parallel_coord {iraceplot} | R Documentation |
Parallel Coordinates Plot
Description
Parallel coordinates plot of a set of selected configurations. Each line in
the plot represents a configuration. By default, the final elite
configurations are shown. To visualize configurations of other iterations
these must be provided setting the argument iterations
, configurations of
different iterations are shown in different colors. Setting the only_elites
argument to FALSE
displays all configurations in the selected
iterations, specific configurations can be selected providing their ids in
the id_configuration
argument.
Usage
parallel_coord(
irace_results,
id_configurations = NULL,
param_names = NULL,
iterations = NULL,
only_elite = TRUE,
by_n_param = NULL,
color_by_instances = TRUE,
filename = NULL
)
Arguments
irace_results |
The data generated when loading the |
id_configurations |
Configuration ids to be included in the
plot. Example: |
param_names |
( |
iterations |
Numeric vector, iteration number that should be included in the plot
(example: |
only_elite |
logical (default |
by_n_param |
Numeric (optional), maximum number of parameters to be displayed. |
color_by_instances |
Logical (default TRUE), choose how to color the lines. TRUE shows the number of instances evaluated by the configuration in the colores. FALSE to show the iteration number where the configuration was sampled. |
filename |
( |
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 if several plots are required
to display the selected data.
To export the plot to a file, it is possible to do it so manually using the functionality provided by plotly in the plot. If a filename is provided, an orca server will be used to export the plots and thus, it requires the library to be installed (https://github.com/plotly/orca).
Value
parallel coordinates plot
Examples
iraceResults <- read_logfile(system.file(package="irace", "exdata",
"irace-acotsp.Rdata", mustWork = TRUE))
parallel_coord(iraceResults)
parallel_coord(iraceResults, by_n_param = 5)
parallel_coord(iraceResults, only_elite = FALSE)
parallel_coord(iraceResults, id_configurations = c(20, 30, 40, 50, 100))
parallel_coord(iraceResults, param_names = c("algorithm", "alpha", "rho", "q0", "rasrank"))
parallel_coord(iraceResults, iterations = c(1, 4, 6))