Giorgio Gilestro
96902e87f0
paper/pnas/make_figs.py re-plots every panel directly from the committed results artifacts into six single-file figures (figs/fig1..fig6.pdf): no experiment codenames or suptitles (interpretation moved to captions), bold panel letters, plain-language axis labels and legend entries, one consistent style (8pt, no top/right spines). Panels: fig1 A-B (grounding equilibrium + MNIST montage with its baked-in title cropped), fig2 A-B (blending cancellation + Fisher-Muller), fig3 A-D (outbreeding, directed recombination, mating breadth champion + diversity), fig4 A-C (society ablation trajectories), fig5 A-F (speciation: analytic curve + cliff, MLP decomposition + conflict sweep, LLM coherence + duration null), fig6 A-D (seed-replicated merging, 7B-hard routing vs averaging, predictive-test scatter, predictor comparison). build.py now places the single PDFs; captions rewritten per lettered panel; in-text panel refs updated (5B->5C-D, 5C->5E-F); stale stacked copies removed. Document 20pp -> 18pp. Co-Authored-By: Claude Fable 5 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01BkRLcc18rwT2Lysu6PbG7v
381 lines
17 KiB
Python
381 lines
17 KiB
Python
"""Publication figures for the PNAS draft — unified, lettered, codename-free.
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Re-plots every panel directly from the committed results artifacts into six single-file figures
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(figs/fig1.pdf .. fig6.pdf): no experiment codenames, no suptitles, no per-panel headline titles
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(interpretation lives in the captions), bold panel letters, one consistent style. The per-experiment
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figures under results/ remain the exploratory versions; these are the manuscript's.
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Usage: python paper/pnas/make_figs.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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import numpy as np
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ROOT = Path(__file__).resolve().parents[2]
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sys.path.insert(0, str(ROOT / "figures"))
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sys.path.insert(0, str(ROOT / "src"))
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import os
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os.chdir(ROOT) # load_bundle uses repo-relative paths
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from _figlib import load_bundle, mean_ci # noqa: E402
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OUT = ROOT / "paper" / "pnas" / "figs"
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plt.rcParams.update({
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"font.size": 8, "axes.labelsize": 8.5, "legend.fontsize": 7, "legend.frameon": False,
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"lines.markersize": 3.6, "axes.spines.top": False, "axes.spines.right": False,
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})
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def letter(ax, s, x=-0.14):
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ax.text(x, 1.02, s, transform=ax.transAxes, fontsize=12, fontweight="bold", va="bottom")
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def save(fig, name):
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OUT.mkdir(exist_ok=True)
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fig.savefig(OUT / f"{name}.pdf", bbox_inches="tight")
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plt.close(fig)
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print("wrote", OUT / f"{name}.pdf")
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# ---------------------------------------------------------------- fig 1: grounding + MNIST
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def fig1():
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from knowledge.analysis import critical_grounding, reduce_to_stationary
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from knowledge.metrics import heterozygosity
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from knowledge.truth import make_true_distribution
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df, cfg = load_bundle("results/E2")
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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, tail_threshold=cfg["truth"]["tail_threshold"])
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H_star = heterozygosity(td.p_star)
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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(1, 2, figsize=(10.6, 3.5), gridspec_kw={"width_ratios": [1, 1.35]})
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ax = axes[0]
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st = reduce_to_stationary(stat, value_col="heterozygosity", replicate_col="replicate", last_frac=1.0)
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gg, Hm, Hci = mean_ci(stat, "g", "heterozygosity")
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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=2, label="simulation")
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ax.plot(gg[~nz], Hm[~nz], "o", mfc="white", mec="#1f77b4")
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m_of_g = stat.groupby("g")["m"].first().to_numpy()
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m_grid = np.linspace(0, m_of_g.max(), 400)
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def H_eq(m):
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m = np.asarray(m, 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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ax.plot(m_grid / (n + m_grid), H_eq(m_grid), "k--", lw=1, label="exact equilibrium")
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ax.axhline(H_star, ls=":", color="gray", lw=1, label="source diversity $H^*$")
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r = critical_grounding(st, H_star=H_star, frac=0.95, seed=7)
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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=1.1,
