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force xgb.cv to return numeric performance values instead of character; update its docs

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This commit is contained in:
Vadim Khotilovich 2015-04-03 12:20:34 -05:00
parent b04920d8e7
commit 271e8202a7
2 changed files with 19 additions and 6 deletions
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15
R-package/R/xgb.cv.R
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@ -50,12 +50,19 @@
#' @param feval custimized evaluation function. Returns #' @param feval custimized evaluation function. Returns
#' \code{list(metric='metric-name', value='metric-value')} with given #' \code{list(metric='metric-name', value='metric-value')} with given
#' prediction and dtrain. #' prediction and dtrain.
#' @param stratified \code{boolean}, whether the sampling of folds should be stratified by the values of labels in \code{data} #' @param stratified \code{boolean}, whether sampling of folds should be stratified by the values of labels in \code{data}
#' @param verbose \code{boolean}, print the statistics during the process #' @param verbose \code{boolean}, print the statistics during the process
#' @param ... other parameters to pass to \code{params}. #' @param ... other parameters to pass to \code{params}.
#' #'
#' @return A \code{data.table} with each mean and standard deviation stat for training set and test set. #' @return
#' #' If \code{prediction = TRUE}, a list with the following elements is returned:
#' \itemize{
#' \item \code{dt} a \code{data.table} with each mean and standard deviation stat for training set and test set
#' \item \code{pred} an array or matrix (for multiclass classification) with predictions for each CV-fold for the model having been trained on the data in all other folds.
#' }
#'
#' If \code{prediction = FALSE}, just a \code{data.table} with each mean and standard deviation stat for training set and test set is returned.
#'
#' @details #' @details
#' The original sample is randomly partitioned into \code{nfold} equal size subsamples. #' The original sample is randomly partitioned into \code{nfold} equal size subsamples.
#' #'
@ -148,7 +155,7 @@ xgb.cv <- function(params=list(), data, nrounds, nfold, label = NULL, missing =
dt <- read.table(text = "", colClasses = type, col.names = colnames) %>% as.data.table dt <- read.table(text = "", colClasses = type, col.names = colnames) %>% as.data.table
split <- str_split(string = history, pattern = "\t") split <- str_split(string = history, pattern = "\t")
for(line in split) dt <- line[2:length(line)] %>% str_extract_all(pattern = "\\d*\\.+\\d*") %>% unlist %>% as.list %>% {vec <- .; rbindlist(list(dt, vec), use.names = F, fill = F)} for(line in split) dt <- line[2:length(line)] %>% str_extract_all(pattern = "\\d*\\.+\\d*") %>% unlist %>% as.numeric %>% as.list %>% {rbindlist(list(dt, .), use.names = F, fill = F)}
if (prediction) { if (prediction) {
return(list(dt = dt,pred = predictValues)) return(list(dt = dt,pred = predictValues))

10
R-package/man/xgb.cv.Rd
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@ -57,14 +57,20 @@ gradient with given prediction and dtrain.}
\code{list(metric='metric-name', value='metric-value')} with given \code{list(metric='metric-name', value='metric-value')} with given
prediction and dtrain.} prediction and dtrain.}
\item{stratified}{\code{boolean}, whether the sampling of folds should be stratified by the values of labels in \code{data}} \item{stratified}{\code{boolean}, whether sampling of folds should be stratified by the values of labels in \code{data}}
\item{verbose}{\code{boolean}, print the statistics during the process} \item{verbose}{\code{boolean}, print the statistics during the process}
\item{...}{other parameters to pass to \code{params}.} \item{...}{other parameters to pass to \code{params}.}
} }
\value{ \value{
A \code{data.table} with each mean and standard deviation stat for training set and test set. If \code{prediction = TRUE}, a list with the following elements is returned:
\itemize{
\item \code{dt} a \code{data.table} with each mean and standard deviation stat for training set and test set
\item \code{pred} an array or matrix (for multiclass classification) with predictions for each CV-fold for the model having been trained on the data in all other folds.
}
If \code{prediction = FALSE}, just a \code{data.table} with each mean and standard deviation stat for training set and test set is returned.
} }
\description{ \description{
The cross valudation function of xgboost The cross valudation function of xgboost