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# Functions for plotting results from manipulation experiments.
# NOTE: This script should only be used after results are generated with run.R
require(cowplot)
require(gridExtra)
require(logging)
# Plots results for experiments of all techniques for a given measure and
# datasset.
plot.measure <- function(ds, techniques, output.dir, measure, n.iter=30) {
measure.df <- data.frame()
scale.labels <- c()
for (tech in techniques) {
Y.measure <- read.table(file.path(output.dir, ds$name, tech$name, paste(measure$name, "Y.tbl", sep="-")))
Ym.measure <- read.table(file.path(output.dir, ds$name, tech$name, paste(measure$name, "Ym.tbl", sep="-")))
measure.df <- rbind(measure.df,
data.frame(name=tech$name.pretty,
type=paste("Y", tech$name, sep="."),
V1=Ym.measure - Y.measure)
)
scale.labels <- c(scale.labels, tech$name.pretty)
}
p <- ggplot(measure.df) +
background_grid(major="xy", minor="none") +
theme(legend.position="none") +
labs(x="", y=measure$name.pretty) +
geom_boxplot(aes(type, V1, fill=name)) +
scale_fill_brewer(palette="Set1", guide=guide_legend(title="")) +
scale_y_continuous(limits=c(min(0, min(measure.df$V1)), max(0, max(measure.df$V1)))) +
scale_x_discrete(labels=scale.labels)
fname <- file.path(output.dir, "plots", ds$name, paste(measure$name, "pdf", sep="."))
loginfo("Saving plot: %s", fname)
save_plot(fname, p, base_aspect_ratio=1.3)
}
# Plot boxplots of techniques, one per measure diff per dataset.
plot.measures <- function(datasets, techniques, measures, output.dir, n.iter=30) {
dir.create.safe(file.path(output.dir, "plots"))
for (ds in datasets) {
dir.create.safe(file.path(output.dir, "plots", ds$name))
for (measure in measures) {
if (is.null(ds$labels.file) && measure$name == "silhouette") {
next
}
plot.measure(ds, techniques, output.dir, measure, n.iter)
}
}
}
# Same as above, but averages over all datasets.
plot.averages <- function(datasets, techniques, measures, output.dir, n.iter=30) {
dir.create.safe(file.path(output.dir, "plots"))
for (measure in measures) {
measure.df <- data.frame()
scale.labels <- c()
for (tech in techniques) {
measure.avg <- rep(0, n.iter)
scale.labels <- c(scale.labels, tech$name.pretty)
for (ds in datasets) {
if (is.null(ds$labels.file) && measure$name == "silhouette") {
next
}
Y.measure <- read.table(file.path(output.dir, ds$name, tech$name, paste(measure$name, "Y.tbl", sep="-")))$V1
Ym.measure <- read.table(file.path(output.dir, ds$name, tech$name, paste(measure$name, "Ym.tbl", sep="-")))$V1
measure.avg <- measure.avg + (Ym.measure - Y.measure)
}
measure.avg <- measure.avg / length(datasets)
measure.df <- rbind(measure.df, data.frame(tech=tech$name, V1=measure.avg))
}
p <- ggplot(measure.df) +
background_grid(major="xy", minor="none") +
theme(legend.position="none") +
labs(x="", y=measure$name.pretty) +
geom_boxplot(aes(tech, V1, fill=tech)) +
scale_fill_brewer(palette="Set1", guide=guide_legend(title="")) +
scale_y_continuous(limits=c(min(0, min(measure.df$V1)), max(0, max(measure.df$V1)))) +
scale_x_discrete(labels=scale.labels)
fname <- file.path(output.dir, "plots", paste(measure$name, "pdf", sep="."))
loginfo("Saving plot: %s", fname)
save_plot(fname, p, base_aspect_ratio=1.3)
}
}
# Plot a single scatterplot of techniques and datasets, where x axis is the
# measure before manipulation and y axis is the measure after manipulation.
# Also adds a y=x line so that visual inspection is easier.
plot.scatter.measure <- function(measure, datasets, techniques, output.dir, n.iter=30) {
measure.df <- data.frame()
for (tech in techniques) {
for (ds in datasets) {
if (is.null(ds$labels.file) && measure$name == "silhouette") {
next
}
base.path <- file.path(output.dir, ds$name, tech$name)
fname <- file.path(base.path, paste(measure$name, "Y.tbl", sep="-"))
Y.measure <- read.table(fname)$V1
fname <- file.path(base.path, paste(measure$name, "Ym.tbl", sep="-"))
Ym.measure <- read.table(fname)$V1
measure.df <- rbind(measure.df, data.frame(tech=tech$name.pretty,
dataset=ds$name.pretty,
x=mean(Y.measure),
y=mean(Ym.measure)))
}
}
min.max <- min(max(measure.df$x), max(measure.df$y))
p <- ggplot(measure.df) +
background_grid(major="xy", minor="none") +
theme(legend.position="right") +
labs(x=paste(measure$name.pretty, "(before)", sep=" "),
y=paste(measure$name.pretty, "(after)", sep=" ")) +
geom_point(aes(x=x, y=y, color=tech, shape=dataset), alpha=0.8, size=3) +
scale_color_brewer(palette="Set1", guide=guide_legend(title="Technique")) +
scale_shape(guide=guide_legend(title="Dataset")) +
geom_abline(intercept=0, slope=1)
fname <- file.path(output.dir, "plots", paste(measure$name, "-scatter", ".pdf", sep=""))
loginfo("Saving plot: %s", fname)
save_plot(fname, p, base_aspect_ratio=1.5)
p
}
# This function runs the scatterplot function above for all measures
plot.scatter <- function(datasets, techniques, measures, output.dir, n.iter=30) {
dir.create.safe(file.path(output.dir, "plots"))
for (measure in measures) {
p <- plot.scatter.measure(measure, datasets, techniques, output.dir, n.iter)
}
}
# Experiment configuration
# Defines: datasets, techniques, measures, output.dir
source("config.R")
args <- commandArgs(T)
# Logging setup
basicConfig()
addHandler(writeToFile,
file=args[1],
level="FINEST")
plot.measures(datasets, techniques, measures, output.dir)
plot.averages(datasets, techniques, measures, output.dir)
plot.scatter(datasets, techniques, measures, output.dir)
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