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label=f"95%-retention threshold $g\\approx{r['g_star']:.3f}$")
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ax.set(xlabel="grounding fraction $g = m/(n+m)$", ylabel="stationary diversity $H$")
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ax.legend()
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letter(ax, "A")
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ax = axes[1]
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from PIL import Image
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im = np.asarray(Image.open("results/mnist_collapse/mnist_montage.png"))
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crop = int(im.shape[0] * 0.085) # remove the baked-in title band
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ax.imshow(im[crop:], interpolation="bilinear")
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ax.set_axis_off()
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letter(ax, "B", x=-0.02)
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save(fig, "fig1")
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# ---------------------------------------------------------------- fig 2: blending vs union + Fisher–Muller
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def fig2():
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fig, axes = plt.subplots(1, 2, figsize=(10.6, 3.5))
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df, _ = load_bundle("results/E4")
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r0 = df[(df["g"] == 0.0) & (df["rho"] == 0.0)]
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mx = r0.groupby("K_T")["surviving_max"].agg(["mean", "sem"])
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mn = r0.groupby("K_T")["surviving_mean"].agg(["mean", "sem"])
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ax = axes[0]
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ax.errorbar(mx.index, mx["mean"], yerr=1.96 * mx["sem"], fmt="-o", color="#1f77b4",
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capsize=2, label="union operator (strongest source)")
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ax.errorbar(mn.index, mn["mean"], yerr=1.96 * mn["sem"], fmt="--s", color="#d62728",
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capsize=2, label="output-mean (blending)")
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ax.set(xlabel="number of parents", ylabel="rare capabilities surviving in the child",
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xticks=sorted(r0["K_T"].unique()))
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ax.legend()
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letter(ax, "A")
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df8, cfg8 = load_bundle("results/E8")
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L = cfg8["society"]["L"]
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d0 = df8[df8["rho"] == 0.0]
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ax = axes[1]
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for col, c, lab in [("best_parent", "#7f7f7f", "best single parent"),
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("average", "#1f77b4", "blended average"),
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("sexual", "#d62728", "recombined offspring")]:
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k, m, ci = mean_ci(d0, "K_T", col)
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ax.errorbar(k, m, yerr=ci, fmt="-o", color=c, capsize=2, label=lab)
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ax.axhline(L, ls=":", color="green", lw=1, label="optimum")
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ax.set(xlabel="number of parents", ylabel="offspring capability")
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ax.legend()
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letter(ax, "B")
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save(fig, "fig2")
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# ---------------------------------------------------------------- fig 3: rugged landscapes
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def fig3():
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fig, axes = plt.subplots(2, 2, figsize=(10.6, 6.8))
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df9, _ = load_bundle("results/E9")
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Ks = sorted(df9["K"].unique())
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colors = plt.cm.viridis(np.linspace(0, 0.85, len(Ks)))
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bp = df9.groupby("K")["best_parent"].mean()
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ax = axes[0, 0]
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for K, c in zip(Ks, colors):
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s = df9[df9["K"] == K].groupby("rate")["mean_offspring"].mean() - bp[K]
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ax.plot(s.index, s.values, "-o", color=c, label=f"$K$={K}")
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ax.axhline(0, ls=":", color="gray", lw=1)
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ax.set(xlabel="recombination rate", ylabel="mean offspring − best parent")
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ax.legend(title="ruggedness", ncol=2)
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letter(ax, "A")
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df10, _ = load_bundle("results/E10")
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ax = axes[0, 1]
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for col, c, lab in [("global_opt", "green", "global optimum"),
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("directed_sex", "#d62728", "screened recombination (directed)"),
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("best_parent", "#7f7f7f", "best single parent"),
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("random_sex", "#1f77b4", "blind recombination")]:
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k, m, ci = mean_ci(df10, "K", col)
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if col == "global_opt":
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ax.plot(k, m, ":", color=c, label=lab)
