Updated with all the code used to run experiments.

1 files changed, 160 insertions, 0 deletions

diff --git a/plot.R b/plot.R
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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)