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Unified Diff: tools/perf/statistics-for-json.R

Issue 1659043002: Add an R script that does some statistical tests on benchmark results (Closed) Base URL: https://chromium.googlesource.com/v8/v8.git@master
Patch Set: Add example command line Created 4 years, 10 months ago
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Index: tools/perf/statistics-for-json.R
diff --git a/tools/perf/statistics-for-json.R b/tools/perf/statistics-for-json.R
new file mode 100644
index 0000000000000000000000000000000000000000..fde2cd75db128e9593fdf0d9c82c3e021b3db97b
--- /dev/null
+++ b/tools/perf/statistics-for-json.R
@@ -0,0 +1,113 @@
+# Copyright 2016 the V8 project authors. All rights reserved.
+# Use of this source code is governed by a BSD-style license that can be
+# found in the LICENSE file.
+
+# Do statistical tests on benchmark results
+# This script requires the libraries rjson, R.utils, ggplot2 and data.table
+# Install them prior to running
+
+# To use the script, first get some benchmark results, for example via
+# tools/run_perf.py ../v8-perf/benchmarks/Octane2.1/Octane2.1-TF.json
+# --outdir=out/x64.release-on --outdir-no-patch=out/x64.release-off
+# --json-test-results=results-on.json
+# --json-test-results-no-patch=results-off.json
+# then run this script
+# Rscript statistics-for-json.R results-on.json results-off.json ~/SVG
+# to produce graphs (and get stdio output of statistical tests).
+
+
+suppressMessages(library("rjson")) # for fromJson
+suppressMessages(library("R.utils")) # for printf
+suppressMessages(library("ggplot2")) # for plotting
+suppressMessages(library("data.table")) # less broken than data.frame
+
+# Clear all variables from environment
+rm(list=ls())
+
+args <- commandArgs(TRUE)
+if (length(args) != 3) {
+ printf(paste("usage: Rscript %%this_script patched-results.json",
+ "unpatched-results.json\n"))
+} else {
+ patch <- fromJSON(file=args[1])
+ nopatch <- fromJSON(file=args[2])
+ outputPath <- args[3]
+ df <- data.table(L = numeric(), R = numeric(), E = numeric(),
+ p.value = numeric(), yL = character(),
+ p.value.sig = logical())
+
+ for (i in seq(1, length(patch$traces))) {
+ testName <- patch$traces[[i]]$graphs[[2]]
+ printf("%s\n", testName)
+
+ nopatch_res <- as.integer(nopatch$traces[[i]]$results)
+ patch_res <- as.integer(patch$traces[[i]]$results)
+ if (length(nopatch_res) > 0) {
+ patch_norm <- shapiro.test(patch_res);
+ nopatch_norm <- shapiro.test(nopatch_res);
+
+ # Shaprio-Wilk test indicates whether data is not likely to
+ # come from a normal distribution. The p-value is the probability
+ # to obtain the sample from a normal distribution. This means, the
+ # smaller p, the more likely the sample was not drawn from a normal
+ # distribution. See [wikipedia:Shapiro-Wilk-Test].
+ printf(" Patched scores look %s distributed (W=%.4f, p=%.4f)\n",
+ ifelse(patch_norm$p.value < 0.05, "not normally", "normally"),
+ patch_norm$statistic, patch_norm$p.value);
+ printf(" Unpatched scores look %s distributed (W=%.4f, p=%.4f)\n",
+ ifelse(nopatch_norm$p.value < 0.05, "not normally", "normally"),
+ nopatch_norm$statistic, nopatch_norm$p.value);
+
+ hist <- ggplot(data=data.frame(x=as.integer(patch_res)), aes(x)) +
+ theme_bw() +
+ geom_histogram(bins=50) +
+ ylab("Points") +
+ xlab(patch$traces[[i]]$graphs[[2]])
+ ggsave(filename=sprintf("%s/%s.svg", outputPath, testName),
+ plot=hist, width=7, height=7)
+
+ hist <- ggplot(data=data.frame(x=as.integer(nopatch_res)), aes(x)) +
+ theme_bw() +
+ geom_histogram(bins=50) +
+ ylab("Points") +
+ xlab(patch$traces[[i]]$graphs[[2]])
+ ggsave(filename=sprintf("%s/%s-before.svg", outputPath, testName),
+ plot=hist, width=7, height=7)
+
+ # The Wilcoxon rank-sum test
+ mww <- wilcox.test(patch_res, nopatch_res, conf.int = TRUE, exact=TRUE)
+ printf(paste(" Wilcoxon U-test W=%.4f, p=%.4f,",
+ "confidence interval [%.1f, %.1f],",
+ "est. effect size %.1f \n"),
+ mww$statistic, mww$p.value,
+ mww$conf.int[1], mww$conf.int[2], mww$estimate);
+ df <-rbind(df, list(mww$conf.int[1], mww$conf.int[2],
+ unname(mww$estimate), unname(mww$p.value),
+ testName, ifelse(mww$p.value < 0.05, TRUE, FALSE)))
+ # t-test
+ t <- t.test(patch_res, nopatch_res, paired=FALSE)
+ printf(paste(" Welch t-test t=%.4f, df = %.2f, p=%.4f,",
+ "confidence interval [%.1f, %.1f], mean diff %.1f \n"),
+ t$statistic, t$parameter, t$p.value,
+ t$conf.int[1], t$conf.int[2], t$estimate[1]-t$estimate[2]);
+ }
+ }
+ df2 <- cbind(x=1:nrow(df), df[order(E),])
+ speedup <- ggplot(df2, aes(x = x, y = E, colour=p.value.sig)) +
+ geom_errorbar(aes(ymax = L, ymin = R), colour="black") +
+ geom_point(size = 4) +
+ scale_x_discrete(limits=df2$yL,
+ name=paste("Benchmark, n=", length(patch_res))) +
+ theme_bw() +
+ geom_hline(yintercept = 0) +
+ ylab("Est. Effect Size in Points") +
+ theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust=0.5)) +
+ theme(legend.position = "bottom") +
+ scale_colour_manual(name="Statistical Significance (MWW, p < 0.05)",
+ values=c("red", "green"),
+ labels=c("not significant", "significant")) +
+ theme(legend.justification=c(0,1), legend.position=c(0,1))
+ print(speedup)
+ ggsave(filename=sprintf("%s/speedup-estimates.svg", outputPath),
+ plot=speedup, width=7, height=7)
+}
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