Giorgio Gilestro
b8da418034
Grounding refinement (18 reps): forward-KL is the operative neural
collapse metric, not H or tail-survival. The RNN's smoothing keeps
spurious tail modes alive, so tail_truth_mass_alive is flat/non-monotone
in g and H stays ~0.8 of H*; only forward-KL falls monotonically (dry
2.08 -> g=0.2: 0.75, paired t up to 3.3). The sharp g* << 1 is an
exact-operator feature carried by the histogram bridge (0.047); the
trained RNN confirms the SIGN and softens the sharpness (half the KL gap
closes by g~0.04, but full recovery needs g~0.19). Blueprint 3.5's
directional claim holds; the pre-registered 95%-of-H*/tail falsifier is
not met because those are the wrong metrics for a smoothing model.
Robustness: a fully-degenerate RNN can emit only invalid codewords, so
measure_distribution now returns a terminal-collapse sentinel (fixation
on the dominant mode) instead of crashing a long sweep. Edge test added
(94 tests green).
Figures: plot_{bridge,collapse,grounding,architectures,recombination}.py,
each a pure function of its committed bundle, wired into `make figures`
(glob plot_*.py minus plot_E[1-6]/_*). bridge sits on the exact H_eq
curve (g*=0.047); recombination shows max-merge rising while mean-distill
stays flat; architectures shows the collapse/rescue signs across
histogram/GRU/MLP.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
82 lines
3.6 KiB
Python
82 lines
3.6 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 # 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="Dry (solid) collapses; grounded (dashed) held —\nin 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"dry (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.suptitle("architectures — dry collapse and grounding-rescue are architecture-general "
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"(histogram, GRU, MLP)", y=1.02, fontsize=13)
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fig.tight_layout()
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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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