1 files changed, 152 insertions, 0 deletions
diff --git a/tests.R b/tests.R
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index 0000000..d56114c
--- /dev/null
+++ b/tests.R
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+require(ggplot2)
+require(gridExtra)
+require(mp)
+
+source("measures.R")
+
+automated.m <- function(D, labels) {
+  D.m <- D
+  for (label in unique(labels)) {
+    same.label <- labels == label
+    D.m[same.label, same.label] <- D[same.label, same.label] * 0.1
+    #D.m[same.label, diff.label] <- D[same.label, diff.label] * 10
+    #D.m[diff.label, same.label] <- D.m[same.label, diff.label]
+  }
+
+  D.m
+}
+
+xy.df <- function(M) {
+  M <- as.data.frame(M)
+  names(M) <- c("x", "y")
+
+  M
+}
+
+test <- function(file, suffix, output.dir) {
+  cat("Testing dataset ", file, "...\n")
+  dataset <- read.table(file)
+
+  # Extract labels
+  labels  <- dataset[, ncol(dataset)]
+
+  # Remove labels from dataset
+  X <- dataset[, -ncol(dataset)]
+  
+  n <- nrow(X)
+
+  # Calculate distances (X) and normalize
+  Dx <- dist(X)
+  Dx <- Dx / mean(Dx)
+  Dx <- as.matrix(Dx)
+
+  sample.indices <- sample(n, 3*sqrt(n))
+  Dx.s <- Dx[sample.indices, sample.indices]
+  Ys   <- forceScheme(Dx.s)
+  Ys   <- xy.df(Ys)
+  Y    <- lamp(X, sample.indices, Ys)
+  Y    <- xy.df(Y)
+
+  # Plot mapping
+  classes <- as.factor(labels)
+  classes.s <- as.factor(labels[sample.indices])
+  p.s <- ggplot(cbind(Ys, classes.s), aes(x = x, y = y, colour = classes.s)) + geom_point()
+  p   <- ggplot(cbind(Y, classes), aes(x = x, y = y, colour = classes)) + geom_point()
+  pdf(paste(output.dir, "original-", suffix, ".pdf", sep=""), width = 10, height = 5)
+  grid.arrange(p.s, p,
+               widths = unit(rep_len(3, 2), "null"),
+               heights = unit(rep_len(1, 2), "null"),
+               ncol=2)
+  dev.off()
+  png(paste(output.dir, "original-", suffix, ".png", sep=""), width = 1200, height = 600)
+  grid.arrange(p.s, p,
+               widths = unit(rep_len(3, 2), "null"),
+               heights = unit(rep_len(1, 2), "null"),
+               ncol=2)
+  dev.off()
+
+  # Calculate distances (Y) and normalize
+  Dy <- dist(Y)
+  Dy <- Dy / mean(Dy)
+  Dy <- as.matrix(Dy)
+
+  # Calculate measures and plot
+  sigmas <- vector("numeric", n)
+  sigmas[] <- 1
+  P <- d2p(Dx, sigmas)
+  Q <- d2p(Dy, sigmas)
+  np = NP(Dx, Dy)
+  #stress = stress(Dx, Dy),
+  precision <- klDivergence(Q, P)
+  recall <- klDivergence(P, Q)
+  p.np        <- ggplot(cbind(Y, np), aes(x = x, y = y, colour = np)) + geom_point() + labs(title = "NP (9)")
+  p.precision <- ggplot(cbind(Y, precision), aes(x = x, y = y, colour = precision)) + geom_point() + labs(title = "Precision")
+  p.recall    <- ggplot(cbind(Y, recall), aes(x = x, y = y, colour = recall)) + geom_point()    + labs(title = "Recall")
+  pdf(paste(output.dir, "measures-original-", suffix, ".pdf", sep=""), width = 15, height = 5)
+  grid.arrange(p.np, p.precision, p.recall,
+               widths = unit(rep_len(3, 3), "null"),
+               heights = unit(rep_len(1, 3), "null"),
+               ncol=3)
+  dev.off()
+  png(paste(output.dir, "measures-original-", suffix, ".png", sep=""), width = 1800, height = 600)
+  grid.arrange(p.np, p.precision, p.recall,
+               widths = unit(rep_len(3, 3), "null"),
+               heights = unit(rep_len(1, 3), "null"),
+               ncol=3)
+  dev.off()
+
+  # Perform manipulation
+  Dx.m <- automated.m(Dx.s, labels[sample.indices])
+  Ys.m <- forceScheme(Dx.m)
+  Ys.m <- xy.df(Ys.m)
+  Y.m  <- lamp(X, sample.indices, Ys.m)
+  Y.m  <- xy.df(Y.m)
+
+  # Plot mapping
+  p.s <- ggplot(cbind(Ys.m, classes.s), aes(x = x, y = y, colour = classes.s)) + geom_point()
+  p   <- ggplot(cbind(Y.m, classes), aes(x = x, y = y, colour = classes)) + geom_point()
+  pdf(paste(output.dir, "manip-", suffix, ".pdf", sep=""), width = 10, height = 5)
+  grid.arrange(p.s, p,
+               widths = unit(rep_len(3, 2), "null"),
+               heights = unit(rep_len(1, 2), "null"),
+               ncol=2)
+  dev.off()
+  png(paste(output.dir, "manip-", suffix, ".png", sep=""), width = 1200, height = 600)
+  grid.arrange(p.s, p,
+               widths = unit(rep_len(3, 2), "null"),
+               heights = unit(rep_len(1, 2), "null"),
+               ncol=2)
+  dev.off()
+
+  # Calculate distances (Y.m) and normalize
+  Dy <- dist(Y.m)
+  Dy <- Dy / mean(Dy)
+  Dy <- as.matrix(Dy)
+  Q <- d2p(Dy, sigmas)
+
+  # Calculate measures and plot
+  np = np - NP(Dx, Dy)
+  #stress = stress(Dx, Dy),
+  precision <- precision - klDivergence(Q, P)
+  recall <- recall - klDivergence(P, Q)
+  p.np        <- ggplot(cbind(Y.m, np), aes(x = x, y = y, colour = np)) + geom_point() + labs(title = "NP (9)")
+  p.precision <- ggplot(cbind(Y.m, precision), aes(x = x, y = y, colour = precision)) + geom_point() + labs(title = "Precision")
+  p.recall    <- ggplot(cbind(Y.m, recall), aes(x = x, y = y, colour = recall)) + geom_point()    + labs(title = "Recall")
+  pdf(paste(output.dir, "measures-manip-", suffix, ".pdf", sep=""), width = 15, height = 5)
+  grid.arrange(p.np, p.precision, p.recall,
+               widths = unit(rep_len(3, 3), "null"),
+               heights = unit(rep_len(1, 3), "null"),
+               ncol=3)
+  dev.off()
+  png(paste(output.dir, "measures-manip-", suffix, ".png", sep=""), width = 1800, height = 600)
+  grid.arrange(p.np, p.precision, p.recall,
+               widths = unit(rep_len(3, 3), "null"),
+               heights = unit(rep_len(1, 3), "null"),
+               ncol=3)
+  dev.off()
+}
+
+test(file = "datasets/iris-std.tbl", suffix = "iris", "plots/")
+test(file = "datasets/wdbc.tbl", suffix = "wdbc", "plots/")
+test(file = "datasets/segmentation.tbl", suffix = "segmentation", "plots/")
+test(file = "datasets/images.tbl", suffix = "images", "plots/")