[benchmarks] Add R script for statistical tests on benchmark results

NOTRY=true

Review URL: https://codereview.chromium.org/1659043002

Cr-Commit-Position: refs/heads/master@{#33769}

tools/perf/statistics-for-json.R

+# 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)
+}