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#' @title Plots attainment functions for data stored in multiple OptPaths.
#'
#' @description
#' Can be used to plot OptPaths where information for bi-objective
#' evaluation was logged for repeated runs of different algorithmic runs.
#' Pretty directly calls \code{\link[eaf]{eafplot}}.
#'
#' @param opt.paths [\code{list}]\cr
#' List of list of \code{OptPath} objects. First index is the algorithm / major variation
#' in the experiment, second index is the index of the replicated run.
#' @param xlim [\code{numeric(2)}]\cr
#' The x limits (x1, x2) of the plot.
#' @param ylim [\code{numeric(2)}]\cr
#' The y limits (y1, y2) of the plot.
#' @param ... [any]\cr
#' Passed on to \code{\link[eaf]{eafplot}}.
#' @note
#' We changed the defaults of \code{\link[eaf]{eafplot}} in the following way:
#' The axis are labeled by \code{y.names}, colors are set to our favorite grey
#' values and linetypes changed, too. With our colors / linetypes default it is
#' possible to distinguish 6 different algorithms. But this can again be overwritten by the user.
#' @return [\code{data.frame}]
#' Invisibly returns the data passed to \code{\link[eaf]{eafplot}}.
#' @export
plotEAF = function(opt.paths, xlim = NULL, ylim = NULL, ...) {
requirePackages("eaf", why = "plotEAF")
# we need a list of lists with optimization pathes
assertList(opt.paths, min.len = 1L, types = "list", names = "unique")
if (!is.null(xlim))
assertNumeric(xlim, len = 2L)
if (!is.null(ylim))
assertNumeric(ylim, len = 2L)
algos = names(opt.paths)
y.names = NULL
minimize = NULL
data = data.frame()
for (i in seq_along(algos)) {
# extract opt path list for current algorithm
a = algos[i]
runs = opt.paths[[i]]
assertList(runs, types = "OptPath", min.len = 1L)
# combine all fronts for this algo + add algo / repl + do some sanity checks
fronts = lapply(seq_along(runs), function(j) {
run = runs[[j]]
df = as.data.frame(getOptPathParetoFront(run))
cns = colnames(df)
if (length(cns) != 2L) {
stopf("Must always have 2 objectives in opt path. But found: %i", length(cns))
}
if (i == 1L && j == 1L) {
y.names <<- cns
minimize <<- run$minimize
}
if (!all(y.names == cns)) {
stopf("Must always have the same 2 objectives in opt path: %s (first ones taken). But found here: %s",
collapse(y.names), collapse(cns))
}
if (!all(minimize == run$minimize)) {
stopf("Must always have the same 'minimize' settings for objectives in opt path: %s (first one taken).
But found here: %s", collapse(minimize), collapse(run$minimize))
}
# add column for algorithm and replication
cbind(df, .algo = a, .repl = j)
})
fronts = do.call(rbind, fronts)
data = rbind(data, fronts)
}
yn1 = y.names[1L]
yn2 = y.names[2L]
# build parameter list for eafplot function
f = as.formula(sprintf("%s + %s ~ .repl", yn1, yn2))
defaults = list(
xlab = yn1, ylab = yn2,
percentiles = 50,
col = c("darkgrey", "darkgrey", "darkgrey", "black", "black", "black"),
lty = c("solid", "dashed", "dotdash", "solid", "dashed", "dotdash")
)
args = list(...)
args = insert(defaults, args)
args$data = data
args$groups = quote(.algo)
args$maximise = !minimize
args$xlim = xlim
args$ylim = ylim
do.call(eaf::eafplot, c(list(f), args)) # due to not so good programming in eafplot the first argument can not be named if it's a formula
return(data)
}
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