folds <- function(nfold, nrows, stratified, label, params) {
# make simple non-stratified folds
kstep <- length(rnd_idx) %/% nfold
folds <- list()
for (i in 1:(nfold - 1)) {
folds[[i]] <- rnd_idx[1:kstep]
rnd_idx <- rnd_idx[-(1:kstep)]
for (i in seq_len(nfold - 1)) {
folds[[i]] <- rnd_idx[seq_len(kstep)]
rnd_idx <- rnd_idx[-seq_len(kstep)]
}
folds[[nfold]] <- rnd_idx
}
@@ -254,15 +254,15 @@ xgb.createFolds <- function(y, k = 10)
## For each class, balance the fold allocation as far
## as possible, then resample the remainder.
## The final assignment of folds is also randomized.
for (i in 1:length(numInClass)) {
for (i in seq_along(numInClass)) {
## create a vector of integers from 1:k as many times as possible without
## going over the number of samples in the class. Note that if the number
## of samples in a class is less than k, nothing is producd here.
seqVector <- rep(1:k, numInClass[i] %/% k)
seqVector <- rep(seq_len(k), numInClass[i] %/% k)
## add enough random integers to get length(seqVector) == numInClass[i]
if (numInClass[i] %% k > 0) seqVector <- c(seqVector, sample(1:k, numInClass[i] %% k))
if (numInClass[i] %% k > 0) seqVector <- c(seqVector, sample.int(k, numInClass[i] %% k))
## shuffle the integers for fold assignment and assign to this classes's data
foldVector[which(y == dimnames(numInClass)$y[i])] <- sample(seqVector)
foldVector[y == dimnames(numInClass)$y[i]] <- sample(seqVector)
}
} else {
foldVector <- seq(along = y)