Restructure: descriptive tier and experiment names, paper/manuscript
- 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
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115
figures/plot_fig2_grounding_sweep.py
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figures/plot_fig2_grounding_sweep.py
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"""E2 figure: the grounding phase boundary (headline), publication-honest.
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Four panels: (A) H trajectories (g=0 slides to 0, g>0 plateau); (B) the phase boundary —
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stationary H vs g tracking the exact H_eq, with an operational g* (where H first reaches
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0.95·H*) and its bootstrap CI, and g=0 marked as a finite-time artifact; (C) tail coverage
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by item-count vs truth-mass — both stay low, the deep tail is largely unrescuable at
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feasible grounding; (D) per-rarity-band survival — the m·p*_i≳1 threshold made visible
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(deep bands lag, motivating E4/E6). Usage: python figures/plot_fig2_grounding_sweep.py [results/fig2_grounding_sweep]
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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, mean_ci, savefig, letter_axes # noqa: E402
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sys.path.insert(0, str(Path(__file__).parents[1] / "src"))
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from inheritance.analysis import critical_grounding, reduce_to_stationary # noqa: E402
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from inheritance.metrics import heterozygosity # noqa: E402
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from inheritance.truth import make_true_distribution # noqa: E402
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def main(results_dir: str = "results/fig2_grounding_sweep") -> None:
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df, cfg = load_bundle(results_dir)
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n = cfg["dynamics"]["n"]
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td = make_true_distribution(cfg["truth"]["K"], 1, "zipf", cfg["truth"]["tail_frac"],
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cfg["truth"]["zipf_s"], 0,
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tail_threshold=cfg["truth"]["tail_threshold"])
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H_star = heterozygosity(td.p_star)
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def H_eq(m):
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m = np.asarray(m, dtype=float)
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return np.where(m <= 0, 0.0, H_star * m * (2 * n + m - 1) / (n + 2 * n * m + m * m))
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g_values = sorted(df["g"].unique())
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last = int(cfg["generations"] * 0.8)
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stat = df[df["generation"] >= last]
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fig, axes = plt.subplots(2, 2, figsize=(13, 9))
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# Panel A: H trajectories, one line per g
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ax = axes[0, 0]
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colors = plt.cm.viridis(np.linspace(0, 0.9, len(g_values)))
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for g, c in zip(g_values, colors):
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s = df[df["g"] == g].groupby("generation")["heterozygosity"].mean()
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ax.plot(s.index, s.values, color=c, label=f"g={g:g}")
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ax.axhline(H_star, ls=":", color="gray", lw=1)
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ax.set(xlabel="generation", ylabel="heterozygosity $H$",
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title="Trajectories: g=0 collapses, g>0 plateau")
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ax.legend(frameon=False, fontsize=8, ncol=2)
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# Panel B: stationary H vs g + exact H_eq + operational g* with bootstrap CI
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ax = axes[0, 1]
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st = reduce_to_stationary(df[df["generation"] >= last], value_col="heterozygosity",
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replicate_col="replicate", last_frac=1.0)
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gg, Hm, Hci = mean_ci(stat, "g", "heterozygosity")
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m_of_g = stat.groupby("g")["m"].first().to_numpy()
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# g=0 marked hollow (finite-time artifact: true H_eq(0)=0)
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nz = gg > 0
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ax.errorbar(gg[nz], Hm[nz], yerr=Hci[nz], fmt="o", color="#1f77b4", capsize=3,
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label="simulation", zorder=3)
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ax.plot(gg[~nz], Hm[~nz], "o", mfc="white", mec="#1f77b4", zorder=3)
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ax.annotate("g=0: pre-convergence\n(true $H_{eq}=0$)", (gg[~nz][0], Hm[~nz][0]),
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textcoords="offset points", xytext=(12, -4), fontsize=7, color="gray")
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m_grid = np.linspace(0, m_of_g.max(), 400)
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ax.plot(m_grid / (n + m_grid), H_eq(m_grid), "k--", label=r"exact $H_{eq}$", zorder=2)
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ax.axhline(H_star, ls=":", color="gray", lw=1, label="$H^*$ (truth)")
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r = critical_grounding(st, H_star=H_star, frac=0.95, seed=7)
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ax.axhline(r["target_H"], ls=":", color="#d62728", lw=1)
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ax.axvspan(r["ci_low"], r["ci_high"], color="#d62728", alpha=0.15)
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ax.axvline(r["g_star"], color="#d62728", lw=1.2,
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label=f"$g^*$={r['g_star']:.3f} (95% CI [{r['ci_low']:.3f},{r['ci_high']:.3f}])")
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ax.set(xlabel="grounding fraction $g=m/(n+m)$", ylabel="stationary $H$",
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title=r"Grounding saturates by $g^\star\approx0.05$ (95% of $H^*$)")
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ax.legend(frameon=False, fontsize=8)
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# Panel C: tail coverage by item-count vs truth-mass (both low: deep tail unrescuable)
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ax = axes[1, 0]
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ig, Im, Ici = mean_ci(stat, "g", "tail_frac_alive")
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mg, Mm, Mci = mean_ci(stat, "g", "tail_truth_mass_alive")
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ax.errorbar(ig, Im, yerr=Ici, fmt="s-", color="#d62728", capsize=3,
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label="tail items alive (count)")
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ax.errorbar(mg, Mm, yerr=Mci, fmt="o-", color="#9467bd", capsize=3,
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label="tail truth-mass alive")
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ax.set(xlabel="grounding fraction $g$", ylabel="fraction of tail retained",
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title="Tail stays largely unrescued at feasible $g$\n(rises only slowly with $g$)")
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ax.legend(frameon=False, fontsize=9)
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# Panel D: per-rarity-band survival across g (band 0 = rarest)
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ax = axes[1, 1]
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band_cols = [c for c in df.columns if c.startswith("band") and c.endswith("_alive")]
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band_cols = sorted(band_cols)
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band_colors = plt.cm.plasma(np.linspace(0.1, 0.85, len(band_cols)))
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for col, c in zip(band_cols, band_colors):
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s = stat.groupby("g")[col].mean()
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depth = col.replace("band", "").replace("_alive", "")
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lab = f"band {depth}" + (" (rarest)" if depth == "0" else
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" (shallowest)" if col == band_cols[-1] else "")
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ax.plot(s.index, s.values, "-o", color=c, ms=4, label=lab)
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ax.set(xlabel="grounding fraction $g$", ylabel="fraction of band alive",
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title=r"Per-rarity band: the $m\,p^*_i\gtrsim1$ threshold (deep lags)")
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ax.legend(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_dir, "fig2_grounding_sweep")
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if __name__ == "__main__":
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main(*sys.argv[1:])
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