mirror of
https://github.com/nodejs/node.git
synced 2024-11-29 23:16:30 +01:00
4897ae2114
PR-URL: https://github.com/nodejs/node/pull/11189 Reviewed-By: Brian White <mscdex@mscdex.net> Reviewed-By: James M Snell <jasnell@gmail.com> Reviewed-By: Michael Dawson <michael_dawson@ca.ibm.com> Reviewed-By: Sakthipriyan Vairamani <thechargingvolcano@gmail.com>
84 lines
2.4 KiB
R
84 lines
2.4 KiB
R
#!/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);
|
|
}
|
|
|
|
# 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));
|
|
|
|
p.value = NA;
|
|
confidence = '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);
|
|
p.value = w$p.value;
|
|
|
|
# Add user friendly stars to the table. There should be at least one star
|
|
# before you can say that there is an improvement.
|
|
confidence = '';
|
|
if (p.value < 0.001) {
|
|
confidence = '***';
|
|
} else if (p.value < 0.01) {
|
|
confidence = '**';
|
|
} else if (p.value < 0.05) {
|
|
confidence = '*';
|
|
}
|
|
}
|
|
|
|
r = list(
|
|
improvement = improvement,
|
|
confidence = confidence,
|
|
p.value = p.value
|
|
);
|
|
return(data.frame(r));
|
|
});
|
|
|
|
|
|
# Set the benchmark names as the row.names to left align them in the print
|
|
row.names(statistics) = statistics$name;
|
|
statistics$name = NULL;
|
|
|
|
options(width = 200);
|
|
print(statistics);
|