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54 lines
2.1 KiB
Python
54 lines
2.1 KiB
Python
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#!/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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t = g[g.epoch >= g.epoch.max() // 2]
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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 {t.fork_rate.mean():.3f} max_reorg_depth {t.max_reorg_depth.max()} "
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f"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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