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Copy pathcompare.R
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120 lines (102 loc) · 3.89 KB
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#!/usr/bin/env Rscript
library(ggplot2);
library(plyr);
# get __dirname and load ./_cli.R
args = commandArgs(trailingOnly = F);
dirname = dirname(sub("--file=", "", args[grep("--file", args)]));
source(paste0(dirname, '/_cli.R'), chdir=T);
if (!is.null(args.options$help) ||
(!is.null(args.options$plot) && args.options$plot == TRUE)) {
stop("usage: cat file.csv | Rscript compare.R
--help show this message
--plot filename save plot to filename");
}
plot.filename = args.options$plot;
dat = read.csv(
file('stdin'),
colClasses=c('character', 'character', 'character', 'numeric', 'numeric')
);
dat = data.frame(dat);
dat$nameTwoLines = paste0(dat$filename, '\n', dat$configuration);
dat$name = paste0(dat$filename, dat$configuration);
# Create a box plot
if (!is.null(plot.filename)) {
p = ggplot(data=dat);
p = p + geom_boxplot(aes(x=nameTwoLines, y=rate, fill=binary));
p = p + ylab("rate of operations (higher is better)");
p = p + xlab("benchmark");
p = p + theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5));
ggsave(plot.filename, p);
}
# computes the shared standard error, as used in the welch t-test
welch.sd = function (old.rate, new.rate) {
old.se.squared = var(old.rate) / length(old.rate)
new.se.squared = var(new.rate) / length(new.rate)
return(sqrt(old.se.squared + new.se.squared))
}
# calculate the improvement confidence interval. The improvement is calculated
# by dividing by old.mu and not new.mu, because old.mu is what the mean
# improvement is calculated relative to.
confidence.interval = function (shared.se, old.mu, w, risk) {
interval = qt(1 - (risk / 2), w$parameter) * shared.se;
return(sprintf("±%.2f%%", (interval / old.mu) * 100))
}
# Print a table with results
statistics = ddply(dat, "name", function(subdat) {
old.rate = subset(subdat, binary == "old")$rate;
new.rate = subset(subdat, binary == "new")$rate;
# Calculate improvement for the "new" binary compared with the "old" binary
old.mu = mean(old.rate);
new.mu = mean(new.rate);
improvement = sprintf("%.2f %%", ((new.mu - old.mu) / old.mu * 100));
r = list(
confidence = "NA",
improvement = improvement,
"accuracy (*)" = "NA",
"(**)" = "NA",
"(***)" = "NA"
);
# Check if there is enough data to calculate the calculate the p-value
if (length(old.rate) > 1 && length(new.rate) > 1) {
# Perform a statistics test to see of there actually is a difference in
# performance.
w = t.test(rate ~ binary, data=subdat);
shared.se = welch.sd(old.rate, new.rate)
# Add user friendly stars to the table. There should be at least one star
# before you can say that there is an improvement.
confidence = '';
if (w$p.value < 0.001) {
confidence = '***';
} else if (w$p.value < 0.01) {
confidence = '**';
} else if (w$p.value < 0.05) {
confidence = '*';
}
r = list(
confidence = confidence,
improvement = improvement,
"accuracy (*)" = confidence.interval(shared.se, old.mu, w, 0.05),
"(**)" = confidence.interval(shared.se, old.mu, w, 0.01),
"(***)" = confidence.interval(shared.se, old.mu, w, 0.001)
);
}
return(data.frame(r, check.names=FALSE));
});
# Set the benchmark names as the row.names to left align them in the print
row.names(statistics) = statistics$name;
statistics$name = NULL;
options(width = 200);
print(statistics);
cat("\n")
cat(sprintf(
"Be aware that when doing many comparisons the risk of a false-positive
result increases. In this case there are %d comparisons, you can thus
expect the following amount of false-positive results:
%.2f false positives, when considering a 5%% risk acceptance (*, **, ***),
%.2f false positives, when considering a 1%% risk acceptance (**, ***),
%.2f false positives, when considering a 0.1%% risk acceptance (***)
",
nrow(statistics),
nrow(statistics) * 0.05,
nrow(statistics) * 0.01,
nrow(statistics) * 0.001))