R/plotTuneMultiCritResult.R

In riebetob/mlr: Machine Learning in R

#' @title Plots multi-criteria results after tuning using ggplot2.
#'
#' @description
#' Visualizes the pareto front and possibly the dominated points.
#'
#' @param res [\code{\link{TuneMultiCritResult}}]\cr
#'   Result of \code{\link{tuneParamsMultiCrit}}.
#' @param path [\code{logical(1)}]\cr
#'   Visualize all evaluated points (or only the non-dominated pareto front)?
#'   For the full path, the size of the points on the front is slightly increased.
#'   Default is \code{TRUE}.
#' @param col [\code{character(1)}]\cr
#'   Which column of \code{res$opt.path} should be mapped to ggplot2 color?
#'   Default is \code{NULL}, which means none.
#' @param shape [\code{character(1)}]\cr
#'   Which column of \code{res$opt.path} should be mapped to ggplot2 shape?
#'   Default is \code{NULL}, which means none.
#' @param pointsize [\code{numeric(1)}]\cr
#'   Point size for ggplot2 \code{\link[ggplot2]{geom_point}} for data points.
#'   Default is 2.
#' @param pretty.names [\code{logical{1}}]\cr
#'   Whether to use the ID of the measures instead of their name in labels. Defaults to \code{TRUE}.
#' @template ret_gg2
#' @family tune_multicrit
#' @export
#' @examples
#' # see tuneParamsMultiCrit
plotTuneMultiCritResult = function(res, path = TRUE, col = NULL, shape = NULL, pointsize = 2, pretty.names = TRUE) {
  assertClass(res, "TuneMultiCritResult")
  assertFlag(path)
  op1 = res$opt.path
  op2 = as.data.frame(op1)
  if (!is.null(col))
    assertChoice(col, choices = colnames(op2))
  if (!is.null(shape))
    assertChoice(shape, colnames(op2))

  names.y = colnames(res$y)[1:2]

  map = aes_string(x = names.y[1L], y = names.y[2L], col = col, shape = shape)
  i.front = res$ind
  if (path) {
    i.data = seq_row(op2)
  } else {
    i.data = i.front
  }
  data = op2[i.data, , drop = FALSE]
  front = op2[i.front, , drop = FALSE]

  p = ggplot(data, mapping = map)
  p = p + geom_point(size = pointsize)
  if (path)
    p = p + geom_point(data = front, size = pointsize * 1.5)
  if (pretty.names) {
    names.y = sapply(res$measures, function(x) x$id)
    p = p + labs(x = names.y[1L], y = names.y[2L])
  }
  return(p)
}
#' @title Plots multi-criteria results after tuning using ggvis.
#'
#' @description
#' Visualizes the pareto front and possibly the dominated points.
#'
#' @param res [\code{\link{TuneMultiCritResult}}]\cr
#'   Result of \code{\link{tuneParamsMultiCrit}}.
#' @param path [\code{logical(1)}]\cr
#'   Visualize all evaluated points (or only the non-dominated pareto front)?
#'   Points are colored according to their location.
#'   Default is \code{TRUE}.
#'
#' @template ret_ggv
#' @family tune_multicrit
#' @export
#' @examples
#' # see tuneParamsMultiCrit
plotTuneMultiCritResultGGVIS = function(res, path = TRUE) {
  requirePackages("_ggvis")
  assertClass(res, "TuneMultiCritResult")
  assertFlag(path)
  plt.data = as.data.frame(res$opt.path)
  plt.data$location = factor(row.names(plt.data) %in% res$ind, levels = c(TRUE, FALSE),
                             labels = c("frontier", "interior"))

  if (path) {
    plt = ggvis::ggvis(plt.data, ggvis::prop("x", as.name(colnames(res$y)[1L])),
                       ggvis::prop("y", as.name(colnames(res$y)[2L])))
    plt = ggvis::layer_points(plt, ggvis::prop("fill", as.name("location")))
  } else {
    plt.data = plt.data[plt.data$location == "frontier", , drop = FALSE]
    plt = ggvis::ggvis(plt.data, ggvis::prop("x", as.name(colnames(res$y)[1L])),
                       ggvis::prop("y", as.name(colnames(res$y)[2L])))
    plt = ggvis::layer_points(plt)
  }
  return(plt)
}