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-rw-r--r--tests.R250
1 files changed, 0 insertions, 250 deletions
diff --git a/tests.R b/tests.R
deleted file mode 100644
index 4c073a0..0000000
--- a/tests.R
+++ /dev/null
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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/")
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