eaf (version 2.1)

eafplot: Plot the Empirical Attainment Function for two objectives

Description

Computes and plots the Empirical Attainment Function, either as attainment surfaces for certain percentiles or as points.

Usage

eafplot(x, ...)
# S3 method for default
eafplot(
  x,
  sets = NULL,
  groups = NULL,
  percentiles = c(0, 50, 100),
  attsurfs = NULL,
  xlab = NULL,
  ylab = NULL,
  xlim = NULL,
  ylim = NULL,
  log = "",
  type = "point",
  col = NULL,
  lty = c("dashed", "solid", "solid", "solid", "dashed"),
  lwd = 1.75,
  pch = NA,
  cex.pch = par("cex"),
  las = par("las"),
  legend.pos = "topright",
  legend.txt = NULL,
  extra.points = NULL,
  extra.legend = NULL,
  extra.pch = 4:25,
  extra.lwd = 0.5,
  extra.lty = NA,
  extra.col = "black",
  maximise = c(FALSE, FALSE),
  xaxis.side = "below",
  yaxis.side = "left",
  axes = TRUE,
  sci.notation = FALSE,
  ...
)
# S3 method for formula
eafplot(formula, data, groups = NULL, subset = NULL, ...)
# S3 method for list
eafplot(x, ...)

Arguments

x

Either a matrix of data values, or a data frame, or a list of data frames of exactly three columns.

...

Other graphical parameters to plot.default().

sets

(numeric) Vector indicating which set each point belongs to.

groups

This may be used to plot profiles of different algorithms on the same plot.

percentiles

(numeric) Vector indicating which percentile should be plot. The default is to plot only the median attainment curve.

attsurfs

TODO

xlab, ylab, xlim, ylim, log, col, lty, lwd, pch, cex.pch, las

Graphical parameters, see plot.default().

type

(character(1)) string giving the type of plot desired. The following values are possible, points and area.

legend.pos

the position of the legend, see legend(). A value of "none" hides the legend.

legend.txt

a character or expression vector to appear in the legend. If NULL, appropriate labels will be generated.

extra.points

A list of matrices or data.frames with two-columns. Each element of the list defines a set of points, or lines if one of the columns is NA.

extra.legend

A character vector providing labels for the groups of points.

extra.pch, extra.lwd, extra.lty, extra.col

Control the graphical aspect of the points. See points() and lines().

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.

xaxis.side

On which side that xaxis is drawn. Valid values are "below" and "above". See axis().

yaxis.side

On which side that yaxis is drawn. Valid values are "left" and "right". See axis().

axes

A logical value indicating whether both axes should be drawn on the plot.

sci.notation

Generate prettier labels

formula

A formula of the type: time + cost ~ run | instance will draw time on the x-axis and cost on the y-axis. If instance is present the plot is conditional to the instances.

data

Dataframe containing the fields mentioned in the formula and in groups.

subset

(integer() | NULL) A vector indicating which rows of the data should be used. If left to default NULL all data in the data frame are used.

Value

No value is returned.

Methods (by class)

• default: Main function

• formula: Formula interface

• list: List interface for lists of data.frames or matrices

Details

This function can be used to plot random sets of points like those obtained by different runs of biobjective stochastic optimization algorithms. An EAF curve represents the boundary separating points that are known to be attainable (that is, dominated in Pareto sense) in at least a fraction (quantile) of the runs from those that are not. The median EAF represents the curve where the fraction of attainable points is 50%. In single objective optimization the function can be used to plot the profile of solution quality over time of a collection of runs of a stochastic optimizer.

See Also

read_datasets() eafdiffplot()

Examples

# NOT RUN {
data(gcp2x2)
tabucol <- subset(gcp2x2, alg != "TSinN1")
tabucol$alg <- tabucol$alg[drop=TRUE]
eafplot(time + best ~ run, data = tabucol, subset = tabucol$inst=="DSJC500.5")

# }
# NOT RUN {
 # These take time
eafplot(time + best ~ run | inst, groups=alg, data=gcp2x2)
eafplot(time + best ~ run | inst, groups=alg, data=gcp2x2,
	percentiles=c(0,50,100), cex.axis = 1.2, lty = c(2,1,2), lwd = c(2,2,2),
     col = c("black","blue","grey50"))

extdata_path <- system.file(package = "eaf", "extdata")
A1 <- read_datasets(file.path(extdata_path, "ALG_1_dat.xz"))
A2 <- read_datasets(file.path(extdata_path, "ALG_2_dat.xz"))
eafplot(A1, percentiles = 50, sci.notation = TRUE)
eafplot(list(A1 = A1, A2 = A2), percentiles = 50)

## Save as a PDF file.
# dev.copy2pdf(file = "eaf.pdf", onefile = TRUE, width = 5, height = 4)
# }
# NOT RUN {
## Using extra.points
# }
# NOT RUN {
data(HybridGA)
data(SPEA2relativeVanzyl)
eafplot(SPEA2relativeVanzyl, percentiles = c(25, 50, 75), 
        xlab = expression(C[E]), ylab = "Total switches", xlim = c(320, 400),
        extra.points = HybridGA$vanzyl, extra.legend = "Hybrid GA")

data(SPEA2relativeRichmond)
eafplot (SPEA2relativeRichmond, percentiles = c(25, 50, 75),
         xlab = expression(C[E]), ylab = "Total switches",
         xlim = c(90, 140), ylim = c(0, 25),
         extra.points = HybridGA$richmond, extra.lty = "dashed",
         extra.legend = "Hybrid GA")

eafplot (SPEA2relativeRichmond, percentiles = c(25, 50, 75),
         xlab = expression(C[E]), ylab = "Total switches",
         xlim = c(90, 140), ylim = c(0, 25), type = "area",
         extra.points = HybridGA$richmond, extra.lty = "dashed",
         extra.legend = "Hybrid GA", legend.pos = "bottomright")

data(SPEA2minstoptimeRichmond)
SPEA2minstoptimeRichmond[,2] <- SPEA2minstoptimeRichmond[,2] / 60
eafplot (SPEA2minstoptimeRichmond, xlab = expression(C[E]),
         ylab = "Minimum idle time (minutes)", maximise = c(FALSE, TRUE),
         las = 1, log = "y", main = "SPEA2 (Richmond)",
         legend.pos = "bottomright")
# }