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#' @title Create control object for hyperparameter tuning with grid search.
#'
#' @description
#' A basic grid search can handle all kinds of parameter types.
#' You can either use their correct param type and `resolution`,
#' or discretize them yourself by always using [ParamHelpers::makeDiscreteParam]
#' in the `par.set` passed to [tuneParams].
#'
#' @inherit TuneControl
#' @param resolution ([integer])\cr
#' Resolution of the grid for each numeric/integer parameter in `par.set`.
#' For vector parameters, it is the resolution per dimension.
#' Either pass one resolution for all parameters, or a named vector.
#' See [ParamHelpers::generateGridDesign].
#' Default is 10.
#' @param budget (`integer(1)`)\cr
#' Maximum budget for tuning. This value restricts the number of function
#' evaluations. If set, must equal the size of the grid.
#' @return ([TuneControlGrid])
#' @aliases TuneControlGrid
#' @family tune
#' @export
makeTuneControlGrid = function(same.resampling.instance = TRUE, impute.val = NULL,
resolution = 10L, tune.threshold = FALSE, tune.threshold.args = list(),
log.fun = "default", final.dw.perc = NULL, budget = NULL) {
assert(checkIntegerish(resolution, lower = 1, any.missing = FALSE, names = "unique"),
checkIntegerish(resolution, lower = 1, any.missing = FALSE, len = 1))
resolution = asInteger(resolution)
makeTuneControl(same.resampling.instance = same.resampling.instance, impute.val = impute.val,
start = NULL, resolution = resolution,
tune.threshold = tune.threshold, tune.threshold.args = tune.threshold.args,
log.fun = log.fun, final.dw.perc = final.dw.perc, budget = budget, cl = "TuneControlGrid")
}
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