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
81 lines
3.5 KiB
Python
81 lines
3.5 KiB
Python
"""`architectures` figure — the Wright-Fisher collapse operator is architecture-general.
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The same dry-collapse / grounding-rescue signature appears in three distinct inductive biases:
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the exact histogram (multinomial), an autoregressive GRU, and a causal-masked MLP. If the
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signs held only for the histogram, the effect would be an artefact of the exact operator;
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seeing them in every trained architecture is the generality claim.
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Three panels: (A) forward-KL trajectories per architecture, dry (solid) vs grounded (dashed);
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(B) stationary forward-KL, dry vs grounded, grouped by architecture (all fall with grounding);
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(C) tail-item survival, dry vs grounded, grouped by architecture (all rise). Reads only the
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committed bundle.
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Usage: python figures/plot_architectures.py [results/architectures]
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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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_ARCH_ORDER = ["histogram", "rnn", "mlp"]
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_ARCH_LABEL = {"histogram": "histogram\n(exact)", "rnn": "GRU\n(autoregressive)",
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"mlp": "MLP\n(causal-masked)"}
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def main(results_dir: str = "results/architectures") -> None:
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df, cfg = load_bundle(results_dir)
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kinds = [k for k in _ARCH_ORDER if k in set(df["kind"].unique())]
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g_dry, g_wet = min(df["g"].unique()), max(df["g"].unique())
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last = int(cfg["generations"] * 0.6)
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stat = df[df["generation"] >= last]
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fig, axes = plt.subplots(1, 3, figsize=(16, 4.6))
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arch_colors = dict(zip(kinds, plt.cm.tab10(np.arange(len(kinds)))))
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# Panel A: forward-KL trajectories per architecture, dry (solid) vs grounded (dashed).
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ax = axes[0]
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for k in kinds:
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for g, ls, alpha in [(g_dry, "-", 1.0), (g_wet, "--", 0.7)]:
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s = df[(df["kind"] == k) & (df["g"] == g)].groupby("generation")["forward_kl"].mean()
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ax.plot(s.index, s.values, ls, color=arch_colors[k], alpha=alpha, lw=1.8,
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label=f"{k} (g={g:g})")
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ax.set(xlabel="generation", ylabel=r"forward-KL $D(p^*\Vert\hat p)$",
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title="No real data (solid) collapses;\ngrounded (dashed) holds in every architecture")
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ax.legend(frameon=False, fontsize=7, ncol=1)
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# Panels B & C: grouped bars, dry vs grounded per architecture.
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def grouped_bar(ax, metric, title, ylabel):
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x = np.arange(len(kinds))
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w = 0.36
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for off, g, lab, col in [(-w / 2, g_dry, f"no real data (g={g_dry:g})", "#d62728"),
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(w / 2, g_wet, f"grounded (g={g_wet:g})", "#2ca02c")]:
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means, errs = [], []
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for k in kinds:
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sub = stat[(stat["kind"] == k) & (stat["g"] == g)]
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_, m, ci = mean_ci(sub.assign(_x=0), "_x", metric)
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means.append(m[0]); errs.append(ci[0])
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ax.bar(x + off, means, w, yerr=errs, capsize=3, label=lab, color=col, alpha=0.85)
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ax.set_xticks(x)
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ax.set_xticklabels([_ARCH_LABEL[k] for k in kinds], fontsize=8)
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ax.set(ylabel=ylabel, title=title)
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ax.legend(frameon=False, fontsize=8)
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grouped_bar(axes[1], "forward_kl", "Stationary forward-KL falls with grounding",
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r"stationary forward-KL")
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grouped_bar(axes[2], "tail_frac_alive", "Tail-item survival rises with grounding",
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"tail items alive")
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fig.tight_layout()
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letter_axes(fig)
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savefig(fig, results_dir, "architectures")
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if __name__ == "__main__":
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main(*sys.argv[1:])
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