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Fix B3 analysis bugs from subagent audit (median + percentile + sweep)

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Three fixes from the B3 audit:

1) joined_analysis.hotspot_index used sorted[n//2] as median, which
   returns the ~60th percentile for n=8 (even-length). Systematically
   under-states the hotspot index. Recomputed values:
       lmetric   2.238 -> 2.253  (+0.7%)
       load_only 1.140 -> 1.294  (+13.5%)
       sticky    2.349 -> 2.728  (+16.1%)
       unified   3.350 -> 3.667  (+9.5%)
       capped    1.937 -> 2.020  (+4.3%)
   Qualitative ranking preserved; "capped only modestly reduces hotspot"
   story holds with ~10% drop instead of the previously reported 13%.
   Added test_hotspot_index_uses_true_median_for_even_n to lock in the
   fix.

2) b3_analyze.sh's pct() helper used floor-indexed percentile
   sorted[int(p*(n-1))], inconsistent with metrics._percentile and
   joined_analysis._percentile which both use linear interpolation.
   Now matches.

3) b3_sweep.sh's capped step called run_policy "capped", but the
   proxy's argparse has no "capped" choice, so the hot-sweep variant
   would have crashed on this step. The actual capped data was
   produced via b3_isolated_policy.sh with --policy lmetric. Replace
   the broken inline call with an explicit launch_proxy lmetric +
   inline replayer block so the sweep script matches the data path
   it documents.

Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
This commit is contained in:
Gahow Wang
2026-05-26 01:08:37 +08:00
parent 8ac41a8684
commit 0e82612100
4 changed files with 64 additions and 4 deletions
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10
scripts/b3_analyze.sh
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@@ -65,9 +65,17 @@ from pathlib import Path
sweep = Path("$SWEEP_DIR")
def pct(vals, p):
# Linear-interpolated percentile, matches metrics._percentile.
# Previously used floor-indexed sorted[int(p*(n-1))] which is
# inconsistent with how the same percentile is computed elsewhere.
if not vals: return None
vs = sorted(vals)
return vs[int(p * (len(vs)-1))]
if len(vs) == 1: return vs[0]
rank = p * (len(vs) - 1)
lo = int(rank)
hi = min(lo + 1, len(vs) - 1)
frac = rank - lo
return vs[lo] * (1 - frac) + vs[hi] * frac
rows = []
for sub in sorted(sweep.iterdir()):