134 lines
5.6 KiB
R
134 lines
5.6 KiB
R
#' Cross Validation
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#'
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#' The cross valudation function of xgboost
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#'
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#' @importFrom data.table data.table
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#' @importFrom data.table as.data.table
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#' @importFrom magrittr %>%
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#' @importFrom data.table :=
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#' @importFrom data.table rbindlist
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#' @importFrom stringr str_extract_all
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#' @importFrom stringr str_extract
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#' @importFrom stringr str_split
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#' @importFrom stringr str_replace
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#' @importFrom stringr str_match
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#'
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#' @param params the list of parameters. Commonly used ones are:
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#' \itemize{
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#' \item \code{objective} objective function, common ones are
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#' \itemize{
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#' \item \code{reg:linear} linear regression
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#' \item \code{binary:logistic} logistic regression for classification
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#' }
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#' \item \code{eta} step size of each boosting step
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#' \item \code{max.depth} maximum depth of the tree
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#' \item \code{nthread} number of thread used in training, if not set, all threads are used
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#' }
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#'
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#' See \url{https://github.com/tqchen/xgboost/wiki/Parameters} for
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#' further details. See also demo/ for walkthrough example in R.
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#' @param data takes an \code{xgb.DMatrix} as the input.
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#' @param nrounds the max number of iterations
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#' @param nfold number of folds used
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#' @param label option field, when data is Matrix
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#' @param missing Missing is only used when input is dense matrix, pick a float
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#' value that represents missing value. Sometime a data use 0 or other extreme value to represents missing values.
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#' @param prediction A logical value indicating whether to return the prediction vector.
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#' @param showsd \code{boolean}, whether show standard deviation of cross validation
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#' @param metrics, list of evaluation metrics to be used in corss validation,
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#' when it is not specified, the evaluation metric is chosen according to objective function.
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#' Possible options are:
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#' \itemize{
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#' \item \code{error} binary classification error rate
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#' \item \code{rmse} Rooted mean square error
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#' \item \code{logloss} negative log-likelihood function
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#' \item \code{auc} Area under curve
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#' \item \code{merror} Exact matching error, used to evaluate multi-class classification
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#' }
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#' @param obj customized objective function. Returns gradient and second order
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#' gradient with given prediction and dtrain,
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#' @param feval custimized evaluation function. Returns
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#' \code{list(metric='metric-name', value='metric-value')} with given
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#' prediction and dtrain,
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#' @param missing Missing is only used when input is dense matrix, pick a float
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# value that represents missing value. Sometime a data use 0 or other extreme value to represents missing values.
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#' @param verbose \code{boolean}, print the statistics during the process.
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#' @param ... other parameters to pass to \code{params}.
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#'
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#' @return A \code{data.table} with each mean and standard deviation stat for training set and test set.
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#'
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#' @details
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#' This is the cross validation function for xgboost
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#'
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#' Parallelization is automatically enabled if OpenMP is present.
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#' Number of threads can also be manually specified via "nthread" parameter.
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#'
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#' This function only accepts an \code{xgb.DMatrix} object as the input.
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#'
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#' @examples
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#' data(agaricus.train, package='xgboost')
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#' dtrain <- xgb.DMatrix(agaricus.train$data, label = agaricus.train$label)
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#' history <- xgb.cv(data = dtrain, nround=3, nfold = 5, metrics=list("rmse","auc"),
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#' "max.depth"=3, "eta"=1, "objective"="binary:logistic")
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#' print(history)
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#' @export
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#'
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xgb.cv <- function(params=list(), data, nrounds, nfold, label = NULL, missing = NULL,
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prediction = FALSE, showsd = TRUE, metrics=list(),
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obj = NULL, feval = NULL, verbose = T,...) {
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if (typeof(params) != "list") {
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stop("xgb.cv: first argument params must be list")
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}
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if (nfold <= 1) {
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stop("nfold must be bigger than 1")
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}
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if (is.null(missing)) {
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dtrain <- xgb.get.DMatrix(data, label)
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} else {
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dtrain <- xgb.get.DMatrix(data, label, missing)
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}
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params <- append(params, list(...))
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params <- append(params, list(silent=1))
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for (mc in metrics) {
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params <- append(params, list("eval_metric"=mc))
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}
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folds <- xgb.cv.mknfold(dtrain, nfold, params)
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predictValues <- rep(0,xgb.numrow(dtrain))
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history <- c()
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for (i in 1:nrounds) {
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msg <- list()
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for (k in 1:nfold) {
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fd <- folds[[k]]
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succ <- xgb.iter.update(fd$booster, fd$dtrain, i - 1, obj)
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if (!prediction){
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msg[[k]] <- xgb.iter.eval(fd$booster, fd$watchlist, i - 1, feval) %>% str_split("\t") %>% .[[1]]
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} else {
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res <- xgb.iter.eval(fd$booster, fd$watchlist, i - 1, feval, prediction)
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predictValues[fd$index] <- res[[2]]
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msg[[k]] <- res[[1]] %>% str_split("\t") %>% .[[1]]
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}
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}
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ret <- xgb.cv.aggcv(msg, showsd)
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history <- c(history, ret)
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if(verbose) paste(ret, "\n", sep="") %>% cat
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}
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colnames <- str_split(string = history[1], pattern = "\t")[[1]] %>% .[2:length(.)] %>% str_extract(".*:") %>% str_replace(":","") %>% str_replace("-", ".")
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colnamesMean <- paste(colnames, "mean")
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colnamesStd <- paste(colnames, "std")
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colnames <- c()
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for(i in 1:length(colnamesMean)) colnames <- c(colnames, colnamesMean[i], colnamesStd[i])
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type <- rep(x = "numeric", times = length(colnames))
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dt <- read.table(text = "", colClasses = type, col.names = colnames) %>% as.data.table
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split <- str_split(string = history, pattern = "\t")
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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)}
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if (prediction) {
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return(list(dt = dt,pred = predictValues))
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}
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return(dt)
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} |