stop("lgb.self.get.handle: supplied a too large value in categorical_feature: ",max(private$categorical_feature)," but only ",length(private$colnames)," features")
stop(
"lgb.self.get.handle: supplied a too large value in categorical_feature: "
,max(private$categorical_feature)
," but only "
,length(private$colnames)
," features"
)
}
}
# Store indices as [0, n-1] indexed instead of [1, n] indexed
# Store indices as [0, n-1] indexed instead of [1, n] indexed
#' @param eval evaluation function, can be (list of) character or custom eval function
#' @param eval evaluation function, can be (list of) character or custom eval function
#' @param record Boolean, TRUE will record iteration message to \code{booster$record_evals}
#' @param record Boolean, TRUE will record iteration message to \code{booster$record_evals}
#' @param showsd \code{boolean}, whether to show standard deviation of cross validation
#' @param showsd \code{boolean}, whether to show standard deviation of cross validation
#' @param stratified a \code{boolean} indicating whether sampling of folds should be stratified
#' @param stratified a \code{boolean} indicating whether sampling of folds should be stratified
#' by the values of outcome labels.
#' by the values of outcome labels.
#' @param folds \code{list} provides a possibility to use a list of pre-defined CV folds
#' @param folds \code{list} provides a possibility to use a list of pre-defined CV folds
#' (each element must be a vector of test fold's indices). When folds are supplied,
#' (each element must be a vector of test fold's indices). When folds are supplied,
#' the \code{nfold} and \code{stratified} parameters are ignored.
#' the \code{nfold} and \code{stratified} parameters are ignored.
#' @param colnames feature names, if not null, will use this to overwrite the names in dataset
#' @param colnames feature names, if not null, will use this to overwrite the names in dataset
#' @param categorical_feature list of str or int
#' @param categorical_feature list of str or int
#' type int represents index,
#' type int represents index,
#' type str represents feature names
#' type str represents feature names
#' @param callbacks list of callback functions
#' @param callbacks List of callback functions that are applied at each iteration.
#' List of callback functions that are applied at each iteration.
#' @param reset_data Boolean, setting it to TRUE (not the default value) will transform the booster model
#' @param reset_data Boolean, setting it to TRUE (not the default value) will transform the booster model into a predictor model which frees up memory and the original datasets
#' into a predictor model which frees up memory and the original datasets
#' @param ... other parameters, see Parameters.rst for more information. A few key parameters:
#' @param ... other parameters, see Parameters.rst for more information. A few key parameters:
#' \itemize{
#' \itemize{
#' \item{boosting}{Boosting type. \code{"gbdt"} or \code{"dart"}}
#' \item{boosting}{Boosting type. \code{"gbdt"} or \code{"dart"}}
#' Data preparator for LightGBM datasets (numeric)
#' Data preparator for LightGBM datasets (numeric)
#'
#'
#' Attempts to prepare a clean dataset to prepare to put in a \code{lgb.Dataset}. Factors and characters are converted to numeric without integers. Please use \code{lgb.prepare_rules} if you want to apply this transformation to other datasets.
#' Attempts to prepare a clean dataset to prepare to put in a \code{lgb.Dataset}.
#' Factors and characters are converted to numeric without integers. Please use
#' \code{lgb.prepare_rules} if you want to apply this transformation to other datasets.
#'
#'
#' @param data A data.frame or data.table to prepare.
#' @param data A data.frame or data.table to prepare.
#'
#'
#' @return The cleaned dataset. It must be converted to a matrix format (\code{as.matrix}) for input in \code{lgb.Dataset}.
#' @return The cleaned dataset. It must be converted to a matrix format (\code{as.matrix})
#' Data preparator for LightGBM datasets (integer)
#' Data preparator for LightGBM datasets (integer)
#'
#'
#' Attempts to prepare a clean dataset to prepare to put in a \code{lgb.Dataset}. Factors and characters are converted to numeric (specifically: integer). Please use \code{lgb.prepare_rules2} if you want to apply this transformation to other datasets. This is useful if you have a specific need for integer dataset instead of numeric dataset. Note that there are programs which do not support integer-only input. Consider this as a half memory technique which is dangerous, especially for LightGBM.
#' Attempts to prepare a clean dataset to prepare to put in a \code{lgb.Dataset}.
#' Factors and characters are converted to numeric (specifically: integer).
#' Please use \code{lgb.prepare_rules2} if you want to apply this transformation to other datasets.
#' This is useful if you have a specific need for integer dataset instead of numeric dataset.
#' Note that there are programs which do not support integer-only input. Consider this as a half
#' memory technique which is dangerous, especially for LightGBM.
#'
#'
#' @param data A data.frame or data.table to prepare.
#' @param data A data.frame or data.table to prepare.
#'
#'
#' @return The cleaned dataset. It must be converted to a matrix format (\code{as.matrix}) for input in \code{lgb.Dataset}.
#' @return The cleaned dataset. It must be converted to a matrix format (\code{as.matrix})
#' Data preparator for LightGBM datasets with rules (numeric)
#' Data preparator for LightGBM datasets with rules (numeric)
#'
#'
#' Attempts to prepare a clean dataset to prepare to put in a \code{lgb.Dataset}. Factors and characters are converted to numeric. In addition, keeps rules created so you can convert other datasets using this converter.
