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Restructure: descriptive tier and experiment names, paper/manuscript

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- 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
This commit is contained in:
Giorgio Gilestro 2026-09-13 17:00:40 +01:00
parent 84124de143
commit ab3dc10587
240 changed files with 477 additions and 476 deletions
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20
figures/plot_kernel.py
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@ -13,7 +13,7 @@ one knob of the learning kernel:
estimator REMOVES collapse pressure.
Analytic arms are read from results/kernel_{sharpen,smooth}; the neural reference endpoints
(dashed) are read from the committed results/mnist_collapse and results/grounding parquets — so the
(dashed) are read from the committed results/fig2_mnist_collapse and results/figS6_grounding_rnn parquets — so the
figure is a pure function of committed artifacts.
Usage: python figures/plot_kernel.py
@ -31,8 +31,8 @@ sys.path.insert(0, str(Path(__file__).parent))
from _figlib import load_bundle, savefig, letter_axes # noqa: E402
sys.path.insert(0, str(Path(__file__).parents[1] / "src"))
from knowledge.metrics import heterozygosity # noqa: E402
from knowledge.truth import make_true_distribution # noqa: E402
from inheritance.metrics import heterozygosity # noqa: E402
from inheritance.truth import make_true_distribution # noqa: E402
def _mean_traj(df, knob, val, col):
@ -52,8 +52,8 @@ def _neural_dry(results_dir, col, stationary_frac=0.0):
def main() -> None:
sh, sh_cfg = load_bundle("results/kernel_sharpen")
sm, sm_cfg = load_bundle("results/kernel_smooth")
sh, sh_cfg = load_bundle("results/figS2_kernel_sharpen")
sm, sm_cfg = load_bundle("results/figS2_kernel_smooth")
Hstar_sh = heterozygosity(make_true_distribution(
sh_cfg["truth"]["K"], 1, "zipf", 0.5, sh_cfg["truth"]["zipf_s"], 0,
tail_threshold=sh_cfg["truth"]["tail_threshold"]).p_star)
@ -62,10 +62,10 @@ def main() -> None:
tail_threshold=sm_cfg["truth"]["tail_threshold"]).p_star)
# Neural reference endpoints (dashed) from the committed neural runs.
vae_H = _neural_dry("results/mnist_collapse", "heterozygosity")
vae_sup = _neural_dry("results/mnist_collapse", "support_size")
rnn_H = _neural_dry("results/grounding", "heterozygosity", stationary_frac=0.4)
rnn_KL = _neural_dry("results/grounding", "forward_kl", stationary_frac=0.4)
vae_H = _neural_dry("results/fig2_mnist_collapse", "heterozygosity")
vae_sup = _neural_dry("results/fig2_mnist_collapse", "support_size")
rnn_H = _neural_dry("results/figS6_grounding_rnn", "heterozygosity", stationary_frac=0.4)
rnn_KL = _neural_dry("results/figS6_grounding_rnn", "forward_kl", stationary_frac=0.4)
fig, axes = plt.subplots(2, 2, figsize=(13, 9))
NEU, KER = "#1f77b4", "#d62728"
@ -111,7 +111,7 @@ def main() -> None:
ax.legend(frameon=False, fontsize=8)
fig.tight_layout()
for d in ("results/kernel_sharpen", "results/kernel_smooth"):
for d in ("results/figS2_kernel_sharpen", "results/figS2_kernel_smooth"):
letter_axes(fig)
savefig(fig, d, "kernel")