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Correctness/completeness review of the report and simulator. Verified against the committed parquets: the sec 6.6 countable-ceiling table (cap-64 MDP sweep), sec 6.10 Result 4's depth ceilings, the sec 3.4 uncle-selection table, all adversary-variant numbers, the rho-boundary row-4 quotes (0.976 at rho=0.91, 4-sigma shortfall at 0.96, max cell 1.0024), and the sec 8.4 capstone table. Three defects found, all fixed: 1. The collapse event was not reproducible from the committed script. Study D swept only the default (random) coalition, but the one observed collapse is a whale cell; the "once in 144 runs" count came from an ad-hoc probe. The committed sweep now carries the selection axis (96 runs) and reproduces the event: 1/12 in the whale 50% cell at delta_max = 8, never at 4. All six fold-related passages now quote the committed sweep, which also retires the stale "the full dynamics never reach it" wording in the sec 6 arc, the sec 6.2 intro, row 6 and item 1 -- text that contradicted item 18 since yesterday's finding. 2. capstone.py's printout could not reproduce the report's sec 8.4 table. The report's numbers are a per-replicate-tail aggregation (each replicate burns in against its own early-stop length); the script cut the tail at the ARM's max epoch, silently dropping any replicate that stopped earlier (7 of 8 in the adversary arm) and landing one rounding step off on three cells. The script now aggregates per replicate and prints the SEM; against the existing parquet it reproduces the table exactly (1.001/0.998, 0.342+-0.009 / 0.343+-0.005, p_ref 1.000/0.990, 8 reps both arms). The report table was right all along; sec 6.8's p_ref quote (0.989, the per-arm value) is aligned to 0.990. 3. Small report fixes: slow-beta deflation rounded 0.765 -> "0.77" (now 0.76); fig13's caption now points at the fig36 ceiling instead of implying free recovery; row 5 cites the measured slow-beta standing deflation; the canonical-data paragraph lists the new studies' artifacts; the simulator README's layout block lists the new tests and scripts. Adds a unit test for reorg.countable_recovery_from_depths (the one new function that had none). 236 tests pass; the new-study parquets are copied to the main checkout's runs/, where every other study's data of record lives. Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
60 lines
2.7 KiB
Python
60 lines
2.7 KiB
Python
#!/usr/bin/env python
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"""Capstone: the recommended configuration end-to-end at true k=2160 (report §8).
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One config — f=1/30, W=300, U=2, β=1, degree 6, Blend 3 hops × 8 s, Pareto stake — run honest
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and under a 30 % uncle-suppression adversary, confirming accuracy, consensus, fork rate, reorg
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depth, and the emergent reference rate p_ref ALL hold together. Writes runs/capstone.parquet.
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"""
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from __future__ import annotations
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import sys
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from pathlib import Path
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sys.path.insert(0, str(Path(__file__).resolve().parents[1] / "src"))
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import pandas as pd
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from joblib import Parallel, delayed
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from tsi_sim.config import SimConfig
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from tsi_sim.engine import run_trajectory
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REC = dict(n_nodes=1000, stake_dist="pareto", topology="blend", degree=6,
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link_latency_mean=0.5, link_latency_dist="geo", blend_hops=3, blend_delay_max=8.0,
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max_uncles=2, uncle_window=300, uncle_strategy="oldest", k=2160, epochs=40,
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genesis_d_factor=0.5, early_stop=True)
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def _one(adv: float, rep: int) -> list[dict]:
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cfg = SimConfig(**REC, adversary_frac=adv, adversary_strategy="suppress", replicate=rep)
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rows = run_trajectory(cfg)
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for r in rows:
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r["adv"] = adv
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return rows
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def main() -> None:
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out = Path(__file__).resolve().parents[1] / "runs"
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jobs = [(a, r) for a in (0.0, 0.3) for r in range(8)]
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res = Parallel(n_jobs=4, backend="loky", inner_max_num_threads=1)(
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delayed(_one)(a, r) for a, r in jobs)
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df = pd.DataFrame([row for traj in res for row in traj])
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df.to_parquet(out / "capstone.parquet", index=False)
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print("=== Capstone: recommended config, all metrics together (equilibrium tail) ===")
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for adv, g in df.groupby("adv"):
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# Per-REPLICATE tail: early_stop ends replicates at different epochs, so a per-arm cut
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# (epoch >= arm_max//2) would silently drop any replicate that stopped before the cut
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# and skew the tail toward the slow-converging ones. The report's §8.4 numbers are the
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# per-replicate aggregation; keep this printout matching them.
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t = pd.concat([r[r.epoch >= r.epoch.max() // 2] for _, r in g.groupby("replicate")])
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per_rep = t.groupby("replicate").fork_rate.mean()
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sem = per_rep.std(ddof=1) / (len(per_rep) ** 0.5)
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print(f"adversary {adv:.0%}: D̂/D {t.mean_ratio.mean():.4f} "
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f"range_ratio {t.range_ratio.max():.4f} agreement {t.agreement_window.min():.4f} "
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f"fork_rate {per_rep.mean():.3f}+-{sem:.3f}(SEM over {len(per_rep)} reps) "
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f"max_reorg_depth {t.max_reorg_depth.max()} p_ref {t.p_ref.mean():.3f}")
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print(f"wrote {out/'capstone.parquet'} ({len(df)} rows)")
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if __name__ == "__main__":
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main()
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