mfowler
33eeb3ce6b
Standalone analysis over RESULTS-campaign.md.data (safe: independent of the live runner). Adds the normalised efficiency ratios per run with min/median/max per variant, alongside the token distributions, commit/LOC medians, correlations, and full raw table. Run: python3 analyze.py (regenerates RESULTS-campaign.md). Orig baseline (5 runs): tokens/LOC ~25k–34k, tokens/sec ~11.3k–14.0k.
100 lines
5.0 KiB
Python
Executable File
100 lines
5.0 KiB
Python
Executable File
#!/usr/bin/env python3
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"""Analyse the campaign data file into RESULTS-campaign.md.
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Reads RESULTS-campaign.md.data (TSV: variant rep success builder adversary total
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duration_s commits loc) and writes a full report: per-variant token distributions,
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median commits/LOC/duration, the normalised EFFICIENCY RATIOS (tokens/LOC,
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tokens/sec, tokens/commit) with min–median–max ranges, correlations, and the full
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raw per-run table. Stats are over SUCCESSFUL runs.
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Usage: python3 analyze.py [data-file] [out-file]
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"""
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import sys, statistics as st
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DATA = sys.argv[1] if len(sys.argv) > 1 else "RESULTS-campaign.md.data"
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OUT = sys.argv[2] if len(sys.argv) > 2 else "RESULTS-campaign.md"
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rows = []
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for line in open(DATA):
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p = line.rstrip("\n").split("\t")
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if len(p) != 9:
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continue
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v, rep, ok, b, a, tot, dur, commits, loc = p
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r = dict(v=v, rep=int(rep), ok=ok, builder=int(b), adversary=int(a),
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total=int(tot), dur=int(dur), commits=int(commits), loc=int(loc))
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r["tok_per_loc"] = r["total"] / r["loc"] if r["loc"] else 0.0
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r["tok_per_sec"] = r["total"] / r["dur"] if r["dur"] else 0.0
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r["tok_per_commit"] = r["total"] / r["commits"] if r["commits"] else 0.0
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rows.append(r)
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variants = []
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for r in rows:
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if r["v"] not in variants:
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variants.append(r["v"])
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ok_rows = [r for r in rows if r["ok"] == "YES"]
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def f(n): return f"{n:,}"
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def fr(x): return f"{x:,.0f}"
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def frr(x): return f"{x:.2f}"
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def mmm(xs, fn=fr):
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if not xs: return "— / — / —"
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return f"{fn(min(xs))} / {fn(int(st.median(xs)) if all(float(x).is_integer() for x in xs) else st.median(xs))} / {fn(max(xs))}"
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def pearson(a, b):
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if len(a) < 2: return float("nan")
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ma, mb = st.mean(a), st.mean(b)
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den = (sum((x-ma)**2 for x in a) * sum((y-mb)**2 for y in b)) ** 0.5
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return (sum((x-ma)*(y-mb) for x, y in zip(a, b)) / den) if den else float("nan")
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with open(OUT, "w") as o:
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o.write("# Full-harness benchmark — campaign analysis\n\n")
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o.write("Real `agents.py up` Builder/Adversary loop pair + watchdog through the 3-phase calculator "
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"to SEQUENCE-COMPLETE. Both loops on Sonnet. Tokens summed from each loop's session "
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"transcript; commits = work-repo commit count; LOC = non-blank `calc/*.py` lines (code + "
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f"tests). {len(ok_rows)} successful runs of {len(rows)} total.\n\n")
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o.write("## Per-variant total tokens (successful runs)\n\n")
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o.write("| variant | runs(ok) | median | mean | min | max | spread |\n|---|:--:|--:|--:|--:|--:|--:|\n")
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for v in variants:
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ts = [r["total"] for r in ok_rows if r["v"] == v]
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n = sum(1 for r in rows if r["v"] == v)
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if not ts: o.write(f"| {v} | 0/{n} | — | — | — | — | — |\n"); continue
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sp = f"{max(ts)/min(ts):.2f}x" if min(ts) else "—"
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o.write(f"| {v} | {len(ts)}/{n} | {f(int(st.median(ts)))} | {f(int(st.mean(ts)))} | "
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f"{f(min(ts))} | {f(max(ts))} | {sp} |\n")
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o.write("\n## Efficiency ratios — min / median / max (successful runs)\n\n")
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o.write("| variant | tokens / LOC | tokens / sec | tokens / commit |\n|---|--:|--:|--:|\n")
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for v in variants:
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sub = [r for r in ok_rows if r["v"] == v]
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if not sub: o.write(f"| {v} | — | — | — |\n"); continue
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o.write(f"| {v} | {mmm([r['tok_per_loc'] for r in sub])} | "
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f"{mmm([r['tok_per_sec'] for r in sub])} | "
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f"{mmm([r['tok_per_commit'] for r in sub])} |\n")
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if ok_rows:
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o.write(f"| **all** | {mmm([r['tok_per_loc'] for r in ok_rows])} | "
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f"{mmm([r['tok_per_sec'] for r in ok_rows])} | "
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f"{mmm([r['tok_per_commit'] for r in ok_rows])} |\n")
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o.write("\n## Per-variant medians (commits / LOC / duration)\n\n")
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o.write("| variant | median commits | median LOC | median dur(s) |\n|---|--:|--:|--:|\n")
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for v in variants:
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sub = [r for r in ok_rows if r["v"] == v]
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if not sub: o.write(f"| {v} | — | — | — |\n"); continue
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o.write(f"| {v} | {int(st.median([r['commits'] for r in sub]))} | "
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f"{int(st.median([r['loc'] for r in sub]))} | {int(st.median([r['dur'] for r in sub]))} |\n")
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o.write(f"\n## Correlations with total tokens (pooled, n={len(ok_rows)})\n\n")
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o.write("| tokens vs | Pearson r |\n|---|--:|\n")
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for k, lab in [("dur", "duration"), ("commits", "commits"), ("loc", "LOC")]:
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o.write(f"| {lab} | {pearson([r['total'] for r in ok_rows], [r[k] for r in ok_rows]):+.2f} |\n")
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o.write("\n## All runs (raw)\n\n")
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o.write("| variant | rep | ok | total | dur(s) | commits | LOC | tok/LOC | tok/sec | tok/commit |\n")
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o.write("|---|:--:|:--:|--:|--:|--:|--:|--:|--:|--:|\n")
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for r in rows:
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o.write(f"| {r['v']} | {r['rep']} | {r['ok']} | {f(r['total'])} | {r['dur']} | {r['commits']} | "
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f"{r['loc']} | {fr(r['tok_per_loc'])} | {fr(r['tok_per_sec'])} | {fr(r['tok_per_commit'])} |\n")
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o.write("\n_Stats over successful runs. Repos kept under the run root for analysis._\n")
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print(f"wrote {OUT} ({len(rows)} runs, {len(ok_rows)} ok)")
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