# 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, pairing.col = "Fold");
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 = "automobile", `Noise type` = "ATT_GAUS",
`Noise ratio`= 10, pvalues = FALSE)
# ---------------------------------------------------
## Not run:
# 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)
# # Check the script in http://decsai.ugr.es/~pjvi/SRCSfiles/DOPvideoScript.R
# ## End(Not run)
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