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
67 lines
3 KiB
Python
67 lines
3 KiB
Python
"""E14 figure — mating systems: monogamy vs promiscuity (mate-pool breadth) across ruggedness.
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Three panels, each vs mate-pool breadth (log x: 0.03 = monogamous/structured -> 1.0 = promiscuous/
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panmictic), one line per landscape ruggedness K:
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(A) best fitness / global optimum — the *champion*. On smooth landscapes (low K) it is maximised by
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wide breadth; as ruggedness rises the peak shifts to an INTERMEDIATE breadth (full promiscuity
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prematurely converges below it) — the mating-system image of E9's "optimal recombination rate
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shrinks with ruggedness".
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(B) mean fitness / global optimum — the *typical* individual. Monotonically favoured by breadth at
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every K: panmixia lifts the whole population toward a good consensus.
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(C) diversity (mean normalised pairwise Hamming) — monotonically DESTROYED by breadth at every K
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(promiscuity homogenises), the reservoir largest under monogamy and on rugged landscapes.
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The tension between (A)/(C) is the result: promiscuity maximises the typical model and kills diversity;
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on rugged landscapes the best model needs preserved diversity, so an intermediate breadth wins.
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Usage: python figures/plot_E14.py
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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, savefig, letter_axes # noqa: E402
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def main() -> None:
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df, _ = load_bundle("results/E14")
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last = df[df["generation"] == df["generation"].max()].copy()
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last["best_n"] = last["best_fitness"] / last["global_opt"]
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last["mean_n"] = last["mean_fitness"] / last["global_opt"]
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Ks = sorted(last["K"].unique())
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cmap = plt.get_cmap("viridis")
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colors = {K: cmap(i / max(1, len(Ks) - 1)) for i, K in enumerate(Ks)}
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fig, axes = plt.subplots(1, 3, figsize=(16, 5))
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panels = [
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("best_n", "best fitness / global optimum",
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"Best model peaks at intermediate breadth on rugged\nlandscapes (the peak shifts left as $K$ rises)"),
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("mean_n", "mean fitness / global optimum",
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"Population mean rises monotonically with breadth\n(panmixia lifts the whole population)"),
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("diversity", "diversity (mean pairwise Hamming)",
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"Standing diversity falls monotonically with breadth\n(promiscuity homogenises; monogamy preserves)"),
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]
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for ax, (col, ylab, title) in zip(axes, panels):
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for K in Ks:
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g = (last[last["K"] == K].groupby("breadth")[col]
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.agg(["mean", "sem"]).reset_index())
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ax.errorbar(g["breadth"], g["mean"], yerr=1.96 * g["sem"].fillna(0.0),
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marker="o", lw=1.8, capsize=2, color=colors[K], label=f"K={K}")
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ax.set_xscale("log")
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ax.set(xlabel="mate-pool breadth (monogamous ← → promiscuous)", ylabel=ylab)
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ax.set_title(title, fontsize=9)
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ax.legend(title="ruggedness", frameon=False, fontsize=8)
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fig.tight_layout()
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letter_axes(fig)
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savefig(fig, "results/E14", "E14")
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if __name__ == "__main__":
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main()
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