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| 1 #!/usr/bin/env python |
| 2 |
| 3 import sys |
| 4 from scipy.stats import mannwhitneyu |
| 5 |
| 6 SIGNIFICANCE_THRESHOLD = 0.0001 |
| 7 |
| 8 a,b = {},{} |
| 9 for (path, d) in [(sys.argv[1], a), (sys.argv[2], b)]: |
| 10 for line in open(path): |
| 11 try: |
| 12 tokens = line.split() |
| 13 samples = tokens[:-1] |
| 14 label = tokens[-1] |
| 15 d[label] = map(float, samples) |
| 16 except: |
| 17 pass |
| 18 |
| 19 common = set(a.keys()).intersection(b.keys()) |
| 20 |
| 21 ps = [] |
| 22 for key in common: |
| 23 _, p = mannwhitneyu(a[key], b[key]) # Non-parametric t-test. Doesn't ass
ume normal dist. |
| 24 am, bm = min(a[key]), min(b[key]) |
| 25 ps.append((bm/am, p, key, am, bm)) |
| 26 ps.sort(reverse=True) |
| 27 |
| 28 def humanize(ns): |
| 29 for threshold, suffix in [(1e9, 's'), (1e6, 'ms'), (1e3, 'us'), (1e0, 'ns')]
: |
| 30 if ns > threshold: |
| 31 return "%.3g%s" % (ns/threshold, suffix) |
| 32 |
| 33 maxlen = max(map(len, common)) |
| 34 |
| 35 # We print only signficant changes in benchmark timing distribution. |
| 36 bonferroni = SIGNIFICANCE_THRESHOLD / len(ps) # Adjust for the fact we've run m
ultiple tests. |
| 37 for ratio, p, key, am, bm in ps: |
| 38 if p < bonferroni: |
| 39 print '%*s\t%6s -> %6s\t%.2gx' % (maxlen, key, humanize(am), humanize(bm
), ratio) |
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