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else:
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ax.errorbar(k, m, yerr=ci, fmt="-o", color=c, capsize=2, label=lab)
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ax.set(xlabel="landscape ruggedness $K$", ylabel="offspring capability")
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ax.legend()
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letter(ax, "B")
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df14, _ = load_bundle("results/E14")
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last = df14[df14["generation"] == df14["generation"].max()].copy()
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last["best_n"] = last["best_fitness"] / last["global_opt"]
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K14 = sorted(last["K"].unique())
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cmap = plt.get_cmap("viridis")
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c14 = {K: cmap(i / max(1, len(K14) - 1)) for i, K in enumerate(K14)}
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for ax, col, ylab, L in [(axes[1, 0], "best_n", "best fitness / optimum", "C"),
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(axes[1, 1], "diversity", "population diversity", "D")]:
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for K in K14:
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g = last[last["K"] == K].groupby("breadth")[col].agg(["mean", "sem"]).reset_index()
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ax.errorbar(g["breadth"], g["mean"], yerr=1.96 * g["sem"].fillna(0), fmt="-o",
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color=c14[K], capsize=2, label=f"$K$={K}")
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ax.set_xscale("log")
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ax.set(xlabel="mate-pool breadth (monogamous → panmictic)", ylabel=ylab)
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ax.legend(title="ruggedness")
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letter(ax, L)
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save(fig, "fig3")
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# ---------------------------------------------------------------- fig 4: the society
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def fig4():
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df, _ = load_bundle("results/E11")
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arms = [("full", "#2ca02c", "full system"),
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("no_sex", "#ff7f0e", "no recombination"),
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("no_diversity", "#9467bd", "no diversity preservation"),
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("no_grounding", "#d62728", "no grounded evaluation")]
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arms = [a for a in arms if a[0] in set(df["arm"].unique())]
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g_opt = df["global_opt"].mean()
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fig, axes = plt.subplots(1, 3, figsize=(11.4, 3.2))
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panels = [("best_fitness", "best real fitness", "A"),
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("diversity", "population diversity", "B"),
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("conformity_true_gap", "conformity − true fitness", "C")]
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for ax, (col, ylab, L) in zip(axes, panels):
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for name, c, lab in arms:
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sub = df[df["arm"] == name]
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g, m, ci = mean_ci(sub, "generation", col)
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ax.plot(g, m, "-", color=c, lw=1.6, label=lab)
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ax.fill_between(g, m - ci, m + ci, color=c, alpha=0.15)
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if col == "best_fitness":
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ax.axhline(g_opt, ls=":", color="gray", lw=1, label="global optimum")
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ax.legend()
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ax.set(xlabel="generation", ylabel=ylab)
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letter(ax, L)
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save(fig, "fig4")
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# ---------------------------------------------------------------- fig 5: speciation, three tiers
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def fig5():
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fig, axes = plt.subplots(2, 3, figsize=(11.4, 6.6))
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bdm, _ = load_bundle("results/E12")
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rhos = sorted(bdm["rho"].unique())
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colors = plt.cm.viridis(np.linspace(0.15, 0.85, len(rhos)))
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def agg(df, keys, value):
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g = df.groupby(keys)[value].agg(["mean", "std", "count"]).reset_index()
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g["se"] = g["std"] / np.sqrt(g["count"].clip(lower=1))
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return g
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ax = axes[0, 0]
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par = agg(bdm, "divergence", "parent_fitness")
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ax.plot(par["divergence"], par["mean"], "k--", lw=1.3, label="parents")
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for rho, c in zip(rhos, colors):
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g = agg(bdm[bdm["rho"] == rho], "divergence", "offspring_fitness")
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ax.plot(g["divergence"], g["mean"], "-o", color=c, label=f"hybrid, density {rho:g}")
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ax.fill_between(g["divergence"], g["mean"] - g["se"], g["mean"] + g["se"], color=c, alpha=0.15)