#' Attempts to prepare a clean dataset to prepare to put in a \code{lgb.Dataset}.
#' Factors and characters are converted to numeric. In addition, keeps rules created
#' so you can convert other datasets using this converter.
#'
#'
#' @param data A data.frame or data.table to prepare.
#' @param data A data.frame or data.table to prepare.
#' @param rules A set of rules from the data preparator, if already used.
#' @param rules A set of rules from the data preparator, if already used.
#'
#'
#' @return A list with the cleaned dataset (\code{data}) and the rules (\code{rules}). The data must be converted to a matrix format (\code{as.matrix}) for input in \code{lgb.Dataset}.
#' @return A list with the cleaned dataset (\code{data}) and the rules (\code{rules}).
#' The data must be converted to a matrix format (\code{as.matrix}) for input
#' Data preparator for LightGBM datasets with rules (integer)
#' Data preparator for LightGBM datasets with rules (integer)
#'
#'
#' Attempts to prepare a clean dataset to prepare to put in a \code{lgb.Dataset}. Factors and characters are converted to numeric (specifically: integer). In addition, keeps rules created so you can convert other datasets using this converter. This is useful if you have a specific need for integer dataset instead of numeric dataset. Note that there are programs which do not support integer-only input. Consider this as a half memory technique which is dangerous, especially for LightGBM.
#' Attempts to prepare a clean dataset to prepare to put in a \code{lgb.Dataset}.
#' Factors and characters are converted to numeric (specifically: integer).
#' In addition, keeps rules created so you can convert other datasets using this converter.
#' This is useful if you have a specific need for integer dataset instead of numeric dataset.
#' Note that there are programs which do not support integer-only input.
#' Consider this as a half memory technique which is dangerous, especially for LightGBM.
#'
#'
#' @param data A data.frame or data.table to prepare.
#' @param data A data.frame or data.table to prepare.
#' @param rules A set of rules from the data preparator, if already used.
#' @param rules A set of rules from the data preparator, if already used.
#'
#'
#' @return A list with the cleaned dataset (\code{data}) and the rules (\code{rules}). The data must be converted to a matrix format (\code{as.matrix}) for input in \code{lgb.Dataset}.
#' @return A list with the cleaned dataset (\code{data}) and the rules (\code{rules}).
#' The data must be converted to a matrix format (\code{as.matrix}) for input in
#' @param eval evaluation function, can be (a list of) character or custom eval function
#' @param eval evaluation function, can be (a list of) character or custom eval function
#' @param record Boolean, TRUE will record iteration message to \code{booster$record_evals}
#' @param record Boolean, TRUE will record iteration message to \code{booster$record_evals}
#' @param colnames feature names, if not null, will use this to overwrite the names in dataset
#' @param colnames feature names, if not null, will use this to overwrite the names in dataset
#' @param categorical_feature list of str or int
#' @param categorical_feature list of str or int
#' type int represents index,
#' type int represents index,
#' type str represents feature names
#' type str represents feature names
#' @param callbacks list of callback functions
#' @param callbacks List of callback functions that are applied at each iteration.
#' List of callback functions that are applied at each iteration.
#' @param reset_data Boolean, setting it to TRUE (not the default value) will transform the
#' @param reset_data Boolean, setting it to TRUE (not the default value) will transform the booster model into a predictor model which frees up memory and the original datasets
#' booster model into a predictor model which frees up memory and the
#' original datasets
#' @param ... other parameters, see Parameters.rst for more information. A few key parameters:
#' @param ... other parameters, see Parameters.rst for more information. A few key parameters:
#' \itemize{
#' \itemize{
#' \item{boosting}{Boosting type. \code{"gbdt"} or \code{"dart"}}
#' \item{boosting}{Boosting type. \code{"gbdt"} or \code{"dart"}}
#' Attempts to unload LightGBM packages so you can remove objects cleanly without having to restart R. This is useful for instance if an object becomes stuck for no apparent reason and you do not want to restart R to fix the lost object.
#' Attempts to unload LightGBM packages so you can remove objects cleanly without having to restart R. This is useful for instance if an object becomes stuck for no apparent reason and you do not want to restart R to fix the lost object.
#'
#'
#' @param restore Whether to reload \code{LightGBM} immediately after detaching from R. Defaults to \code{TRUE} which means automatically reload \code{LightGBM} once unloading is performed.
#' @param restore Whether to reload \code{LightGBM} immediately after detaching from R.
#' @param wipe Whether to wipe all \code{lgb.Dataset} and \code{lgb.Booster} from the global environment. Defaults to \code{FALSE} which means to not remove them.
#' Defaults to \code{TRUE} which means automatically reload \code{LightGBM} once
#' @param envir The environment to perform wiping on if \code{wipe == TRUE}. Defaults to \code{.GlobalEnv} which is the global environment.
#' unloading is performed.
#' @param wipe Whether to wipe all \code{lgb.Dataset} and \code{lgb.Booster} from the global
#' environment. Defaults to \code{FALSE} which means to not remove them.
#' @param envir The environment to perform wiping on if \code{wipe == TRUE}. Defaults to
#' \code{.GlobalEnv} which is the global environment.
