a375ad2822
* [R] make sure things work for a single split model; fixes #2191 * [R] add option use_int_id to xgb.model.dt.tree * [R] add example of exporting tree plot to a file * [R] set save_period = NULL as default in xgboost() to be the same as in xgb.train; fixes #2182 * [R] it's a good practice after CRAN releases to bump up package version in dev * [R] allow xgb.DMatrix construction from integer dense matrices * [R] xgb.DMatrix: silent parameter; improve documentation * [R] xgb.model.dt.tree code style changes * [R] update NEWS with parameter changes * [R] code safety & style; handle non-strict matrix and inherited classes of input and model; fixes #2242 * [R] change to x.y.z.p R-package versioning scheme and set version to 0.6.4.3 * [R] add an R package versioning section to the contributors guide * [R] R-package/README.md: clean up the redundant old installation instructions, link the contributors guide
72 lines
2.9 KiB
R
72 lines
2.9 KiB
R
#' Dump an xgboost model in text format.
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#'
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#' Dump an xgboost model in text format.
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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.
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#' If not provided or set to \code{NULL}, the model is returned as a \code{character} vector.
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#' @param fmap feature map file representing feature types.
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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 to dump some additional statistics about the splits.
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#' When this option is on, the model dump contains two additional values:
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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_format 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
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#' 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, fname = NULL, fmap = "", with_stats=FALSE,
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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 (!inherits(model, "xgb.Booster"))
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stop("model: argument must be of type xgb.Booster")
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if (!(is.null(fname) || is.character(fname)))
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stop("fname: argument must be a character string (when provided)")
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if (!(is.null(fmap) || is.character(fmap)))
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stop("fmap: argument must be a character string (when provided)")
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model <- xgb.Booster.complete(model)
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model_dump <- .Call("XGBoosterDumpModel_R", model$handle, NVL(fmap, "")[1], as.integer(with_stats),
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as.character(dump_format), PACKAGE = "xgboost")
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if (is.null(fname))
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model_dump <- stri_replace_all_regex(model_dump, '\t', '')
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if (dump_format == "text")
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model_dump <- unlist(stri_split_regex(model_dump, '\n'))
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model_dump <- grep('^\\s*$', 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[1])
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return(TRUE)
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}
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}
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