Renamed main script file.

1 files changed, 250 insertions, 0 deletions

diff --git a/run.R b/run.R
new file mode 100644
index 0000000..4c073a0
--- /dev/null
+++ b/run.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
+#}
+automated.m <- function(Xs, labels) {
+  n <- nrow(Xs)
+  p <- ncol(Xs)
+  Xs <- cbind(Xs, matrix(data=0, nrow=n, ncol=p))
+  for (label in unique(labels)) {
+    for (j in 1:p) {
+      Xs[labels == label, j + p] <- mean(Xs[labels == label, j])
+    }
+  }
+
+  dist(Xs)
+}
+
+color_scale.blue_orange <- function(name) {
+  scale_colour_gradient(name = name, high = "#376092", low = "#e46c0a", space = "Lab")
+}
+
+color_scale.gradient2 <- function(name) {
+  scale_colour_gradient2(name = name, mid = "#dddddd", space = "Lab")
+}
+
+test <- function(file, suffix, output.dir) {
+  message("Testing dataset: ", file)
+  dataset <- read.table(file)
+
+  # Extract labels
+  labels <- dataset[, ncol(dataset)]
+  classes <- as.factor(labels)
+  X <- dataset[, -ncol(dataset)]
+
+  n <- nrow(X)
+
+  # Calculate distances (X) and normalize
+  message("\tCalculating dist(X)")
+  Dx <- dist(X)
+  Dx <- Dx / mean(Dx)
+  Dx <- as.matrix(Dx)
+
+  # Sample dataset
+  sample.indices <- sample(n, 3*sqrt(n))
+  classes.s <- as.factor(labels[sample.indices])
+
+  # Automatic sample positioning
+  message("\tCalculating Ys")
+  Dx.s <- Dx[sample.indices, sample.indices]
+  Ys   <- forceScheme(Dx.s)
+
+  # LAMP
+  message("\tCalculating Y")
+  Y <- lamp(X, sample.indices, Ys)
+
+  # Calculate distances (Y) and normalize
+  message("\tCalculating dist(Y)")
+  Dy <- dist(Y)
+  Dy <- Dy / mean(Dy)
+  Dy <- as.matrix(Dy)
+
+  message("\tCalculating P and Q")
+  prob <- d2p(Dx^2)
+  P <- prob$P
+  Q <- d2p.beta(Dy^2, prob$beta)
+
+  # Calculate measures
+  message("\tCalculating measures")
+  np <- NP(Dx, Dy)
+  silh <- silhouette(Dy, classes)
+  precision <- klDivergence(Q, P)
+  recall <- klDivergence(P, Q)
+
+  measures <- rbind(data.frame(mean=mean(np), median=median(np), sd=sd(np)),
+                    data.frame(mean=mean(silh), median=median(silh), sd=sd(silh)),
+                    data.frame(mean=mean(precision), median=median(precision), sd=sd(precision)),
+                    data.frame(mean=mean(recall), median=median(recall), sd=sd(recall)))
+  write.table(measures, paste(output.dir, suffix, "-measures.csv", sep=""), row.names=F)
+
+  if (!(all(is.finite(np)) &&
+      all(is.finite(silh)) &&
+      all(is.finite(precision)) &&
+      all(is.finite(recall)))) {
+    stop("Non-finite measures found")
+  }
+
+  # Plot results
+  message("\tPlotting results")
+  shape_scale <- scale_shape_manual(name = "Classe", values = 1:nlevels(classes))
+  Ys <- cbind(as.data.frame(Ys), classes.s)
+  Y  <- cbind(as.data.frame(Y), classes, np, silh, precision, recall)
+  p.s <- ggplot(Ys) +
+      theme_bw() +
+      labs(x = "", y = "") +
+      geom_point(aes(x = V1, y = V2, shape = classes.s, colour = classes.s)) +
