Giorgio Gilestro
1721d047fa
Finishes the Layer 1 analytical core. All six experiments run with honest, publication-quality figures; 71 tests green. - E3 region-matched grounding: `grounding.exercised` knob + per-region tail survival. Matched holds the exercised region's tail (0.49) where uniform spreads thin and lets it collapse (0.07). - E4 multi-teacher recombination: `run_coverage` runner. Union coverage matches U(K_T,rho,q) exactly. Finding: mean-mixture distillation shows NO surviving benefit (a conservation law — 1/K_T dilution cancels the union gain); a union-preserving max-merge (M2N2-style) does. E4 reports both operators. - E5 QD vs greedy: greedy drives fixation (H~0.01); QD holds H at 0.48-0.88, rising with the novelty exponent. - E6 re-mint gate: `arm` multi-override sweep. Re-minting a collapsed lineage locks in divergence of KL-to-original; gating on diversity prevents it. - E2 analysis add-ons (from the companion work order, numbers verified): new analysis.py (reduce_to_stationary, critical_grounding with bootstrap CI -> g*=0.048, 95% CI [0.047,0.050]); tail_band_metrics + per-band logging; the E2 figure rebuilt as a 2x2 (defined g*+CI, g=0 flagged as a finite-time artifact, tail item-vs-mass, per-rarity-band panel). Uses truth-mass-weighted tail coverage rather than the raw (martingale) tail_mass. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
80 lines
3.2 KiB
Python
80 lines
3.2 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_E5.py [results/E5]
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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 # noqa: E402
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def main(results_dir: str = "results/E5") -> 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", "qd (α=1)"),
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("qd", 2.0, "#1f77b4", "qd (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; QD 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="qd")
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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="QD maintains H above 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, "qd α=0.5"),
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("qd", 1.0, "qd α=1"), ("qd", 2.0, "qd 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.suptitle("E5 — quality-diversity selection maintains diversity where greedy "
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"fixes it", y=1.02)
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fig.tight_layout()
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savefig(fig, results_dir, "E5")
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if __name__ == "__main__":
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main(*sys.argv[1:])
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