rename arguments to be dot-seperated

R-package/R/xgb.cv.R

@@ -54,12 +54,11 @@
 #' @param folds \code{list} provides a possibility of using a list of pre-defined CV folds (each element must be a vector of fold's indices).
 #'   If folds are supplied, the nfold and stratified parameters would be ignored.
 #' @param verbose \code{boolean}, print the statistics during the process
-#' @param printEveryN Print every N progress messages when \code{verbose>0}. Default is 1 which means all messages are printed.
-#' @param early_stop_round If \code{NULL}, the early stopping function is not triggered. 
+#' @param print.every.n Print every N progress messages when \code{verbose>0}. Default is 1 which means all messages are printed.
+#' @param early.stop.round If \code{NULL}, the early stopping function is not triggered. 
 #'     If set to an integer \code{k}, training with a validation set will stop if the performance 
 #'     keeps getting worse consecutively for \code{k} rounds.
-#' @param early.stop.round An alternative of \code{early_stop_round}.
-#' @param maximize If \code{feval} and \code{early_stop_round} are set, then \code{maximize} must be set as well.
+#' @param maximize If \code{feval} and \code{early.stop.round} are set, then \code{maximize} must be set as well.
 #'     \code{maximize=TRUE} means the larger the evaluation score the better.
 #'     
 #' @param ... other parameters to pass to \code{params}.
@@ -94,8 +93,8 @@
 #'
 xgb.cv <- function(params=list(), data, nrounds, nfold, label = NULL, missing = NULL, 
                    prediction = FALSE, showsd = TRUE, metrics=list(), 
-                   obj = NULL, feval = NULL, stratified = TRUE, folds = NULL, verbose = T, printEveryN=1L,
-                   early_stop_round = NULL, early.stop.round = NULL, maximize = NULL, ...) {
+                   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")
   }
@@ -136,9 +135,7 @@ xgb.cv <- function(params=list(), data, nrounds, nfold, label = NULL, missing =
     }
   
   # Early Stopping
-  if (is.null(early_stop_round) && !is.null(early.stop.round))
-    early_stop_round = early.stop.round
-  if (!is.null(early_stop_round)){
+  if (!is.null(early.stop.round)){
     if (!is.null(feval) && is.null(maximize))
       stop('Please set maximize to note whether the model is maximizing the evaluation or not.')
     if (is.null(maximize) && is.null(params$eval_metric))
@@ -178,7 +175,7 @@ xgb.cv <- function(params=list(), data, nrounds, nfold, label = NULL, missing =
   else
     predictValues <- rep(0,xgb.numrow(dtrain))
   history <- c()
-  printEveryN = max(as.integer(printEveryN), 1L)
+  print.every.n = max(as.integer(print.every.n), 1L)
   for (i in 1:nrounds) {
     msg <- list()
     for (k in 1:nfold) {
@@ -204,11 +201,11 @@ xgb.cv <- function(params=list(), data, nrounds, nfold, label = NULL, missing =
     ret <- xgb.cv.aggcv(msg, showsd)
     history <- c(history, ret)
     if(verbose)
-      if (0==(i-1L)%%printEveryN)
+      if (0==(i-1L)%%print.every.n)
         cat(ret, "\n", sep="")
     
     # early_Stopping
-    if (!is.null(early_stop_round)){
+    if (!is.null(early.stop.round)){
       score = strsplit(ret,'\\s+')[[1]][1+length(metrics)+1]
       score = strsplit(score,'\\+|:')[[1]][[2]]
       score = as.numeric(score)
@@ -216,7 +213,7 @@ xgb.cv <- function(params=list(), data, nrounds, nfold, label = NULL, missing =
         bestScore = score
         bestInd = i
       } else {
-        if (i-bestInd>=early_stop_round) {
+        if (i-bestInd>=early.stop.round) {
           earlyStopflag = TRUE
           cat('Stopping. Best iteration:',bestInd)
           break

