Interactively choose according to empirical attainment function differences

Creates the same plot as eafdiffplot() but waits for the user to click in one of the sides. Then it returns the rectangles the give the differences in favour of the chosen side. These rectangles may be used for interactive decision-making as shown in Diaz and López-Ibáñez (2021) . The function choose_eafdiff() may be used in a non-interactive context.

Usage

choose_eafdiffplot(
  data.left,
  data.right,
  intervals = 5,
  maximise = c(FALSE, FALSE),
  title.left = deparse(substitute(data.left)),
  title.right = deparse(substitute(data.right)),
  ...
)

choose_eafdiff(x, left = stop("'left' must be either TRUE or FALSE"))

Arguments

data.left, data.right: Data frames corresponding to the input data of left and right sides, respectively. Each data frame has at least three columns, the third one being the set of each point. See also read_datasets().
intervals: (integer(1)|character())
The absolute range of the differences \([0, 1]\) is partitioned into the number of intervals provided. If an integer is provided, then labels for each interval are computed automatically. If a character vector is provided, its length is taken as the number of intervals.
maximise: (logical() | logical(1))
Whether the objectives must be maximised instead of minimised. Either a single logical value that applies to all objectives or a vector of logical values, with one value per objective.
title.left, title.right: Title for left and right panels, respectively.
...: Other graphical parameters are passed down to eafdiffplot().
x: (matrix()) Matrix of rectangles representing EAF differences (returned by eafdiff() with rectangles=TRUE).
left: (logical(1)) With left=TRUE return the rectangles with positive differences, otherwise return those with negative differences but differences are converted to positive.

Value

matrix where the first 4 columns give the coordinates of two corners of each rectangle and the last column. In both cases, the last column gives the positive differences in favor of the chosen side.

References

Juan Esteban Diaz, Manuel López-Ibáñez (2021). “Incorporating Decision-Maker's Preferences into the Automatic Configuration of Bi-Objective Optimisation Algorithms.” European Journal of Operational Research, 289(3), 1209–1222. doi:10.1016/j.ejor.2020.07.059 .

Examples


# \donttest{
extdata_dir <- system.file(package="eaf", "extdata") 
A1 <- read_datasets(file.path(extdata_dir, "wrots_l100w10_dat"))
A2 <- read_datasets(file.path(extdata_dir, "wrots_l10w100_dat"))
if (interactive()) {
  rectangles <- choose_eafdiffplot(A1, A2, intervals = 5)
} else { # Choose A1
  rectangles <- eafdiff(A1, A2, intervals = 5, rectangles = TRUE)
  rectangles <- choose_eafdiff(rectangles, left = TRUE)
}
reference <- c(max(A1[, 1], A2[, 1]), max(A1[, 2], A2[, 2]))
x <- split.data.frame(A1[,1:2], A1[,3])
hv_A1 <- sapply(split.data.frame(A1[, 1:2], A1[, 3]),
                 hypervolume, reference=reference)
hv_A2 <- sapply(split.data.frame(A2[, 1:2], A2[, 3]),
                 hypervolume, reference=reference)
boxplot(list(A1=hv_A1, A2=hv_A2), main = "Hypervolume")


whv_A1 <- sapply(split.data.frame(A1[, 1:2], A1[, 3]),
                 whv_rect, rectangles=rectangles, reference=reference)
whv_A2 <- sapply(split.data.frame(A2[, 1:2], A2[, 3]),
                 whv_rect, rectangles=rectangles, reference=reference)
boxplot(list(A1=whv_A1, A2=whv_A2), main = "Weighted hypervolume")

# }