+      shape_scale + scale_color_manual(name = "Classe", values = 1:nlevels(classes))
+  ggsave(paste(output.dir, "subsample-", suffix, ".pdf", sep=""), p.s, width=5, height=5)
+
+  p <- ggplot(Y) +
+      theme_bw() +
+      labs(x = "", y = "") +
+      geom_point(aes(x = V1, y = V2, shape = classes, colour = classes)) +
+      shape_scale + scale_color_manual(name = "Classe", values = 1:nlevels(classes))
+  ggsave(paste(output.dir, suffix, ".pdf", sep=""), p, width=5, height=5)
+
+  p.np <- ggplot(Y) +
+      theme_bw() +
+      labs(x = "", y = "") +
+      geom_point(aes(x = V1, y = V2, shape = classes, colour = np)) +
+      shape_scale + color_scale.blue_orange("NP")
+  ggsave(paste(output.dir, "np-", suffix, ".pdf", sep=""), p.np, width=5, height=5)
+
+  p.silh <- ggplot(Y) +
+      theme_bw() +
+      labs(x = "", y = "") +
+      geom_point(aes(x = V1, y = V2, shape = classes, colour = silh)) +
+      shape_scale + color_scale.blue_orange("Silhueta")
+  ggsave(paste(output.dir, "silh-", suffix, ".pdf", sep=""), p.silh, width=5, height=5)
+
+  p.precision <- ggplot(Y) +
+      theme_bw() +
+      labs(x = "", y = "") +
+      geom_point(aes(x = V1, y = V2, shape = classes, colour = precision)) +
+      shape_scale + color_scale.blue_orange("Precisão")
+  ggsave(paste(output.dir, "precision-", suffix, ".pdf", sep=""), p.precision, width=5, height=5)
+
+  p.recall <- ggplot(Y) +
+      theme_bw() +
+      labs(x = "", y = "") +
+      geom_point(aes(x = V1, y = V2, shape = classes, colour = recall)) +
+      shape_scale + color_scale.blue_orange("Revocação")
+  ggsave(paste(output.dir, "recall-", suffix, ".pdf", sep=""), p.recall, width=5, height=5)
+
+  
+  pdf(paste(output.dir, "all-", suffix, ".pdf", sep=""), width = 12, height = 16)
+  grid.arrange(p.s, p, p.np, p.silh, p.precision, p.recall, ncol = 2)
+  dev.off()
+
+  # Perform manipulation
+  message("\tCalculating Ys.m")
+  Dx.m <- automated.m(X[sample.indices, ], labels[sample.indices])
+  Ys.m <- forceScheme(Dx.m, Ys[, 1:2])
+
+  # LAMP
+  message("\tCalculating Y.m")
+  Y.m <- lamp(X, sample.indices, Ys.m)
+
+  # Calculate distances (Y.m) and normalize
+  message("\tCalculating dist(Y.m)")
+  Dy <- dist(Y.m)
+  Dy <- Dy / mean(Dy)
+  Dy <- as.matrix(Dy)
+
+  message("\tCalculating Q")
+  Q <- d2p.beta(Dy^2, prob$beta)
+
+  # Calculate measures
+  message("\tCalculating measures")
+  np <- NP(Dx, Dy) - np
+  silh <- silhouette(Dy, classes) - silh
+  precision <- klDivergence(Q, P) - precision
+  recall <- klDivergence(P, Q) - recall
+
+  measures <- rbind(data.frame(mean=mean(np), median=median(np), sd=sd(np)),
+                    data.frame(mean=mean(silh), median=median(silh), sd=sd(silh)),
+                    data.frame(mean=mean(precision), median=median(precision), sd=sd(precision)),
+                    data.frame(mean=mean(recall), median=median(recall), sd=sd(recall)))
+  write.table(measures, paste(output.dir, suffix, "-manip-measures.csv", sep=""), row.names=F)
+
+  if (!(all(is.finite(np)) &&
