style cleanup, incomplete CV

R-package/R/xgb.cv.R

@@ -0,0 +1,57 @@
+#' eXtreme Gradient Boosting Training
+#' 
+#' The training function of xgboost
+#'
+#' @param params the list of parameters. Commonly used ones are:
+#' \itemize{
+#'   \item \code{objective} objective function, common ones are
+#'   \itemize{
+#'     \item \code{reg:linear} linear regression
+#'     \item \code{binary:logistic} logistic regression for classification
+#'   }
+#'   \item \code{eta} step size of each boosting step
+#'   \item \code{max_depth} maximum depth of the tree
+#'   \item \code{nthread} number of thread used in training, if not set, all threads are used
+#' }
+#'
+#'   See \url{https://github.com/tqchen/xgboost/wiki/Parameters} for 
+#'   further details. See also inst/examples/demo.R for walkthrough example in R.
+#' @param data takes an \code{xgb.DMatrix} as the input.
+#' @param nrounds the max number of iterations
+#' @param metrics, list of evaluation metrics to be used in corss validation,
+#'   when it is not specified, the evaluation metric is chosen according to objective function.
+#'   Possible options are:
+#' \itemize{
+#'   \item \code{error} binary classification error rate
+#'   \item \code{rmse} Rooted mean square error
+#'   \item \code{logloss} negative log-likelihood function
+#'   \item \code{auc} Area under curve
+#'   \item \code{merror} Exact matching error, used to evaluate multi-class classification
+#' }
+#'
+#' @param obj customized objective function. Returns gradient and second order 
+#'   gradient with given prediction and dtrain, 
+#' @param feval custimized evaluation function. Returns 
+#'   \code{list(metric='metric-name', value='metric-value')} with given 
+#'   prediction and dtrain,
+#' @param ... other parameters to pass to \code{params}.
+#' 
+#' @details 
+#' This is the cross validation function for xgboost
+#'
+#' Parallelization is automatically enabled if OpenMP is present.
+#' Number of threads can also be manually specified via "nthread" parameter.
+#' 
+#' This function only accepts an \code{xgb.DMatrix} object as the input.
+#'
+#' @export
+#'
+xgb.cv <- function(params=list(), data, nrounds, metrics=list(), label = NULL,
+                   obj = NULL, feval = NULL, ...) {
+  if (typeof(params) != "list") {
+    stop("xgb.cv: first argument params must be list")
+  }
+  dtrain <- xgb.get.DMatrix(data, label)
+  params = append(params, list(...))
+  
+}