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