diff options
| -rw-r--r-- | tests.R | 187 |
1 files changed, 99 insertions, 88 deletions
@@ -9,140 +9,151 @@ automated.m <- function(D, labels) { 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") + message("Testing dataset ", file) dataset <- read.table(file) # Extract labels - labels <- dataset[, ncol(dataset)] - - # Remove labels from dataset + 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) - 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() + # 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) - # Calculate measures and plot + message("\tCalculating P and Q") sigmas <- vector("numeric", n) sigmas[] <- 1 P <- d2p(Dx, sigmas) Q <- d2p(Dy, sigmas) - np = NP(Dx, Dy) - #stress = stress(Dx, Dy), + + # Calculate measures + message("\tCalculating measures") + np <- NP(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) + + # Plot results + #color_scale <- scale_colour_gradientn(colours = c("#e46c0a", "#dddddd", "#376092")) + color_scale <- scale_colour_gradient2(mid = "#dddddd", space = "Lab") + shape_scale <- scale_shape(solid = FALSE) + Ys <- cbind(as.data.frame(Ys), classes.s) + Y <- cbind(as.data.frame(Y), classes, np, 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 + p <- ggplot(Y) + + theme_bw() + + labs(x = "", y = "") + + geom_point(aes(x = V1, y = V2, shape = classes, colour = classes)) + + shape_scale + p.np <- ggplot(Y) + + theme_bw() + + labs(x = "", y = "", title = "NP (9)") + + geom_point(aes(x = V1, y = V2, shape = classes, colour = np)) + + shape_scale + color_scale + p.precision <- ggplot(Y) + + theme_bw() + + labs(x = "", y = "", title = "Precision") + + geom_point(aes(x = V1, y = V2, shape = classes, colour = precision)) + + shape_scale + color_scale + p.recall <- ggplot(Y) + + theme_bw() + + labs(x = "", y = "", title = "Recall") + + geom_point(aes(x = V1, y = V2, shape = classes, colour = recall)) + + shape_scale + color_scale + + pdf(paste(output.dir, "original-", suffix, ".pdf", sep=""), width = 16, height = 8) + grid.arrange(p.s, p, p.np, p.precision, p.recall, ncol=3) dev.off() # Perform manipulation + message("\tCalculating Ys.m") 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() + + # 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) - Q <- d2p(Dy, sigmas) - # Calculate measures and plot - np = np - NP(Dx, Dy) - #stress = stress(Dx, Dy), + message("\tCalculating Q") + Q <- d2p(Dy, sigmas) + + # Calculate measures + message("\tCalculating measures") + np <- np - NP(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) + + # Plot results + Ys.m <- cbind(as.data.frame(Ys.m), classes.s) + Y.m <- cbind(as.data.frame(Y.m), classes, np, precision, recall) + p.s <- ggplot(cbind(Ys.m, classes.s)) + + theme_bw() + + labs(x = "", y = "") + + geom_point(aes(x = V1, y = V2, shape = classes.s, colour = classes.s)) + + scale_shape_identity() + + shape_scale + p <- ggplot(cbind(Y.m, classes)) + + theme_bw() + + labs(x = "", y = "") + + geom_point(aes(x = V1, y = V2, shape = classes, colour = classes)) + + shape_scale + p.np <- ggplot(cbind(Y.m, np)) + + theme_bw() + + labs(x = "", y = "", title = "NP (9)") + + geom_point(aes(x = V1, y = V2, shape = classes, colour = np)) + + shape_scale + color_scale + p.precision <- ggplot(cbind(Y.m, precision)) + + theme_bw() + + labs(x = "", y = "", title = "Precision") + + geom_point(aes(x = V1, y = V2, shape = classes, colour = precision)) + + shape_scale + color_scale + p.recall <- ggplot(cbind(Y.m, recall)) + + theme_bw() + + labs(x = "", y = "", title = "Recall") + + geom_point(aes(x = V1, y = V2, shape = classes, colour = recall)) + + shape_scale + color_scale + + pdf(paste(output.dir, "manip-", suffix, ".pdf", sep=""), width = 16, height = 8) + grid.arrange(p.s, p, p.np, p.precision, p.recall, ncol=3) dev.off() } |