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ax.axhline(0, color="#999", lw=0.7, ls=":")
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ax.set(xlabel="parental divergence (substitutions)", ylabel="fitness")
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ax.legend()
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letter(ax, "A")
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ax = axes[0, 1]
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for rho, c in zip(rhos, colors):
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g = agg(bdm[bdm["rho"] == rho], "divergence", "isolation")
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ax.plot(g["divergence"], g["mean"], "-o", color=c, label=f"{rho:g}")
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ax.set(xlabel="parental divergence (substitutions)", ylabel="P(hybrid inviable)", ylim=(-0.02, 1.02))
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ax.legend(title="incompatibility density")
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letter(ax, "B")
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dec, _ = load_bundle("results/speciation_real")
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order = [c for c in ["shared", "independent", "conflict"] if c in set(dec["condition"])]
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g = dec.groupby("condition").agg(naive=("barrier_naive", "mean"),
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res=("residual_scale", "mean")).reindex(order)
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ax = axes[0, 2]
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x = np.arange(len(order)); w = 0.38
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ax.bar(x - w / 2, g["naive"], w, color="#9ecae1", label="before alignment")
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ax.bar(x + w / 2, g["res"], w, color="#d62728", label="after alignment (residual)")
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ax.set_xticks(x)
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ax.set_xticklabels(["same task,\nshared start", "same task,\ndifferent start", "conflicting\ntasks"])
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ax.set(ylabel="merge error barrier")
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ax.legend()
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letter(ax, "C")
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cliff, _ = load_bundle("results/speciation_real_cliff")
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cg = cliff.groupby("conflict_frac").agg(res=("residual_scale", "mean"),
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hyb=("acc_merge_scale", "mean")).reset_index()
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ax = axes[1, 0]
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ax.plot(cg["conflict_frac"], cg["res"], "-o", color="#d62728", label="residual barrier")
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ax2 = ax.twinx()
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ax2.plot(cg["conflict_frac"], cg["hyb"], "-s", color="#2c7fb8", label="merged-model accuracy")
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ax2.set_ylabel("merged accuracy", color="#2c7fb8")
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ax2.tick_params(axis="y", labelcolor="#2c7fb8")
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ax2.spines["right"].set_visible(True)
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ax.set(xlabel="fraction of classes in conflict", ylabel="residual barrier")
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l1, la1 = ax.get_legend_handles_labels(); l2, la2 = ax2.get_legend_handles_labels()
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ax.legend(l1 + l2, la1 + la2, loc="center left")
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letter(ax, "D")
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rep, _ = load_bundle("results/llm_speciation")
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def series(df, mode, model, metric):
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sub = df[(df["mode"] == mode) & (df["model"] == model) & (df["metric"] == metric)]
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g = sub.groupby("x")["accuracy"].mean().reset_index()
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return g["x"], g["accuracy"]
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ax = axes[1, 1]
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x_, y_ = series(rep, "conflict", "parent_a", "ambig_asc")
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ax.plot(x_, y_, "--o", color="#9ecae1", label="parent A, own convention")
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x_, y_ = series(rep, "conflict", "parent_b", "ambig_desc")
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ax.plot(x_, y_, "--o", color="#a1d99b", label="parent B, own convention")
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x_, y_ = series(rep, "conflict", "merge_soup", "coherence")
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ax.plot(x_, y_, "-s", color="#d62728", label="merge, best convention")
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ax.set(xlabel="fraction of training in conflict", ylabel="accuracy, shared prompts")
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ax.legend()
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letter(ax, "E")
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ax = axes[1, 2]
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x_, y_ = series(rep, "duration", "merge_soup", "mean_private")
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ax.plot(x_, y_, "-o", color="#d62728", label="merged model")
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x_, y_ = series(rep, "duration", "parent_a", "strings")
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ax.plot(x_, y_, "--o", color="#9ecae1", label="parent A, own task")
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x_, y_ = series(rep, "duration", "parent_b", "arith")
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ax.plot(x_, y_, "--o", color="#a1d99b", label="parent B, own task")
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ax.set(xlabel="specialist training (epochs)", ylabel="accuracy", ylim=(0, 1.02))
