b21e658a02
* [R-package] JSON tree dump interface * [R-package] precision bugfix in xgb.attributes * [R-package] bugfix for cb.early.stop called from xgb.cv * [R-package] a bit more clarity on labels checking in xgb.cv * [R-package] test JSON dump for gblinear as well * whitespace lint
69 lines
2.9 KiB
R
69 lines
2.9 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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#' @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/dmlc/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/dmlc/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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#' @param dump_fomat either 'text' or 'json' format could be specified.
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#' @param ... currently not used
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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, nthread = 2, nrounds = 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, with_stats = TRUE))
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#'
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#' # print in JSON format:
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#' cat(xgb.dump(bst, with_stats = TRUE, dump_format='json'))
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#'
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#' @export
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xgb.dump <- function(model = NULL, fname = NULL, fmap = "", with_stats=FALSE, dump_format = c("text", "json"), ...) {
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check.deprecation(...)
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dump_format <- match.arg(dump_format)
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if (class(model) != "xgb.Booster")
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stop("model: argument must be of type xgb.Booster")
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if (!(class(fname) %in% c("character", "NULL") && length(fname) <= 1))
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stop("fname: argument must be of type character (when provided)")
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if (!(class(fmap) %in% c("character", "NULL") && length(fmap) <= 1))
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stop("fmap: argument must be of type character (when provided)")
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model <- xgb.Booster.check(model)
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model_dump <- .Call("XGBoosterDumpModel_R", model$handle, fmap, as.integer(with_stats),
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as.character(dump_format), PACKAGE = "xgboost")
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if (is.null(fname))
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model_dump <- stri_replace_all_regex(model_dump, '\t', '')
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if (dump_format == "text")
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model_dump <- unlist(stri_split_regex(model_dump, '\n'))
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model_dump <- grep('^\\s*$', model_dump, invert = TRUE, value = TRUE)
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if (is.null(fname)) {
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return(model_dump)
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} else {
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writeLines(model_dump, fname)
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return(TRUE)
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}
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}
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