Hendrik/xgboost
1
0
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity

Add a new verbose parameter to print progress during the process (set to true by default to not change behavior of existing code) + source code refactoring

Browse Source
This commit is contained in:
El Potaeto 2015-01-02 11:21:53 +01:00
parent 4d0d65837d
commit cdea1685e5
3 changed files with 18 additions and 18 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

1
R-package/NAMESPACE
View File

@ -25,5 +25,4 @@ importFrom(stringr,str_extract)
importFrom(stringr,str_extract_all)
importFrom(stringr,str_match)
importFrom(stringr,str_replace)
importFrom(stringr,str_replace_all)
importFrom(stringr,str_split)

26
R-package/R/xgb.cv.R
View File

@ -8,8 +8,8 @@
#' @importFrom data.table :=
#' @importFrom data.table rbindlist
#' @importFrom stringr str_extract_all
#' @importFrom stringr str_extract
#' @importFrom stringr str_split
#' @importFrom stringr str_replace_all
#' @importFrom stringr str_replace
#' @importFrom stringr str_match
#'
@ -31,7 +31,7 @@
#' @param nrounds the max number of iterations
#' @param nfold number of folds used
#' @param label option field, when data is Matrix
#' @param showsd boolean, whether show standard deviation of cross validation
#' @param showsd \code{boolean}, whether show standard deviation of cross validation
#' @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:
@ -49,9 +49,10 @@
#' prediction and dtrain,
#' @param missing Missing is only used when input is dense matrix, pick a float
# value that represents missing value. Sometime a data use 0 or other extreme value to represents missing values.
#' @param verbose \code{boolean}, print the statistics during the process.
#' @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 A \code{data.table} with each mean and standard deviation stat for training set and test set.
#'
#' @details
#' This is the cross validation function for xgboost
@ -66,10 +67,11 @@
#' dtrain <- xgb.DMatrix(agaricus.train$data, label = agaricus.train$label)
#' history <- xgb.cv(data = dtrain, nround=3, nfold = 5, metrics=list("rmse","auc"),
#' "max.depth"=3, "eta"=1, "objective"="binary:logistic")
#' print(history)
#' @export
#'
xgb.cv <- function(params=list(), data, nrounds, nfold, label = NULL, missing = NULL,
showsd = TRUE, metrics=list(), obj = NULL, feval = NULL, ...) {
showsd = TRUE, metrics=list(), obj = NULL, feval = NULL, verbose = T,...) {
if (typeof(params) != "list") {
stop("xgb.cv: first argument params must be list")
}
@ -94,28 +96,24 @@ xgb.cv <- function(params=list(), data, nrounds, nfold, label = NULL, missing =
for (k in 1:nfold) {
fd <- folds[[k]]
succ <- xgb.iter.update(fd$booster, fd$dtrain, i - 1, obj)
msg[[k]] <- strsplit(xgb.iter.eval(fd$booster, fd$watchlist, i - 1, feval),
"\t")[[1]]
msg[[k]] <- xgb.iter.eval(fd$booster, fd$watchlist, i - 1, feval) %>% str_split("\t") %>% .[[1]]
}
ret <- xgb.cv.aggcv(msg, showsd)
history <- c(history, ret)
cat(paste(ret, "\n", sep=""))
if(verbose) paste(ret, "\n", sep="") %>% cat
}
colnames <- str_split(string = history[1], pattern = "\t")[[1]] %>% .[2:length(.)] %>% str_extract(".*:") %>% str_replace(":","") %>% str_replace_all("-", ".")
colnames <- str_split(string = history[1], pattern = "\t")[[1]] %>% .[2:length(.)] %>% str_extract(".*:") %>% str_replace(":","") %>% str_replace("-", ".")
colnamesMean <- paste(colnames, "mean")
colnamesStd <- paste(colnames, "std")
colnames <- c()
for(i in 1:length(colnamesMean)) colnames <- c(colnames, colnamesMean[i], colnamesStd[i])
type <- rep(x = "numeric", times = length(colnames))
dt <- read.table(text = "", colClasses = type, col.names = colnames) %>% as.data.table
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.list %>% {vec <- .; rbindlist(list(dt, vec), use.names = F, fill = F)}
dt
}

9
R-package/man/xgb.cv.Rd
View File

@ -6,7 +6,7 @@
\usage{
xgb.cv(params = list(), data, nrounds, nfold, label = NULL,
missing = NULL, showsd = TRUE, metrics = list(), obj = NULL,
feval = NULL, ...)
feval = NULL, verbose = T, ...)
}
\arguments{
\item{params}{the list of parameters. Commonly used ones are:
@ -34,7 +34,7 @@ xgb.cv(params = list(), data, nrounds, nfold, label = NULL,
\item{missing}{Missing is only used when input is dense matrix, pick a float}
\item{showsd}{boolean, whether show standard deviation of cross validation}
\item{showsd}{\code{boolean}, whether show standard deviation of cross validation}
\item{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.
@ -54,10 +54,12 @@ gradient with given prediction and dtrain,}
\code{list(metric='metric-name', value='metric-value')} with given
prediction and dtrain,}
\item{verbose}{\code{boolean}, print the statistics during the process.}
\item{...}{other parameters to pass to \code{params}.}
}
\value{
a \code{data.table} with each mean and standard deviation stat for training set and test set.
A \code{data.table} with each mean and standard deviation stat for training set and test set.
}
\description{
The cross valudation function of xgboost
@ -75,5 +77,6 @@ data(agaricus.train, package='xgboost')
dtrain <- xgb.DMatrix(agaricus.train$data, label = agaricus.train$label)
history <- xgb.cv(data = dtrain, nround=3, nfold = 5, metrics=list("rmse","auc"),
"max.depth"=3, "eta"=1, "objective"="binary:logistic")
print(history)
}