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923f6ac08f
PR-URL: https://github.com/nodejs/node/pull/21085 Reviewed-By: Benjamin Gruenbaum <benjamingr@gmail.com> Reviewed-By: Lance Ball <lball@redhat.com> Reviewed-By: Luigi Pinca <luigipinca@gmail.com> Reviewed-By: Rich Trott <rtrott@gmail.com> Reviewed-By: Richard Lau <riclau@uk.ibm.com>
121 lines
3.9 KiB
R
121 lines
3.9 KiB
R
#!/usr/bin/env Rscript
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library(ggplot2);
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library(plyr);
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# get __dirname and load ./_cli.R
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args = commandArgs(trailingOnly = F);
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dirname = dirname(sub("--file=", "", args[grep("--file", args)]));
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source(paste0(dirname, '/_cli.R'), chdir=T);
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if (!is.null(args.options$help) ||
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(!is.null(args.options$plot) && args.options$plot == TRUE)) {
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stop("usage: cat file.csv | Rscript compare.R
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--help show this message
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--plot filename save plot to filename");
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}
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plot.filename = args.options$plot;
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dat = read.csv(
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file('stdin'),
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colClasses=c('character', 'character', 'character', 'numeric', 'numeric')
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);
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dat = data.frame(dat);
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dat$nameTwoLines = paste0(dat$filename, '\n', dat$configuration);
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dat$name = paste0(dat$filename, dat$configuration);
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# Create a box plot
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if (!is.null(plot.filename)) {
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p = ggplot(data=dat);
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p = p + geom_boxplot(aes(x=nameTwoLines, y=rate, fill=binary));
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p = p + ylab("rate of operations (higher is better)");
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p = p + xlab("benchmark");
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p = p + theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5));
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ggsave(plot.filename, p);
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}
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# computes the shared standard error, as used in the welch t-test
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welch.sd = function (old.rate, new.rate) {
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old.se.squared = var(old.rate) / length(old.rate)
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new.se.squared = var(new.rate) / length(new.rate)
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return(sqrt(old.se.squared + new.se.squared))
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}
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# calculate the improvement confidence interval. The improvement is calculated
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# by dividing by old.mu and not new.mu, because old.mu is what the mean
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# improvement is calculated relative to.
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confidence.interval = function (shared.se, old.mu, w, risk) {
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interval = qt(1 - (risk / 2), w$parameter) * shared.se;
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return(sprintf("±%.2f%%", (interval / old.mu) * 100))
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}
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# Print a table with results
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statistics = ddply(dat, "name", function(subdat) {
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old.rate = subset(subdat, binary == "old")$rate;
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new.rate = subset(subdat, binary == "new")$rate;
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# Calculate improvement for the "new" binary compared with the "old" binary
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old.mu = mean(old.rate);
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new.mu = mean(new.rate);
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improvement = sprintf("%.2f %%", ((new.mu - old.mu) / old.mu * 100));
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r = list(
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confidence = "NA",
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improvement = improvement,
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"accuracy (*)" = "NA",
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"(**)" = "NA",
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"(***)" = "NA"
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);
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# Check if there is enough data to calculate the calculate the p-value
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if (length(old.rate) > 1 && length(new.rate) > 1) {
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# Perform a statistics test to see of there actually is a difference in
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# performance.
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w = t.test(rate ~ binary, data=subdat);
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shared.se = welch.sd(old.rate, new.rate)
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# Add user friendly stars to the table. There should be at least one star
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# before you can say that there is an improvement.
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confidence = '';
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if (w$p.value < 0.001) {
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confidence = '***';
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} else if (w$p.value < 0.01) {
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confidence = '**';
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} else if (w$p.value < 0.05) {
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confidence = '*';
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}
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r = list(
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confidence = confidence,
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improvement = improvement,
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"accuracy (*)" = confidence.interval(shared.se, old.mu, w, 0.05),
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"(**)" = confidence.interval(shared.se, old.mu, w, 0.01),
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"(***)" = confidence.interval(shared.se, old.mu, w, 0.001)
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);
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}
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return(data.frame(r, check.names=FALSE));
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});
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# Set the benchmark names as the row.names to left align them in the print
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row.names(statistics) = statistics$name;
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statistics$name = NULL;
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options(width = 200);
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print(statistics);
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cat("\n")
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cat(sprintf(
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"Be aware that when doing many comparisons the risk of a false-positive
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result increases. In this case there are %d comparisons, you can thus
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expect the following amount of false-positive results:
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%.2f false positives, when considering a 5%% risk acceptance (*, **, ***),
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%.2f false positives, when considering a 1%% risk acceptance (**, ***),
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%.2f false positives, when considering a 0.1%% risk acceptance (***)
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",
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nrow(statistics),
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nrow(statistics) * 0.05,
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nrow(statistics) * 0.01,
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nrow(statistics) * 0.001))
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