R-callbacks refactor
This commit is contained in:
Vadim Khotilovich
@@ -1,83 +1,28 @@
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#' eXtreme Gradient Boosting (Tree) library
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#'
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#' A simple interface for training xgboost model. Look at \code{\link{xgb.train}} function for a more advanced interface.
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#'
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#' @param data takes \code{matrix}, \code{dgCMatrix}, local data file or
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#' \code{xgb.DMatrix}.
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#' @param label the response variable. User should not set this field,
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#' if data is local data file or \code{xgb.DMatrix}.
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#' @param params the list of parameters.
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#'
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#' 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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#' Look at \code{\link{xgb.train}} for a more complete list of parameters or \url{https://github.com/dmlc/xgboost/wiki/Parameters} for the full list.
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#'
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#' See also \code{demo/} for walkthrough example in R.
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#'
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#' @param nrounds the max number of iterations
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#' @param verbose If 0, xgboost will stay silent. If 1, xgboost will print
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#' information of performance. If 2, xgboost will print information of both
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#' performance and construction progress information
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#' @param print.every.n Print every N progress messages when \code{verbose>0}. Default is 1 which means all messages are printed.
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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. Sometimes a data use 0 or other extreme value to represents missing values.
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#' @param weight a vector indicating the weight for each row of the input.
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#' @param early.stop.round If \code{NULL}, the early stopping function is not triggered.
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#' If set to an integer \code{k}, training with a validation set will stop if the performance
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#' keeps getting worse consecutively for \code{k} rounds.
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#' @param maximize If \code{feval} and \code{early.stop.round} are set, then \code{maximize} must be set as well.
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#' \code{maximize=TRUE} means the larger the evaluation score the better.
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#' @param save_period save the model to the disk in every \code{save_period} rounds, 0 means no such action.
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#' @param save_name the name or path for periodically saved model file.
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#' @param ... other parameters to pass to \code{params}.
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#'
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#' @details
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#' This is the modeling function for Xgboost.
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#'
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#' Parallelization is automatically enabled if \code{OpenMP} is present.
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#'
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#' Number of threads can also be manually specified via \code{nthread} parameter.
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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, nround = 2, objective = "binary:logistic")
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#' pred <- predict(bst, test$data)
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#'
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# Simple interface for training an xgboost model.
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# Its documentation is combined with xgb.train.
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#
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#' @rdname xgb.train
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#' @export
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xgboost <- function(data = NULL, label = NULL, missing = NA, weight = NULL,
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params = list(), nrounds,
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verbose = 1, print.every.n = 1L, early.stop.round = NULL,
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maximize = NULL, save_period = 0, save_name = "xgboost.model", ...) {
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verbose = 1, print.every.n = 1L,
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early.stop.round = NULL, maximize = NULL,
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save_period = 0, save_name = "xgboost.model",
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xgb_model = NULL, callbacks = list(), ...) {
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dtrain <- xgb.get.DMatrix(data, label, missing, weight)
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params <- append(params, list(...))
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if (verbose > 0) {
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watchlist <- list(train = dtrain)
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} else {
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watchlist <- list()
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}
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watchlist <- list()
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if (verbose > 0)
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watchlist$train = dtrain
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bst <- xgb.train(params, dtrain, nrounds, watchlist, verbose = verbose, print.every.n=print.every.n,
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early.stop.round = early.stop.round, maximize = maximize,
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save_period = save_period, save_name = save_name)
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save_period = save_period, save_name = save_name,
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xgb_model = xgb_model, callbacks = callbacks, ...)
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return(bst)
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}
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#' Training part from Mushroom Data Set
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#'
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#' This data set is originally from the Mushroom data set,
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@@ -131,3 +76,18 @@ NULL
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#' @format A list containing a label vector, and a dgCMatrix object with 1611
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#' rows and 126 variables
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NULL
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# Various imports
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#' @importClassesFrom Matrix dgCMatrix dgeMatrix
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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
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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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#' @import methods
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#' @useDynLib xgboost
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NULL
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