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>
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_E3.py [results/E3]
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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 # noqa: E402
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def main(results_dir: str = "results/E3") -> 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): matched 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; matched concentrates on the exercised region",
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xticks=regions)
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ax.legend(frameon=False)
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fig.suptitle("E3 — grounding must overlap the content it protects", y=1.02)
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fig.tight_layout()
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savefig(fig, results_dir, "E3")
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if __name__ == "__main__":
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main(*sys.argv[1:])
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