R-package/R/xgb.train.R

@@ -66,12 +66,11 @@
 #'   prediction and dtrain,
 #' @param verbose If 0, xgboost will stay silent. If 1, xgboost will print 
 #'   information of performance. If 2, xgboost will print information of both
-#' @param printEveryN Print every N progress messages when \code{verbose>0}. Default is 1 which means all messages are printed.
-#' @param early_stop_round If \code{NULL}, the early stopping function is not triggered. 
+#' @param print.every.n Print every N progress messages when \code{verbose>0}. Default is 1 which means all messages are printed.
+#' @param early.stop.round If \code{NULL}, the early stopping function is not triggered. 
 #'     If set to an integer \code{k}, training with a validation set will stop if the performance 
 #'     keeps getting worse consecutively for \code{k} rounds.
-#' @param early.stop.round An alternative of \code{early_stop_round}.
-#' @param maximize If \code{feval} and \code{early_stop_round} are set, then \code{maximize} must be set as well.
+#' @param maximize If \code{feval} and \code{early.stop.round} are set, then \code{maximize} must be set as well.
 #'     \code{maximize=TRUE} means the larger the evaluation score the better.
 #' @param ... other parameters to pass to \code{params}.
 #' 
@@ -120,9 +119,8 @@
 #' @export
 #' 
 xgb.train <- function(params=list(), data, nrounds, watchlist = list(), 
-                      obj = NULL, feval = NULL, verbose = 1, printEveryN=1L,
-                      early_stop_round = NULL, early.stop.round = NULL,
-                      maximize = NULL, ...) {
+                      obj = NULL, feval = NULL, verbose = 1, print.every.n=1L,
+                      early.stop.round = NULL, maximize = NULL, ...) {
   dtrain <- data
   if (typeof(params) != "list") {
     stop("xgb.train: first argument params must be list")
@@ -157,9 +155,7 @@ xgb.train <- function(params=list(), data, nrounds, watchlist = list(),
     }
     
   # Early stopping
-  if (is.null(early_stop_round) && !is.null(early.stop.round))
-    early_stop_round = early.stop.round
-  if (!is.null(early_stop_round)){
+  if (!is.null(early.stop.round)){
     if (!is.null(feval) && is.null(maximize))
       stop('Please set maximize to note whether the model is maximizing the evaluation or not.')
     if (length(watchlist) == 0)
@@ -190,14 +186,14 @@ xgb.train <- function(params=list(), data, nrounds, watchlist = list(),
   
   handle <- xgb.Booster(params, append(watchlist, dtrain))
   bst <- xgb.handleToBooster(handle)
-  printEveryN=max( as.integer(printEveryN), 1L)
+  print.every.n=max( as.integer(print.every.n), 1L)
   for (i in 1:nrounds) {
     succ <- xgb.iter.update(bst$handle, dtrain, i - 1, obj)
     if (length(watchlist) != 0) {
       msg <- xgb.iter.eval(bst$handle, watchlist, i - 1, feval)
-      if (0== ( (i-1) %% printEveryN))
+      if (0== ( (i-1) %% print.every.n))
 	    cat(paste(msg, "\n", sep=""))
-      if (!is.null(early_stop_round))
+      if (!is.null(early.stop.round))
       {
         score = strsplit(msg,':|\\s+')[[1]][3]
         score = as.numeric(score)
@@ -205,7 +201,7 @@ xgb.train <- function(params=list(), data, nrounds, watchlist = list(),
           bestScore = score
           bestInd = i
         } else {
-          if (i-bestInd>=early_stop_round) {
+          if (i-bestInd>=early.stop.round) {
             earlyStopflag = TRUE
             cat('Stopping. Best iteration:',bestInd)
             break
@@ -215,7 +211,7 @@ xgb.train <- function(params=list(), data, nrounds, watchlist = list(),
     }
   }
   bst <- xgb.Booster.check(bst)
-  if (!is.null(early_stop_round)) {
+  if (!is.null(early.stop.round)) {
     bst$bestScore = bestScore
     bst$bestInd = bestInd
   }

