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
79 lines
3.3 KiB
Python
79 lines
3.3 KiB
Python
"""E5 figure: quality-diversity vs greedy selection.
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At matched grounding, greedy (directional) selection drives the lineage toward the
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fittest items and collapses diversity, while quality-diversity selection (a novelty bonus
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w_i ∝ f_i·p_i^{-alpha}) maintains a high stationary heterozygosity that rises with the
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novelty exponent alpha. Usage: python figures/plot_figS12_quality_diversity.py [results/figS12_quality_diversity]
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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/figS12_quality_diversity") -> None:
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df, cfg = load_bundle(results_dir)
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last = int(cfg["generations"] * 0.8)
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def arm(mode, alpha=1.0):
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return df[(df["mode"] == mode) & (df["novelty_alpha"] == alpha)]
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fig, axes = plt.subplots(1, 3, figsize=(15, 4.3))
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# Panel 1: H trajectories
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ax = axes[0]
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series = [("greedy", 1.0, "#d62728", "greedy"),
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("qd", 1.0, "#ff7f0e", "quality-diversity (α=1)"),
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("qd", 2.0, "#1f77b4", "quality-diversity (α=2)"),
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("none", 1.0, "#2ca02c", "none (grounding only)")]
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for mode, a, c, lab in series:
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s = arm(mode, a).groupby("generation")["heterozygosity"].mean()
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ax.plot(s.index, s.values, color=c, label=lab)
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ax.set(xlabel="generation", ylabel="heterozygosity $H$",
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title="Greedy collapses;\nquality-diversity maintains diversity")
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ax.legend(frameon=False, fontsize=8)
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# Panel 2: stationary H vs alpha for qd, with greedy/none reference lines
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ax = axes[1]
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qd = df[(df["mode"] == "qd") & (df["generation"] >= last)]
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st = qd.groupby("novelty_alpha")["heterozygosity"].agg(["mean", "sem"])
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ax.errorbar(st.index, st["mean"], yerr=1.96 * st["sem"], fmt="-o",
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color="#ff7f0e", capsize=3, label="quality-diversity")
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for mode, c in (("greedy", "#d62728"), ("none", "#2ca02c")):
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h = arm(mode, 1.0)
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h = h[h["generation"] >= last]["heterozygosity"].mean()
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ax.axhline(h, ls="--", color=c, label=f"{mode}")
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ax.set(xlabel=r"novelty exponent $\alpha$", ylabel="stationary $H$",
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title="Quality-diversity keeps $H$\nabove greedy for all α")
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ax.legend(frameon=False, fontsize=9)
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# Panel 3: stationary support size per arm
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ax = axes[2]
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arms = [("greedy", 1.0, "greedy"), ("qd", 0.5, "quality-diversity α=0.5"),
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("qd", 1.0, "quality-diversity α=1"), ("qd", 2.0, "quality-diversity α=2"), ("none", 1.0, "none")]
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labels, vals, errs, colors = [], [], [], []
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palette = {"greedy": "#d62728", "qd": "#ff7f0e", "none": "#2ca02c"}
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for mode, a, lab in arms:
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s = arm(mode, a)
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s = s[s["generation"] >= last]["support_size"]
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labels.append(lab); vals.append(s.mean()); errs.append(1.96 * s.sem())
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colors.append(palette[mode])
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ax.bar(range(len(labels)), vals, yerr=errs, color=colors, capsize=3)
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ax.set(ylabel="stationary support size", title="Surviving items per arm",
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xticks=range(len(labels)))
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ax.set_xticklabels(labels, rotation=25, ha="right", fontsize=8)
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fig.tight_layout()
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letter_axes(fig)
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savefig(fig, results_dir, "figS12_quality_diversity")
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if __name__ == "__main__":
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main(*sys.argv[1:])
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