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
64 lines
3 KiB
Python
64 lines
3 KiB
Python
"""E10 figure — directed sex beats biological sex (the distinctly-AI superpower).
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On rugged (epistatic) landscapes, blind "biological" sex — random mates, no offspring selection —
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suffers outbreeding depression: offspring are worse than the parents. But an AI can do what biology
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cannot: choose complementary mates, evaluate *many* recombinant offspring, and keep only the fittest,
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over several rounds, with no two-parent limit. This **directed sex** avoids the catastrophe and
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matches or exceeds the best parent even when skills are entangled.
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Two panels: (A) deployed capability vs landscape ruggedness — best single parent, random (blind) sex,
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directed sex, and the global optimum; (B) each strategy's edge over the best parent, making the
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random-sex collapse and the directed-sex rescue explicit. Reads only the committed bundle.
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Usage: python figures/plot_figS11_directed_recombination.py [results/figS11_directed_recombination]
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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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sys.path.insert(0, str(Path(__file__).parent))
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from _figlib import load_bundle, mean_ci, savefig, letter_axes # noqa: E402
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def main(results_dir: str = "results/figS11_directed_recombination") -> None:
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df, _ = load_bundle(results_dir)
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fig, axes = plt.subplots(1, 2, figsize=(13, 5))
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# Panel A: the three strategies + global optimum vs ruggedness.
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ax = axes[0]
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for col, c, lab in [("global_opt", "green", "global optimum"),
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("directed_sex", "#d62728", "directed sex (AI: choose + select)"),
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("best_parent", "#7f7f7f", "best single parent"),
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("random_sex", "#1f77b4", "random sex (blind, biology)")]:
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k, m, ci = mean_ci(df, "K", col)
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ls = ":" if col == "global_opt" else "-o"
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ax.plot(k, m, ls, color=c, label=lab) if col == "global_opt" else \
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ax.errorbar(k, m, yerr=ci, fmt=ls, color=c, capsize=3, label=lab)
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ax.set(xlabel="landscape ruggedness $K$ (epistasis)", ylabel="deployed capability (fitness)",
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title="Random sex craters with ruggedness;\ndirected sex tracks/exceeds the best parent")
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ax.legend(frameon=False, fontsize=8)
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# Panel B: edge over best parent (random collapse vs directed rescue).
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ax = axes[1]
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bp = df.groupby("K")["best_parent"].mean()
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for col, c, lab in [("directed_sex", "#d62728", "directed sex"),
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("random_sex", "#1f77b4", "random sex")]:
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s = df.groupby("K")[col].mean() - bp
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ax.plot(s.index, s.values, "-o", color=c, label=lab)
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ax.axhline(0, ls=":", color="gray", lw=1, label="best parent")
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ax.set(xlabel="landscape ruggedness $K$", ylabel="capability − best parent",
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title="Directed sex stays ≥ parents; blind sex\nfalls far below (outbreeding depression)")
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ax.legend(frameon=False, fontsize=9)
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fig.tight_layout()
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letter_axes(fig)
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savefig(fig, results_dir, "figS11_directed_recombination")
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if __name__ == "__main__":
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main(*sys.argv[1:])
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