69 lines
2.8 KiB
R
69 lines
2.8 KiB
R
#' Save xgboost model to text file
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#'
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#' Save a xgboost model to text file. Could be parsed later.
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#'
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#' @importFrom magrittr %>%
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#' @importFrom stringr str_replace
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#' @importFrom data.table fread
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#' @importFrom data.table :=
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#' @importFrom data.table setnames
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#' @param model the model object.
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#' @param fname the name of the text file where to save the model text dump. If not provided or set to \code{NULL} the function will return the model as a \code{character} vector.
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#' @param fmap feature map file representing the type of feature.
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#' Detailed description could be found at
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#' \url{https://github.com/tqchen/xgboost/wiki/Binary-Classification#dump-model}.
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#' See demo/ for walkthrough example in R, and
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#' \url{https://github.com/tqchen/xgboost/blob/master/demo/data/featmap.txt}
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#' for example Format.
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#' @param with.stats whether dump statistics of splits
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#' When this option is on, the model dump comes with two additional statistics:
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#' gain is the approximate loss function gain we get in each split;
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#' cover is the sum of second order gradient in each node.
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#'
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#' @return
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#' if fname is not provided or set to \code{NULL} the function will return the model as a \code{character} vector. Otherwise it will return \code{TRUE}.
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#'
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#' @examples
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#' data(agaricus.train, package='xgboost')
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#' data(agaricus.test, package='xgboost')
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#' train <- agaricus.train
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#' test <- agaricus.test
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#' bst <- xgboost(data = train$data, label = train$label, max.depth = 2,
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#' eta = 1, nround = 2,objective = "binary:logistic")
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#' # save the model in file 'xgb.model.dump'
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#' xgb.dump(bst, 'xgb.model.dump', with.stats = TRUE)
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#'
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#' # print the model without saving it to a file
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#' print(xgb.dump(bst))
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#' @export
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#'
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xgb.dump <- function(model = NULL, fname = NULL, fmap = "", with.stats=FALSE) {
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if (class(model) != "xgb.Booster") {
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stop("model: argument must be type xgb.Booster")
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}
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if (!(class(fname) %in% c("character", "NULL") && length(fname) <= 1)) {
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stop("fname: argument must be type character (when provided)")
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}
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if (!(class(fmap) %in% c("character", "NULL") && length(fname) <= 1)) {
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stop("fmap: argument must be type character (when provided)")
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}
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longString <- .Call("XGBoosterDumpModel_R", model, fmap, as.integer(with.stats), PACKAGE = "xgboost")
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dt <- fread(paste(longString, collapse = ""), sep = "\n", header = F)
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setnames(dt, "Lines")
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if(is.null(fname)) {
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result <- dt[Lines != "0"][, Lines := str_replace(Lines, "^\t+", "")][Lines != ""][, paste(Lines)]
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return(result)
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} else {
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result <- dt[Lines != "0"][Lines != ""][, paste(Lines)] %>% writeLines(fname)
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return(TRUE)
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}
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}
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# Avoid error messages during CRAN check.
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# The reason is that these variables are never declared
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# They are mainly column names inferred by Data.table...
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globalVariables(c("Lines", ".")) |