R-package/R/xgboost.R

@@ -28,14 +28,13 @@
 #' @param verbose If 0, xgboost will stay silent. If 1, xgboost will print 
 #'   information of performance. If 2, xgboost will print information of both
 #'   performance and construction progress information
-#' @param printEveryN Print every N progress messages when \code{verbose>0}. Default is 1 which means all messages are printed.
+#' @param print.every.n Print every N progress messages when \code{verbose>0}. Default is 1 which means all messages are printed.
 #' @param missing Missing is only used when input is dense matrix, pick a float 
 #'     value that represents missing value. Sometimes a data use 0 or other extreme value to represents missing values.
-#' @param early_stop_round If \code{NULL}, the early stopping function is not triggered. 
+#' @param early.stop.round If \code{NULL}, the early stopping function is not triggered. 
 #'     If set to an integer \code{k}, training with a validation set will stop if the performance 
 #'     keeps getting worse consecutively for \code{k} rounds.
-#' @param early.stop.round An alternative of \code{early_stop_round}.
-#' @param maximize If \code{feval} and \code{early_stop_round} are set, then \code{maximize} must be set as well.
+#' @param maximize If \code{feval} and \code{early.stop.round} are set, then \code{maximize} must be set as well.
 #'     \code{maximize=TRUE} means the larger the evaluation score the better.
 #' @param ... other parameters to pass to \code{params}.
 #' 
@@ -58,7 +57,7 @@
 #' @export
 #' 
 xgboost <- function(data = NULL, label = NULL, missing = NULL, params = list(), nrounds, 
-                    verbose = 1, printEveryN=1L, early_stop_round = NULL, early.stop.round = NULL,
+                    verbose = 1, print.every.n = 1L, early.stop.round = NULL,
                     maximize = NULL, ...) {
   if (is.null(missing)) {
     dtrain <- xgb.get.DMatrix(data, label)
@@ -74,8 +73,7 @@ xgboost <- function(data = NULL, label = NULL, missing = NULL, params = list(),
     watchlist <- list()
   }
   
-  bst <- xgb.train(params, dtrain, nrounds, watchlist, verbose = verbose, printEveryN=printEveryN,
-                   early_stop_round = early_stop_round,
+  bst <- xgb.train(params, dtrain, nrounds, watchlist, verbose = verbose, print.every.n=print.every.n,
                    early.stop.round = early.stop.round)
   
   return(bst)

R-package/man/xgb.cv.Rd

@@ -7,8 +7,8 @@
 xgb.cv(params = list(), data, nrounds, nfold, label = NULL,
   missing = NULL, prediction = FALSE, showsd = TRUE, metrics = list(),
   obj = NULL, feval = NULL, stratified = TRUE, folds = NULL,
-  verbose = T, printEveryN = 1L, early_stop_round = NULL,
-  early.stop.round = NULL, maximize = NULL, ...)
+  verbose = T, print.every.n = 1L, early.stop.round = NULL,
+  maximize = NULL, ...)
 }
 \arguments{
 \item{params}{the list of parameters. Commonly used ones are:
@@ -66,15 +66,13 @@ If folds are supplied, the nfold and stratified parameters would be ignored.}
 
 \item{verbose}{\code{boolean}, print the statistics during the process}
 
-\item{printEveryN}{Print every N progress messages when \code{verbose>0}. Default is 1 which means all messages are printed.}
+\item{print.every.n}{Print every N progress messages when \code{verbose>0}. Default is 1 which means all messages are printed.}
 
-\item{early_stop_round}{If \code{NULL}, the early stopping function is not triggered.
+\item{early.stop.round}{If \code{NULL}, the early stopping function is not triggered.
 If set to an integer \code{k}, training with a validation set will stop if the performance
 keeps getting worse consecutively for \code{k} rounds.}
 
