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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
}
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)
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")
color_scale <- scale_colour_gradient(high = "#376092", low = "#e46c0a", space = "Lab")
shape_scale <- scale_shape_manual(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
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.silh <- ggplot(Y) +
theme_bw() +
labs(x = "", y = "", title = "Silhouette") +
geom_point(aes(x = V1, y = V2, shape = classes, colour = silh)) +
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 = 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(Dx.s, 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
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")
color_scale <- scale_colour_gradient2(mid = "#dddddd", space = "Lab")
Ys.m <- cbind(as.data.frame(Ys.m), classes.s)
Y.m <- cbind(as.data.frame(Y.m), classes, np, silh, precision, recall)
p.s <- ggplot(Ys.m) +
theme_bw() +
labs(x = "", y = "") +
geom_point(aes(x = V1, y = V2, shape = classes.s, colour = classes.s)) +
shape_scale
p <- ggplot(Y.m) +
theme_bw() +
labs(x = "", y = "") +
geom_point(aes(x = V1, y = V2, shape = classes, colour = classes)) +
shape_scale
p.np <- ggplot(Y.m) +
theme_bw() +
labs(x = "", y = "", title = "NP (9)") +
geom_point(aes(x = V1, y = V2, shape = classes, colour = np)) +
shape_scale + color_scale
p.silh <- ggplot(Y.m) +
theme_bw() +
labs(x = "", y = "", title = "Silhouette") +
geom_point(aes(x = V1, y = V2, shape = classes, colour = silh)) +
shape_scale + color_scale
p.precision <- ggplot(Y.m) +
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.m) +
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 = 12, height = 16)
grid.arrange(p.s, p, p.np, p.silh, p.precision, p.recall, ncol = 2)
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/")
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