[R-package] various fixes for R CMD check (#1328)

* [R] fix xgb.create.features

* [R] fixes for R CMD check

R-package/R/xgb.Booster.R

@@ -178,7 +178,7 @@ xgb.Booster.check <- function(bst, saveraw = TRUE) {
 #' @rdname predict.xgb.Booster
 #' @export
 predict.xgb.Booster <- function(object, newdata, missing = NA,
-    outputmargin = FALSE, ntreelimit = NULL, predleaf = FALSE, reshape = FALSE) {
+    outputmargin = FALSE, ntreelimit = NULL, predleaf = FALSE, reshape = FALSE, ...) {
 
   object <- xgb.Booster.check(object, saveraw = FALSE)
   if (class(newdata) != "xgb.DMatrix")
@@ -245,7 +245,7 @@ predict.xgb.Booster.handle <- function(object, ...) {
 #' and its serialization is handled extrnally.
 #' Also, setting an attribute that has the same name as one of xgboost's parameters wouldn't 
 #' change the value of that parameter for a model. 
-#' Use \code{\link{`xgb.parameters<-`}} to set or change model parameters.
+#' Use \code{\link{xgb.parameters<-}} to set or change model parameters.
 #' 
 #' The attribute setters would usually work more efficiently for \code{xgb.Booster.handle}
 #' than for \code{xgb.Booster}, since only just a handle (pointer) would need to be copied.
@@ -413,7 +413,8 @@ xgb.ntree <- function(bst) {
 #' 
 #' print(bst)
 #' print(bst, verbose=TRUE)
-#' 
+#'
+#' @method print xgb.Booster 
 #' @export
 print.xgb.Booster <- function(x, verbose=FALSE, ...) {
   cat('##### xgb.Booster\n')

R-package/R/xgb.DMatrix.R

@@ -186,7 +186,7 @@ getinfo <- function(object, ...) UseMethod("getinfo")
 
 #' @rdname getinfo
 #' @export
-getinfo.xgb.DMatrix <- function(object, name) {
+getinfo.xgb.DMatrix <- function(object, name, ...) {
   if (typeof(name) != "character" ||
       length(name) != 1 ||
       !name %in% c('label', 'weight', 'base_margin', 'nrow')) {
@@ -211,7 +211,7 @@ getinfo.xgb.DMatrix <- function(object, name) {
 #' @param name the name of the field to get
 #' @param info the specific field of information to set
 #' @param ... other parameters
-#' 
+#'
 #' @details
 #' The \code{name} field can be one of the following:
 #' 
@@ -237,7 +237,7 @@ setinfo <- function(object, ...) UseMethod("setinfo")
 
 #' @rdname setinfo
 #' @export
-setinfo.xgb.DMatrix <- function(object, name, info) {
+setinfo.xgb.DMatrix <- function(object, name, info, ...) {
   if (name == "label") {
     if (length(info) != nrow(object))
       stop("The length of labels must equal to the number of rows in the input data")
@@ -341,6 +341,8 @@ slice.xgb.DMatrix <- function(object, idxset, ...) {
 #' 
 #' dtrain
 #' print(dtrain, verbose=TRUE)
+#' 
+#' @method print xgb.DMatrix
 #' @export
 print.xgb.DMatrix <- function(x, verbose=FALSE, ...) {
   cat('xgb.DMatrix  dim:', nrow(x), 'x', ncol(x), ' info: ')

R-package/R/xgb.create.features.R

@@ -14,7 +14,7 @@
 #' \strong{Practical Lessons from Predicting Clicks on Ads at Facebook}
 #' 
 #' \emph{(Xinran He, Junfeng Pan, Ou Jin, Tianbing Xu, Bo Liu, Tao Xu, Yan, xin Shi, Antoine Atallah, Ralf Herbrich, Stuart Bowers, 
-#' Joaquin Quiñonero Candela)}
+#' Joaquin Quinonero Candela)}
 #'  
 #' International Workshop on Data Mining for Online Advertising (ADKDD) - August 24, 2014
 #' 
@@ -22,7 +22,7 @@
 #' 
 #' Extract explaining the method:
 #' 
-#' "\emph{We found that boosted decision trees are a powerful and very
+#' "We found that boosted decision trees are a powerful and very
 #' convenient way to implement non-linear and tuple transformations
 #' of the kind we just described. We treat each individual
 #' tree as a categorical feature that takes as value the
@@ -43,7 +43,7 @@
 #' based transformation as a supervised feature encoding that
 #' converts a real-valued vector into a compact binary-valued
 #' vector. A traversal from root node to a leaf node represents
-#' a rule on certain features.}"
+#' a rule on certain features."
 #' 
 #' @examples
 #' data(agaricus.train, package='xgboost')
@@ -78,12 +78,7 @@
 #' @export
 xgb.create.features <- function(model, data, ...){
   check.deprecation(...)
-  pred_with_leaf = predict(model, data, predleaf = TRUE)
-  cols <- list()
-  for(i in 1:length(trees)){
-    # max is not the real max but it s not important for the purpose of adding features
-    leaf_id <- sort(unique(pred_with_leaf[,i]))
-    cols[[i]] <- factor(x = pred_with_leaf[,i], level = leaf_id)
-  }
-  cBind(data, sparse.model.matrix( ~ . -1, as.data.frame(cols)))
+  pred_with_leaf <- predict(model, data, predleaf = TRUE)
+  cols <- lapply(as.data.frame(pred_with_leaf), factor)
+  cBind(data, sparse.model.matrix( ~ . -1, cols))
 }

