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#' @title Plots for Tuning Instances
#'
#' @description
#' Visualizations for [mlr3tuning::TuningInstanceSingleCrit].
#' The argument `type` controls what kind of plot is drawn.
#' Possible choices are:
#'
#' * `"marginal"` (default): Scatter plots of x versus y.
#' The color of the points shows the batch number.
#' * `"performance"`: Scatter plots of batch number versus y
#' * `"parameter"`: Scatter plots of batch number versus input.
#' The color of the points shows the y values.
#' * `"parallel"`: Parallel coordinates plot.
#' hyperparameters are rescaled by `(x - mean(x)) / sd(x)`.
#' * `"points"`: Scatter plot of two x dimensions versus.
#' The color of the points shows the y values.
#' * `"surface"`: Surface plot of two x dimensions versus y values.
#' The y values are interpolated with the supplied [mlr3::Learner].
#' * `"pairs"`: Plots all x and y values against each other.
#' * `"incumbent"`: Plots the incumbent versus the number of configurations.
#'
#' @param object ([mlr3tuning::TuningInstanceSingleCrit].
#' @template param_type
#' @param cols_x (`character()`)\cr
#' Column names of hyperparameters.
#' By default, all untransformed hyperparameters are plotted.
#' Transformed hyperparameters are prefixed with `x_domain_`.
#' @param trafo (`logical(1)`)\cr
#' If `FALSE` (default), the untransformed hyperparameters are plotted.
#' If `TRUE`, the transformed hyperparameters are plotted.
#' @param learner ([mlr3::Learner])\cr
#' Regression learner used to interpolate the data of the surface plot.
#' @param grid_resolution (`numeric()`)\cr
#' Resolution of the surface plot.
#' @template param_theme
#' @param ... (ignored).
#'
#' @return [ggplot2::ggplot()].
#'
#' @export
#' @examples
#' if (requireNamespace("mlr3tuning") && requireNamespace("patchwork")) {
#' library(mlr3tuning)
#'
#' learner = lrn("classif.rpart")
#' learner$param_set$values$cp = to_tune(0.001, 0.1)
#' learner$param_set$values$minsplit = to_tune(1, 10)
#'
#' instance = TuningInstanceSingleCrit$new(
#' task = tsk("iris"),
#' learner = learner,
#' resampling = rsmp("holdout"),
#' measure = msr("classif.ce"),
#' terminator = trm("evals", n_evals = 10))
#'
#' tuner = tnr("random_search")
#'
#' tuner$optimize(instance)
#'
#' # plot performance versus batch number
#' autoplot(instance, type = "performance")
#'
#' # plot cp values versus performance
#' autoplot(instance, type = "marginal", cols_x = "cp")
#'
#' # plot transformed parameter values versus batch number
#' autoplot(instance, type = "parameter", trafo = TRUE)
#'
#' # plot parallel coordinates plot
#' autoplot(instance, type = "parallel")
#'
#' # plot pairs
#' autoplot(instance, type = "pairs")
#' }
autoplot.TuningInstanceSingleCrit = function(object, type = "marginal", cols_x = NULL, trafo = FALSE, learner = mlr3::lrn("regr.ranger"), grid_resolution = 100, theme = theme_minimal(), ...) {
autoplot.OptimInstanceSingleCrit(object = object, type = type, cols_x = cols_x, trafo = trafo, learner = learner, grid_resolution = grid_resolution, theme = theme, ...)
}
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