mirror of
https://github.com/logos-blockchain/research.git
synced 2026-08-07 19:53:10 +00:00
79 lines
3.4 KiB
Python
79 lines
3.4 KiB
Python
|
#!/usr/bin/env python
|
||
|
|
"""Organic honest stake churn: does TSI track active stake within-epoch? (report §6.x, fig29).
|
||
|
|
|
||
|
|
The active honest stake oscillates (sine, weekly cycle), ramps, or steps down; TSI should
|
||
|
|
track it with a one-epoch lag (β=1, §6.5 EMA law). We measure D̂/D_active (should stay ~1)
|
||
|
|
and D̂/D_total (follows the active fraction), plus the fork rate the transient induces.
|
||
|
|
Blend, U=2, degree 6, k=256. Writes runs/churn.parquet and fig29_churn.
|
||
|
|
"""
|
||
|
|
from __future__ import annotations
|
||
|
|
|
||
|
|
import sys
|
||
|
|
from pathlib import Path
|
||
|
|
|
||
|
|
sys.path.insert(0, str(Path(__file__).resolve().parents[1] / "src"))
|
||
|
|
|
||
|
|
import pandas as pd
|
||
|
|
from joblib import Parallel, delayed
|
||
|
|
|
||
|
|
from tsi_sim.config import SimConfig
|
||
|
|
from tsi_sim.engine import run_trajectory
|
||
|
|
from tsi_sim.plotting import style
|
||
|
|
|
||
|
|
BASE = dict(n_nodes=1000, stake_dist="pareto", topology="blend", degree=6,
|
||
|
|
link_latency_mean=0.5, link_latency_dist="geo", blend_hops=3, blend_delay_max=8.0,
|
||
|
|
max_uncles=2, uncle_window=300, k=256, epochs=40, genesis_d_factor=0.5)
|
||
|
|
GRID = [(m, p, r) for m in ("sine", "ramp", "step") for p in (2, 4, 8) for r in range(4)]
|
||
|
|
|
||
|
|
|
||
|
|
def _one(mode: str, period: int, rep: int) -> list[dict]:
|
||
|
|
cfg = SimConfig(**BASE, churn_amp=0.3, churn_period=period, churn_mode=mode, replicate=rep)
|
||
|
|
rows = run_trajectory(cfg)
|
||
|
|
for r in rows:
|
||
|
|
r["mode"], r["period"] = mode, period
|
||
|
|
return rows
|
||
|
|
|
||
|
|
|
||
|
|
def fig29(df: pd.DataFrame) -> None:
|
||
|
|
import matplotlib.pyplot as plt
|
||
|
|
style.apply_style()
|
||
|
|
fig, axes = plt.subplots(1, 3, figsize=(13.2, 4.0), sharey=True)
|
||
|
|
for ax, mode in zip(axes, ("sine", "ramp", "step"), strict=True):
|
||
|
|
s = df[(df["mode"] == mode) & (df.period == 4)]
|
||
|
|
g = s.groupby("epoch").agg(active=("active_stake_frac", "mean"),
|
||
|
|
tot=("mean_ratio", "mean"))
|
||
|
|
g["corr"] = g.tot / g.active
|
||
|
|
ax.plot(g.index, g.active, "--", color="0.6", lw=1.4, label="active stake / total")
|
||
|
|
ax.plot(g.index, g.tot, "-o", ms=3, color=style.OKABE_ITO[0], label="D̂ / D_total")
|
||
|
|
ax.plot(g.index, g["corr"], "-s", ms=3, color=style.OKABE_ITO[1], label="D̂ / D_active")
|
||
|
|
ax.axhline(1.0, color="0.8", lw=0.7, ls=":")
|
||
|
|
ax.set_xlabel("epoch")
|
||
|
|
ax.set_title(f"{mode} churn (30 %, period 4)")
|
||
|
|
axes[0].set_ylabel("stake fraction / accuracy")
|
||
|
|
axes[0].legend(fontsize=8, loc="lower left")
|
||
|
|
fig.suptitle("TSI tracks active stake under organic churn (β=1, one-epoch lag); "
|
||
|
|
"corrected accuracy stays ~1", y=1.03)
|
||
|
|
style.save(fig, Path(__file__).resolve().parents[1] / "report-figures" / "fig29_churn",
|
||
|
|
provenance="scripts/churn.py")
|
||
|
|
plt.close(fig)
|
||
|
|
|
||
|
|
|
||
|
|
def main() -> None:
|
||
|
|
out = Path(__file__).resolve().parents[1] / "runs"
|
||
|
|
res = Parallel(n_jobs=6, backend="loky", inner_max_num_threads=1)(
|
||
|
|
delayed(_one)(m, p, r) for m, p, r in GRID)
|
||
|
|
df = pd.DataFrame([row for traj in res for row in traj])
|
||
|
|
df.to_parquet(out / "churn.parquet", index=False)
|
||
|
|
fig29(df)
|
||
|
|
print("=== churn: D̂/D_active (tracking accuracy) and worst lag, period 4 ===")
|
||
|
|
for (mode,), g in df[df.period == 4].groupby(["mode"]):
|
||
|
|
t = g[g.epoch >= 8]
|
||
|
|
corr = (t.mean_ratio / t.active_stake_frac)
|
||
|
|
print(f"{mode}: D̂/D_active {corr.mean():.3f} (min {corr.min():.3f}), "
|
||
|
|
f"range_ratio {t.range_ratio.max():.4f}, fork_rate {t.fork_rate.mean():.3f}")
|
||
|
|
print(f"wrote {out/'churn.parquet'} and fig29_churn")
|
||
|
|
|
||
|
|
|
||
|
|
if __name__ == "__main__":
|
||
|
|
main()
|