parallel_cat: Parallel Coordinates Category

Description

Parallel categories plot of selected configurations. Numerical parameters are discretized to maximum n_bins intervals. To visualize configurations of other iterations these must be provided setting the argument iterations, groups of configurations of different iterations are shown in different colors. Specific configurations can be selected providing their ids in the id_configurations argument.

Usage

parallel_cat(
  irace_results,
  id_configurations = NULL,
  param_names = NULL,
  iterations = NULL,
  by_n_param = NULL,
  n_bins = 3,
  filename = NULL
)

Value

parallel categories plot

Arguments

irace_results: The data generated when loading the .Rdata file created by irace (or the filename of that file).
id_configurations: Configuration ids to be included in the plot. Example: c(20,50,100,300,500,600,700)
param_names: (character()) Parameters to be included in the plot. Example: c("algorithm","alpha","rho","q0","rasrank").
iterations: Numeric vector, iterations from which configuration should be obtained (example: iterations = c(1,4,5))
by_n_param: Numeric (optional), maximum number of parameters to be displayed.
n_bins: Numeric (default 3), number of intervals to generate for numerical parameters.
filename: (character(1)) File name to save the plot, for example "~/path/example/filename.png".

Details

The parameters to be included in the plot can be selected with the param_names argument. Additionally, the maximum number of parameters to be displayed in one plot. A list of plots is returned by this function in several plots are required to display the selected data.

Examples

Run this code

iraceResults <- read_logfile(system.file(package="irace", "exdata",
                                         "irace-acotsp.Rdata", mustWork = TRUE))
parallel_cat(iraceResults)
# \donttest{ 
parallel_cat(iraceResults, by_n_param = 6)
parallel_cat(iraceResults, id_configurations = c(20, 50, 100))
parallel_cat(iraceResults, param_names = c("algorithm", "alpha", "rho", "q0", "rasrank"))
parallel_cat(iraceResults, iterations = c(1, 4, 6), n_bins=4)
# }