+      all(is.finite(silh)) &&
+      all(is.finite(precision)) &&
+      all(is.finite(recall)))) {
+    stop("Non-finite measures found")
+  }
+
+  # Plot results
+  message("\tPlotting results")
+  Ys.m <- cbind(as.data.frame(Ys.m), classes.s)
+  Y.m  <- cbind(as.data.frame(Y.m), classes, np, silh, precision, recall)
+  pm.s <- ggplot(Ys.m) +
+      theme_bw() +
+      labs(x = "", y = "") +
+      geom_point(aes(x = V1, y = V2, shape = classes.s, colour = classes.s)) +
+      shape_scale + scale_color_manual(name = "Classe", values = 1:nlevels(classes))
+  ggsave(paste(output.dir, "manip-subsample-", suffix, ".pdf", sep=""), pm.s, width=5, height=5)
+
+  pm <- ggplot(Y.m) +
+      theme_bw() +
+      labs(x = "", y = "") +
+      geom_point(aes(x = V1, y = V2, shape = classes, colour = classes)) +
+      shape_scale + scale_color_manual(name = "Classe", values = 1:nlevels(classes))
+  ggsave(paste(output.dir, "manip-", suffix, ".pdf", sep=""), pm, width=5, height=5)
+
+  p.np <- ggplot(Y.m) +
+      theme_bw() +
+      labs(x = "", y = "") +
+      geom_point(aes(x = V1, y = V2, shape = classes, colour = np)) +
+      shape_scale + color_scale.gradient2("NP")
+  ggsave(paste(output.dir, "manip-np-", suffix, ".pdf", sep=""), p.np, width=5, height=5)
+
+  p.silh <- ggplot(Y.m) +
+      theme_bw() +
+      labs(x = "", y = "") +
+      geom_point(aes(x = V1, y = V2, shape = classes, colour = silh)) +
+      shape_scale + color_scale.gradient2("Silhueta")
+  ggsave(paste(output.dir, "manip-silh-", suffix, ".pdf", sep=""), p.silh, width=5, height=5)
+
+  p.precision <- ggplot(Y.m) +
+      theme_bw() +
+      labs(x = "", y = "") +
+      geom_point(aes(x = V1, y = V2, shape = classes, colour = precision)) +
+      shape_scale + color_scale.gradient2("Precisão")
+  ggsave(paste(output.dir, "manip-precision-", suffix, ".pdf", sep=""), p.precision, width=5, height=5)
+
+  p.recall <- ggplot(Y.m) +
+      theme_bw() +
+      labs(x = "", y = "") +
+      geom_point(aes(x = V1, y = V2, shape = classes, colour = recall)) +
+      shape_scale + color_scale.gradient2("Revocação")
+  ggsave(paste(output.dir, "manip-recall-", suffix, ".pdf", sep=""), p.recall, width=5, height=5)
+
+  pdf(paste(output.dir, "original-manip-", suffix, ".pdf", sep=""), width = 10, height = 8)
+  grid.arrange(p.s, p, pm.s, pm, ncol = 2)
+  dev.off()
+
+  pdf(paste(output.dir, "manip-measures-", suffix, ".pdf", sep=""), width = 10, height = 8)
+  grid.arrange(p.np, p.silh, p.precision, p.recall, ncol = 2)
+  dev.off()
+  
+  pdf(paste(output.dir, "manip-all-", suffix, ".pdf", sep=""), width = 12, height = 16)
+  grid.arrange(pm.s, pm, p.np, p.silh, p.precision, p.recall, ncol = 2)
+  dev.off()
+}
+
+#test(file = "datasets/iris.tbl", suffix = "iris", "tests/")
+test(file = "datasets/wdbc.tbl", suffix = "wdbc", "tests/")
+test(file = "datasets/segmentation.tbl", suffix = "segmentation", "tests/")
+test(file = "datasets/glass.tbl", suffix = "glass", "tests/")