Giorgio Gilestro
ab3dc10587
- paper/pnas -> paper/manuscript (venue-neutral)
- configs/layer1 -> configs/inheritance, src/knowledge -> src/inheritance
(imported as `inheritance`), make layer1 -> make inheritance; layer2 alias dropped
- inheritance and trained-network bundles named after the manuscript figure
they feed (fig2_grounding_sweep, figS3_rebaselining, ...), or descriptively
where they feed none; configs keep their `experiment:` value so parquet
hashes are unchanged, only output.dir moves
- figure scripts, SI figure sources, notebooks, REPRODUCING.md, README and the
SI Methods/tables updated; make clean no longer deletes tracked manifests;
reproduce.sh hashes the s{seed}/ layouts too
Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01Y64o8FKP7rCuXzC48pxpMm
73 lines
2.8 KiB
Python
73 lines
2.8 KiB
Python
"""E3 figure: region-matched grounding.
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Shows that grounding must *overlap* the content it protects. At the same total budget,
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uniform grounding spreads thin and lets the exercised region's tail collapse, while
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matched grounding concentrates on that region and keeps its rare items alive (at the cost
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of the regions it does not touch). Usage: python figures/plot_figS5_aimed_grounding.py [results/figS5_aimed_grounding]
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Metric: per-region tail-item survival. (Per-region *heterozygosity* is confounded by
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region mass under matched grounding, so it is deliberately not used here.)
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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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import matplotlib.pyplot as plt
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import numpy as np
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sys.path.insert(0, str(Path(__file__).parent))
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from _figlib import load_bundle, savefig, letter_axes # noqa: E402
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def main(results_dir: str = "results/figS5_aimed_grounding") -> None:
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df, cfg = load_bundle(results_dir)
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R = cfg["truth"]["R"]
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exercised = cfg["dynamics"]["grounding"]["exercised"]
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target = exercised[0]
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last = int(cfg["generations"] * 0.8)
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colors = {"uniform": "#d62728", "matched": "#1f77b4"}
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fig, axes = plt.subplots(1, 2, figsize=(12, 4.4))
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# Panel 1: tail survival of the target region over generations
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ax = axes[0]
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tcol = f"tailalive_region_{target}"
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for pol in ("uniform", "matched"):
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sub = df[df["policy"] == pol].groupby("generation")[tcol]
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mean = sub.mean()
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sem = sub.sem()
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ax.plot(mean.index, mean.values, color=colors[pol], label=pol)
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ax.fill_between(mean.index, mean - 1.96 * sem, mean + 1.96 * sem,
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color=colors[pol], alpha=0.2)
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ax.set(xlabel="generation",
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ylabel=f"tail items alive in region {target}",
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title=f"Target region {target} (exercised):\nmatched holds, uniform collapses")
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ax.legend(frameon=False)
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# Panel 2: stationary tail survival per region, uniform vs matched
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ax = axes[1]
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stat = df[df["generation"] >= last]
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regions = np.arange(R)
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width = 0.4
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for i, pol in enumerate(("uniform", "matched")):
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vals = [stat[stat["policy"] == pol][f"tailalive_region_{r}"].mean()
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for r in regions]
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ax.bar(regions + (i - 0.5) * width, vals, width,
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color=colors[pol], label=pol)
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ax.axvline(target, ls=":", color="gray", lw=1)
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ax.annotate("exercised", (target, ax.get_ylim()[1] * 0.9), fontsize=8,
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ha="center", color="gray")
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ax.set(xlabel="region", ylabel="stationary tail items alive",
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title="Uniform spreads thin;\nmatched concentrates on the exercised region",
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xticks=regions)
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ax.legend(frameon=False)
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fig.tight_layout()
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letter_axes(fig)
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savefig(fig, results_dir, "figS5_aimed_grounding")
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if __name__ == "__main__":
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main(*sys.argv[1:])
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