Giorgio Gilestro
84124de143
Clarity pass over the main text (36-item audit), Discussion rewrite and cut, acknowledgements, Souly et al. as ref 62, lettered SI panels, model section moved under Results; plus the untracked curriculum/society/compose/smol configs, runners, figures, stats and tests that the SI already cites. Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01Y64o8FKP7rCuXzC48pxpMm
73 lines
2.7 KiB
Python
73 lines
2.7 KiB
Python
"""E3 figure: region-matched grounding.
|
|
|
|
Shows that grounding must *overlap* the content it protects. At the same total budget,
|
|
uniform grounding spreads thin and lets the exercised region's tail collapse, while
|
|
matched grounding concentrates on that region and keeps its rare items alive (at the cost
|
|
of the regions it does not touch). Usage: python figures/plot_E3.py [results/E3]
|
|
|
|
Metric: per-region tail-item survival. (Per-region *heterozygosity* is confounded by
|
|
region mass under matched grounding, so it is deliberately not used here.)
|
|
"""
|
|
|
|
from __future__ import annotations
|
|
|
|
import sys
|
|
from pathlib import Path
|
|
|
|
import matplotlib.pyplot as plt
|
|
import numpy as np
|
|
|
|
sys.path.insert(0, str(Path(__file__).parent))
|
|
from _figlib import load_bundle, savefig, letter_axes # noqa: E402
|
|
|
|
|
|
def main(results_dir: str = "results/E3") -> None:
|
|
df, cfg = load_bundle(results_dir)
|
|
R = cfg["truth"]["R"]
|
|
exercised = cfg["dynamics"]["grounding"]["exercised"]
|
|
target = exercised[0]
|
|
last = int(cfg["generations"] * 0.8)
|
|
colors = {"uniform": "#d62728", "matched": "#1f77b4"}
|
|
|
|
fig, axes = plt.subplots(1, 2, figsize=(12, 4.4))
|
|
|
|
# Panel 1: tail survival of the target region over generations
|
|
ax = axes[0]
|
|
tcol = f"tailalive_region_{target}"
|
|
for pol in ("uniform", "matched"):
|
|
sub = df[df["policy"] == pol].groupby("generation")[tcol]
|
|
mean = sub.mean()
|
|
sem = sub.sem()
|
|
ax.plot(mean.index, mean.values, color=colors[pol], label=pol)
|
|
ax.fill_between(mean.index, mean - 1.96 * sem, mean + 1.96 * sem,
|
|
color=colors[pol], alpha=0.2)
|
|
ax.set(xlabel="generation",
|
|
ylabel=f"tail items alive in region {target}",
|
|
title=f"Target region {target} (exercised):\nmatched holds, uniform collapses")
|
|
ax.legend(frameon=False)
|
|
|
|
# Panel 2: stationary tail survival per region, uniform vs matched
|
|
ax = axes[1]
|
|
stat = df[df["generation"] >= last]
|
|
regions = np.arange(R)
|
|
width = 0.4
|
|
for i, pol in enumerate(("uniform", "matched")):
|
|
vals = [stat[stat["policy"] == pol][f"tailalive_region_{r}"].mean()
|
|
for r in regions]
|
|
ax.bar(regions + (i - 0.5) * width, vals, width,
|
|
color=colors[pol], label=pol)
|
|
ax.axvline(target, ls=":", color="gray", lw=1)
|
|
ax.annotate("exercised", (target, ax.get_ylim()[1] * 0.9), fontsize=8,
|
|
ha="center", color="gray")
|
|
ax.set(xlabel="region", ylabel="stationary tail items alive",
|
|
title="Uniform spreads thin;\nmatched concentrates on the exercised region",
|
|
xticks=regions)
|
|
ax.legend(frameon=False)
|
|
|
|
fig.tight_layout()
|
|
letter_axes(fig)
|
|
savefig(fig, results_dir, "E3")
|
|
|
|
|
|
if __name__ == "__main__":
|
|
main(*sys.argv[1:])
|