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change doc and demo for new obj feval interface

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hetong007 2015-05-25 11:30:04 -07:00
parent 19b24cf978
commit 8d3a7e1688
5 changed files with 17 additions and 15 deletions
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8
R-package/R/xgb.train.R
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@ -36,7 +36,7 @@
#' 3. Task Parameters
#'
#' \itemize{
#' \item \code{objective} specify the learning task and the corresponding learning objective, and the objective options are below:
#' \item \code{objective} specify the learning task and the corresponding learning objective, users can pass a self-defined function to it. The default objective options are below:
#' \itemize{
#' \item \code{reg:linear} linear regression (Default).
#' \item \code{reg:logistic} logistic regression.
@ -48,7 +48,7 @@
#' \item \code{rank:pairwise} set xgboost to do ranking task by minimizing the pairwise loss.
#' }
#' \item \code{base_score} the initial prediction score of all instances, global bias. Default: 0.5
#' \item \code{eval_metric} evaluation metrics for validation data. Default: metric will be assigned according to objective(rmse for regression, and error for classification, mean average precision for ranking). List is provided in detail section.
#' \item \code{eval_metric} evaluation metrics for validation data. Users can pass a self-defined function to it. Default: metric will be assigned according to objective(rmse for regression, and error for classification, mean average precision for ranking). List is provided in detail section.
#' }
#'
#' @param data takes an \code{xgb.DMatrix} as the input.
@ -103,7 +103,6 @@
#' dtrain <- xgb.DMatrix(agaricus.train$data, label = agaricus.train$label)
#' dtest <- dtrain
#' watchlist <- list(eval = dtest, train = dtrain)
#' param <- list(max.depth = 2, eta = 1, silent = 1)
#' logregobj <- function(preds, dtrain) {
#' labels <- getinfo(dtrain, "label")
#' preds <- 1/(1 + exp(-preds))
@ -116,7 +115,8 @@
#' err <- as.numeric(sum(labels != (preds > 0)))/length(labels)
#' return(list(metric = "error", value = err))
#' }
#' bst <- xgb.train(param, dtrain, nthread = 2, nround = 2, watchlist, logregobj, evalerror)
#' param <- list(max.depth = 2, eta = 1, silent = 1, objective=logregobj,eval_metric=evalerror)
#' bst <- xgb.train(param, dtrain, nthread = 2, nround = 2, watchlist)
#' @export
#'
xgb.train <- function(params=list(), data, nrounds, watchlist = list(),

6
R-package/demo/custom_objective.R
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@ -8,7 +8,6 @@ dtest <- xgb.DMatrix(agaricus.test$data, label = agaricus.test$label)
# note: for customized objective function, we leave objective as default
# note: what we are getting is margin value in prediction
# you must know what you are doing
param <- list(max.depth=2,eta=1,nthread = 2, silent=1)
watchlist <- list(eval = dtest, train = dtrain)
num_round <- 2
@ -33,10 +32,13 @@ evalerror <- function(preds, dtrain) {
err <- as.numeric(sum(labels != (preds > 0)))/length(labels)
return(list(metric = "error", value = err))
}
param <- list(max.depth=2,eta=1,nthread = 2, silent=1,
objective=logregobj, eval_metric=evalerror)
print ('start training with user customized objective')
# training with customized objective, we can also do step by step training
# simply look at xgboost.py's implementation of train
bst <- xgb.train(param, dtrain, num_round, watchlist, logregobj, evalerror)
bst <- xgb.train(param, dtrain, num_round, watchlist)
#
# there can be cases where you want additional information

4
R-package/man/xgb.cv.Rd
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@ -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, printEveryN = 1L, early_stop_round = NULL,
early.stop.round = NULL, maximize = NULL, ...)
}
\arguments{
\item{params}{the list of parameters. Commonly used ones are:

8
R-package/man/xgb.train.Rd
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@ -43,7 +43,7 @@ xgb.train(params = list(), data, nrounds, watchlist = list(), obj = NULL,
3. Task Parameters
\itemize{
\item \code{objective} specify the learning task and the corresponding learning objective, and the objective options are below:
\item \code{objective} specify the learning task and the corresponding learning objective, users can pass a self-defined function to it. The default objective options are below:
\itemize{
\item \code{reg:linear} linear regression (Default).
\item \code{reg:logistic} logistic regression.
@ -55,7 +55,7 @@ xgb.train(params = list(), data, nrounds, watchlist = list(), obj = NULL,
\item \code{rank:pairwise} set xgboost to do ranking task by minimizing the pairwise loss.
}
\item \code{base_score} the initial prediction score of all instances, global bias. Default: 0.5
\item \code{eval_metric} evaluation metrics for validation data. Default: metric will be assigned according to objective(rmse for regression, and error for classification, mean average precision for ranking). List is provided in detail section.
\item \code{eval_metric} evaluation metrics for validation data. Users can pass a self-defined function to it. Default: metric will be assigned according to objective(rmse for regression, and error for classification, mean average precision for ranking). List is provided in detail section.
}}
\item{data}{takes an \code{xgb.DMatrix} as the input.}
@ -122,7 +122,6 @@ data(agaricus.train, package='xgboost')
dtrain <- xgb.DMatrix(agaricus.train$data, label = agaricus.train$label)
dtest <- dtrain
watchlist <- list(eval = dtest, train = dtrain)
param <- list(max.depth = 2, eta = 1, silent = 1)
logregobj <- function(preds, dtrain) {
labels <- getinfo(dtrain, "label")
preds <- 1/(1 + exp(-preds))
@ -135,6 +134,7 @@ evalerror <- function(preds, dtrain) {
err <- as.numeric(sum(labels != (preds > 0)))/length(labels)
return(list(metric = "error", value = err))
}
bst <- xgb.train(param, dtrain, nthread = 2, nround = 2, watchlist, logregobj, evalerror)
param <- list(max.depth = 2, eta = 1, silent = 1, objective=logregobj,eval_metric=evalerror)
bst <- xgb.train(param, dtrain, nthread = 2, nround = 2, watchlist)
}

4
R-package/man/xgboost.Rd
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@ -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, printEveryN = 1L, early_stop_round = NULL,
early.stop.round = NULL, maximize = NULL, ...)
}
\arguments{
\item{data}{takes \code{matrix}, \code{dgCMatrix}, local data file or