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ax.legend()
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letter(ax, "F")
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save(fig, "fig5")
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# ---------------------------------------------------------------- fig 6: the language-model tier
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def fig6():
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import pandas as pd
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from scipy.stats import spearmanr
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fig, axes = plt.subplots(2, 2, figsize=(10.6, 6.6))
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dfm, _ = load_bundle("results/llm_merge_seeds")
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specs = sorted(m for m in dfm["model"].unique() if m.startswith("spec_"))
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rows = []
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for s, sub in dfm.groupby("seed"):
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ov = {m: sub[(sub["model"] == m) & (sub["metric"] == "overall")]["accuracy"].mean() for m in specs}
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b = sub[sub["model"] == max(ov, key=ov.get)].copy(); b["model"] = "best_specialist"
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rows.append(b)
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dfm = pd.concat([dfm] + rows, ignore_index=True)
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models = ["base", "best_specialist", "merge_soup", "merge_ties"]
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labels = ["base", "best\nspecialist", "merged\n(average)", "merged\n(interference-aware)"]
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ax = axes[0, 0]
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x = np.arange(len(models))
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for off, metric, c, lab in ((-0.19, "overall", "#2c7fb8", "overall"),
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(0.19, "worst_family", "#d62728", "worst task family")):
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vals, errs = [], []
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for m in models:
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v = dfm[(dfm["model"] == m) & (dfm["metric"] == metric)].groupby("seed")["accuracy"].mean()
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vals.append(v.mean()); errs.append(1.96 * v.std(ddof=1) / max(1, np.sqrt(len(v))))
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ax.bar(x + off, vals, 0.36, yerr=errs, capsize=2, color=c, label=lab)
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ax.set_xticks(x); ax.set_xticklabels(labels, fontsize=7)
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ax.set(ylabel="verifier accuracy")
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ax.legend()
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letter(ax, "A")
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df7, _ = load_bundle("results/llm_moe_hard_hpc")
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def acc7(model, metric):
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r = df7[(df7["model"] == model) & (df7["metric"] == metric)]["accuracy"]
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return float(r.iloc[0]) if len(r) else np.nan
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specs7 = sorted(m for m in df7["model"].unique() if m.startswith("spec_"))
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best7 = max(specs7, key=lambda m: acc7(m, "overall"))
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models7 = [best7, "merge_soup", "merge_ties", "moe_oracle"]
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labels7 = ["best\nspecialist", "merged\n(average)", "merged\n(interference-aware)", "routed\n(kept separate)"]
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ax = axes[0, 1]
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x = np.arange(len(models7))
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for off, metric, c, lab in ((-0.19, "overall", "#2c7fb8", "overall"),
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(0.19, "worst_family", "#d62728", "worst task family")):
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ax.bar(x + off, [acc7(m, metric) for m in models7], 0.36, color=c, label=lab)
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ax.set_xticks(x); ax.set_xticklabels(labels7, fontsize=7)
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ax.set(ylabel="verifier accuracy")
|
||
ax.legend()
|
||
letter(ax, "B")
|
||
|
||
a = pd.read_parquet("results/llm_epistasis/results.parquet")
|
||
b = pd.read_parquet("results/llm_epistasis_compat/results.parquet")
|
||
dfe = pd.concat([a, b], ignore_index=True)
|
||
ax = axes[1, 0]
|
||
for mode, c, mk, lab in (("conflict", "#d62728", "o", "conflicting conventions"),
|
||
("duration", "#2c7fb8", "s", "divergence only"),
|
||
("compat", "#41ab5d", "^", "overlap, no conflict")):
|
||
sub = dfe[dfe["mode"] == mode]
|
||
ax.scatter(sub["epi_conf"], sub["merge_penalty"], c=c, marker=mk, s=26, alpha=0.75, label=lab)
|
||
ax.axhline(0, color="#999", lw=0.6)
|
||
ax.set(xlabel="pre-merge functional conflict (confidence-weighted)",
|
||
ylabel="merge penalty")
|
||
ax.legend()
|
||
letter(ax, "C")
|
||
|
||
preds = [("dis_raw", "raw\ndisagreement"), ("epi_conf", "conf-weighted\nconflict"),
|
||
("grad_cos", "gradient\nalignment"), ("delta_cos", "weight\ncosine"),
|
||
("delta_l2", "weight\ndistance"), ("cross_perf", "cross-task\naccuracy")]
|
||
ax = axes[1, 1]
|
||
rhos_ = [abs(spearmanr(dfe[c], dfe["merge_penalty"])[0]) for c, _ in preds]
|
||
cols = ["#fc9272", "#d62728", "#9ecae1", "#9ecae1", "#9ecae1", "#9ecae1"]
|
||
ax.bar(np.arange(len(preds)), rhos_, 0.6, color=cols)
|
||
ax.set_xticks(np.arange(len(preds)))
|
||
ax.set_xticklabels([l for _, l in preds], fontsize=6.5)
|
||
ax.set(ylabel="|Spearman ρ| vs merge penalty", ylim=(0, 0.8))
|
||
letter(ax, "D")
|
||
save(fig, "fig6")
|
||
|
||
|
||
if __name__ == "__main__":
|
||
for f in (fig1, fig2, fig3, fig4, fig5, fig6):
|
||
f()
|