plot.SRCS: Heatmap plot of the ranking achieved by a target variable levels after all statistical pairwise comparisons in multi-parameter problem instances.

Description

plot.SRCS: Function to display a grid of heatmaps representing the statistical ranking of one level of the target factor (usually, the algorithm) vs the rest of levels of the target factor, over several problem configurations characterized by (at most) 3 parameters in addition to the target factor. animatedplot: Function to generate an animated video consisting of a temporal sequence of grid plots like those generated by plot.SRCS. The function requires software ImageMagick has been installed. singleplot: Function to display either a single heatmap representing the statistical ranking of one level of the target factor (usually, the algorithm) vs the rest of levels of the target factor, over one single problem configurations defined by a combination of values for the problem configuration parameters.

Usage

## S3 method for class 'SRCS':
plot(x, yOuter, xOuter, yInner, xInner, zInner = "rank",
  out.Y.par = list(), out.X.par = list(), inner.X.par = list(),
  inner.Y.par = list(), colorbar.par = list(),
  color.function = heat.colors, heatmaps.per.row = NULL,
  heatmaps.titles = NULL, show.colorbar = TRUE, annotation.lab = NULL,
  heat.cell.par = list(), heat.axes.par = list(),
  colorbar.cell.par = list(), colorbar.axes.par = list(),
  annotation.text.par = list(), ...)

animatedplot(x, filename, path.to.converter, yOuter, xOuter, yInner, xInner,
  zInner, width = 800, height = 800, res = 100, pointsize = 16,
  delay = 30, type = c("png", "jpeg", "bmp", "tiff"), quality = 75,
  compression = c("none", "rle", "lzw", "jpeg", "zip"), annotations = NULL,
  ...)

singleplot(x, yInner, xInner, zInner = "rank", color.function = heat.colors,
  labels.par = list(), colorbar.par = list(), heat.axes.par = list(),
  colorbar.axes.par = list(), haxis = TRUE, vaxis = TRUE, title = "",
  show.colorbar = TRUE, ...)

Arguments

An SRCS object containing columns for the names of the problem parameters (including the algorithm) and the rank obtained by that algorithm when compared with the rest over the same problem configuration. Typically this is the object returned by a call t

yOuter,xOuter

Names of the variables in x that will be placed vertically (in the left-most part) and horizontally (on the top), respectively. Each level of yOuter (resp. xOuter) corresponds to a complete row (complete column) of

yInner,xInner

Names of the variables in x that will be placed on the Y axis and on the X axis of every heatmap, respectively. Each level of yInner (resp. xInner) corresponds to a row (column) inside a heatmap.

zInner

Name of the variable in x that will be represented with a color code inside every heatmap. Usually corresponds to the ranking column of x, which will most often contain integer values (negatives are allowed). When the SRCS ob

out.Y.par,out.X.par

A tagged list with parameters to configure how the labels of the outer Y and X variables and their levels are displayed. Valid parameters and their default values are as follows:

lab = TRUE

{ Label with the name of the

item

inner.X.par,inner.Y.par
lab.width = lcm(1)
lab.textpar = list(cex = 1)
levels.loc = c("bottom", "left", "all", "none")
levels.at = NULL
levels.las = 1
colorbar.par
hlines = TRUE
color.function
heatmaps.per.row
heatmaps.titles
show.colorbar
annotation.lab
heat.cell.par
heat.axes.par
colorbar.cell.par
colorbar.axes.par
annotation.text.par
...
filename
path.to.converter
width,height
res
pointsize
delay
type
quality
compression
annotations
labels.par
ylab = TRUE
xlevels.at = NULL
ylevels.at = NULL
haxis,vaxis
title

itemize

xlab = TRUE

code

zInner

Details

plot.SRCS plots a grid with the results over all problem configurations, and should be applied to the object returned by SRCSranks with only one performance column. singleplot is used for plotting only one heatmap for a subset of problem configurations in which the outer X and Y parameters take a fixed value, and should be applied to the object returned by SRCScomparison. animatedplot creates a video from a sequence of plots, intended to show the temporal evolution of the ranking over time. It should be applied only to the object returned by SRCSranks when the performance argument passed to it was a vector of strings, each of them being the performance column of the data at a given time instant.

Examples

Run this code

# Example from a Machine Learning problem with noisy data
	ranks = SRCSranks(ML1, params = c("Dataset", "Noise type", "Noise ratio"),
	  target = "Algorithm", performance="Performance", maximize = TRUE, ncores = 2,
	  paired = TRUE);
	singleplot(ranks, yInner = "Noise type",
   xInner = "Noise ratio", Algorithm = "C4.5", Dataset = "glass",
	  )
	plot(x = ranks, yOuter = "Dataset", xOuter = "Algorithm", yInner = "Noise type",
	  xInner = "Noise ratio", out.X.par = list(levels.lab.textpar =
	  list(col = "white"), levels.bg = "black", levels.border = "white"),
	  out.Y.par = list(levels.bg = "gray"), colorbar.axes.par = list(cex.axis = 0.8),
	  show.colorbar = TRUE)
	SRCScomparison(ranks, "Algorithm", Dataset = "autos", `Noise type` = "ATT_GAUS",
	  `Noise ratio`= 10, pvalues = FALSE)
# ---------------------------------------------------
mat = matrix(NA, nrow = nrow(MPBall), ncol = ncol(MPBall))
# First, take the average of the previous performance columns up to each change point
for(j in 6:ncol(MPBall)){
  mat[,j] = rowSums(MPBall[,5:j])/(j-5+1)
}
MPBall[,6:ncol(MPBall)] = mat[,6:ncol(MPBall)]

ranksall = SRCSranks(MPBall, params = c("Dim", "CF", "Severity"), target="Algorithm",
   test = "tukeyHSD", performance=paste("OffError", seq(from=1, to = 100, by = 24),
   sep = "_"), maximize = FALSE, ncores = 2)

# Adjust argument path.to.converter to point to ImageMagick convert utility
animatedplot(x = ranksall, filename = "MPBconv_reduced.gif",
	             path.to.converter = "C:/Program Files/ImageMagick-6.8.8-Q8/convert.exe",
	             yOuter = "Algorithm", xOuter = "Dim", yInner = "CF", xInner = "Severity",
	             zInner = paste0("rank",1:5), delay = 30,
	             annotations = paste0("At change ",seq.int(from = 1, to = 100, by = 24)),
	             inner.Y.par = list(levels.at = c("40", "200", "400", "600", "800", "1000"),
              lab = "Change\nfrequency", levels.loc = "left"),
	             heat.cell.par = list(pty = "s"),
	             inner.X.par = list(levels.at = c("2", "8", "14")),
	             out.Y.par = list(levels.lab.textpar = list(cex = 1, col = "white"),
              levels.bg = "black", levels.border = "white"),
	             out.X.par = list(lab = "Dimension", levels.bg = "gray"),
	             colorbar.par = list(levels.at = c("-2", "0", "2")),
	             colorbar.axes.par = list(cex.axis = 0.8),
	             show.colorbar = TRUE, height = 500
            )
# The full dataset (20 MB) can be downloaded from
# http://decsai.ugr.es/~pjvi/SRCSfiles/MPBall.RData
# (the average must still be computed before plotting, just as in the example above)

Run the code above in your browser using DataLab

Data Engineering and BI courses are free this week!