R-package/R/xgb.cv.R

@@ -171,7 +171,7 @@ xgb.cv <- function(params=list(), data, nrounds, nfold, label = NULL, missing =
   # CV-predictions callback
   if (prediction &&
       !has.callbacks(callbacks, 'cb.cv.predict')) {
-    callbacks <- add.cb(callbacks, cb.cv.predict(save_model=FALSE))
+    callbacks <- add.cb(callbacks, cb.cv.predict(save_models=FALSE))
   }
   # Sort the callbacks into categories
   cb <- categorize.callbacks(callbacks)
@@ -253,6 +253,7 @@ xgb.cv <- function(params=list(), data, nrounds, nfold, label = NULL, missing =
 #' print(cv, verbose=TRUE)
 #' 
 #' @rdname print.xgb.cv
+#' @method print xgb.cv.synchronous
 #' @export
 print.xgb.cv.synchronous <- function(x, verbose=FALSE, ...) {
   cat('##### xgb.cv ', length(x$folds), '-folds\n', sep='')

R-package/R/xgb.importance.R

@@ -103,4 +103,4 @@ xgb.importance <- function(feature_names = NULL, model = NULL, data = NULL, labe
 # Avoid error messages during CRAN check.
 # The reason is that these variables are never declared
 # They are mainly column names inferred by Data.table...
-globalVariables(c(".", "Feature", "Split", "No", "Missing", "MissingNo", "RealCover"))
+globalVariables(c(".", ".N", "Gain", "Frequency", "Feature", "Split", "No", "Missing", "MissingNo", "RealCover"))

R-package/R/xgb.plot.deepness.R

@@ -1,7 +1,8 @@
 #' Plot multiple graphs at the same time
 #' 
 #' Plot multiple graph aligned by rows and columns.
-#' 
+#'
+#' @param ... the plots
 #' @param cols number of columns
 #' @return NULL
 multiplot <- function(..., cols = 1) {
@@ -40,7 +41,7 @@ edge.parser <- function(element) {
 }
 
 #' Extract path from root to leaf from data.table
-#' @param dt.tree data.table containing the nodes and edges of the trees
+#' @param dt_tree data.table containing the nodes and edges of the trees
 get.paths.to.leaf <- function(dt_tree) {
   dt.not.leaf.edges <-
     dt_tree[Feature != "Leaf",.(ID, Yes, Tree)] %>% list(dt_tree[Feature != "Leaf",.(ID, No, Tree)]) %>% rbindlist(use.names = F)
@@ -149,6 +150,6 @@ xgb.plot.deepness <- function(model = NULL) {
 # They are mainly column names inferred by Data.table...
 globalVariables(
   c(
-    "Feature", "Count", "ggplot", "aes", "geom_bar", "xlab", "ylab", "ggtitle", "theme", "element_blank", "element_text", "ID", "Yes", "No", "Tree"
+    ".N", "N", "size", "Feature", "Count", "ggplot", "aes", "geom_bar", "xlab", "ylab", "ggtitle", "theme", "element_blank", "element_text", "ID", "Yes", "No", "Tree"
   )
 )

R-package/R/xgb.plot.multi.trees.R

@@ -103,6 +103,6 @@ xgb.plot.multi.trees <- function(model, feature_names = NULL, features_keep = 5,
 
 globalVariables(
   c(
-    "Feature", "no.nodes.abs.pos", "ID", "Yes", "No", "Tree", "yes.nodes.abs.pos", "abs.node.position"
+    ".N", "N", "From", "To", "Text", "Feature", "no.nodes.abs.pos", "ID", "Yes", "No", "Tree", "yes.nodes.abs.pos", "abs.node.position"
   )
 )

R-package/R/xgboost.R

@@ -95,6 +95,8 @@ NULL
 #' @importFrom stringr str_replace
 #' @importFrom stringr str_replace_all
 #' @importFrom stringr str_split
+#' @importFrom utils object.size str tail
+#' @importFrom stats predict
 #' 
 #' @import methods
 #' @useDynLib xgboost