-\item{early.stop.round}{An alternative of \code{early_stop_round}.}
-
-\item{maximize}{If \code{feval} and \code{early_stop_round} are set, then \code{maximize} must be set as well.
+\item{maximize}{If \code{feval} and \code{early.stop.round} are set, then \code{maximize} must be set as well.
     \code{maximize=TRUE} means the larger the evaluation score the better.}
 
 \item{...}{other parameters to pass to \code{params}.}

R-package/man/xgb.train.Rd

@@ -5,7 +5,7 @@
 \title{eXtreme Gradient Boosting Training}
 \usage{
 xgb.train(params = list(), data, nrounds, watchlist = list(), obj = NULL,
-  feval = NULL, verbose = 1, printEveryN = 1L, early_stop_round = NULL,
+  feval = NULL, verbose = 1, print.every.n = 1L,
   early.stop.round = NULL, maximize = NULL, ...)
 }
 \arguments{
@@ -78,15 +78,13 @@ prediction and dtrain,}
 \item{verbose}{If 0, xgboost will stay silent. If 1, xgboost will print
 information of performance. If 2, xgboost will print information of both}
 
-\item{printEveryN}{Print every N progress messages when \code{verbose>0}. Default is 1 which means all messages are printed.}
+\item{print.every.n}{Print every N progress messages when \code{verbose>0}. Default is 1 which means all messages are printed.}
 
-\item{early_stop_round}{If \code{NULL}, the early stopping function is not triggered.
+\item{early.stop.round}{If \code{NULL}, the early stopping function is not triggered.
 If set to an integer \code{k}, training with a validation set will stop if the performance
 keeps getting worse consecutively for \code{k} rounds.}
 
-\item{early.stop.round}{An alternative of \code{early_stop_round}.}
-
-\item{maximize}{If \code{feval} and \code{early_stop_round} are set, then \code{maximize} must be set as well.
+\item{maximize}{If \code{feval} and \code{early.stop.round} are set, then \code{maximize} must be set as well.
 \code{maximize=TRUE} means the larger the evaluation score the better.}
 
 \item{...}{other parameters to pass to \code{params}.}

R-package/man/xgboost.Rd

@@ -5,8 +5,8 @@
 \title{eXtreme Gradient Boosting (Tree) library}
 \usage{
 xgboost(data = NULL, label = NULL, missing = NULL, params = list(),
-  nrounds, verbose = 1, printEveryN = 1L, early_stop_round = NULL,
-  early.stop.round = NULL, maximize = NULL, ...)
+  nrounds, verbose = 1, print.every.n = 1L, early.stop.round = NULL,
+  maximize = NULL, ...)
 }
 \arguments{
 \item{data}{takes \code{matrix}, \code{dgCMatrix}, local data file or
@@ -42,15 +42,13 @@ Commonly used ones are:
 information of performance. If 2, xgboost will print information of both
 performance and construction progress information}
 
-\item{printEveryN}{Print every N progress messages when \code{verbose>0}. Default is 1 which means all messages are printed.}
+\item{print.every.n}{Print every N progress messages when \code{verbose>0}. Default is 1 which means all messages are printed.}
 
-\item{early_stop_round}{If \code{NULL}, the early stopping function is not triggered.
+\item{early.stop.round}{If \code{NULL}, the early stopping function is not triggered.
 If set to an integer \code{k}, training with a validation set will stop if the performance
 keeps getting worse consecutively for \code{k} rounds.}
 
-\item{early.stop.round}{An alternative of \code{early_stop_round}.}
-
-\item{maximize}{If \code{feval} and \code{early_stop_round} are set, then \code{maximize} must be set as well.
+\item{maximize}{If \code{feval} and \code{early.stop.round} are set, then \code{maximize} must be set as well.
 \code{maximize=TRUE} means the larger the evaluation score the better.}
 
 \item{...}{other parameters to pass to \code{params}.}