giorgio/MachineSex
1
0
Fork 0
Code Issues Pull requests Projects Releases Packages Wiki Activity Actions

Manuscript revision and pending experiment work, snapshot before restructuring

Browse source 
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
This commit is contained in:
Giorgio Gilestro 2026-09-13 16:54:09 +01:00
parent e4804adabc
commit 84124de143
450 changed files with 52813 additions and 1202 deletions
Download patch file Download diff file Expand all files Collapse all files

13
figures/plot_kernel.py
View file

@ -28,7 +28,7 @@ import matplotlib.pyplot as plt
import numpy as np
sys.path.insert(0, str(Path(__file__).parent))
from _figlib import load_bundle, savefig # noqa: E402
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
@ -76,7 +76,7 @@ def main() -> None:
g, y = _mean_traj(sh, "temperature", val, "heterozygosity")
ax.plot(g, y, "-o", color=c, ms=3, label=lab)
ax.axhline(Hstar_sh, ls=":", color="gray", lw=1, label="$H^*$")
ax.axhline(vae_H, ls="--", color="#2ca02c", lw=1.3, label=f"real VAE (dry): {vae_H:.2f}")
ax.axhline(vae_H, ls="--", color="#2ca02c", lw=1.3, label=f"real VAE (no real data): {vae_H:.2f}")
ax.set(xlabel="generation", ylabel="heterozygosity $H$",
title="VAE regime ($n$=6000, $K$=30): neutral drift is inert;\nsharpening collapses (like the VAE)")
ax.legend(frameon=False, fontsize=8)
@ -85,7 +85,7 @@ def main() -> None:
for val, c, lab in [(1.0, NEU, "neutral (τ=1)"), (0.8, KER, "sharpened (τ=0.8)")]:
g, y = _mean_traj(sh, "temperature", val, "support_size")
ax.plot(g, y, "-o", color=c, ms=3, label=lab)
ax.axhline(vae_sup, ls="--", color="#2ca02c", lw=1.3, label=f"real VAE (dry): {vae_sup:.0f}")
ax.axhline(vae_sup, ls="--", color="#2ca02c", lw=1.3, label=f"real VAE (no real data): {vae_sup:.0f}")
ax.set(xlabel="generation", ylabel="distinct modes alive",
title="Support: neutral holds ~all; sharpening → 1 mode")
ax.legend(frameon=False, fontsize=8)
@ -96,7 +96,7 @@ def main() -> None:
g, y = _mean_traj(sm, "reset", val, "heterozygosity")
ax.plot(g, y, "-", color=c, lw=1.8, label=lab)
ax.axhline(Hstar_sm, ls=":", color="gray", lw=1, label="$H^*$")
ax.axhline(rnn_H, ls="--", color="#2ca02c", lw=1.3, label=f"real RNN (dry): {rnn_H:.2f}")
ax.axhline(rnn_H, ls="--", color="#2ca02c", lw=1.3, label=f"real RNN (no real data): {rnn_H:.2f}")
ax.set(xlabel="generation", ylabel="heterozygosity $H$",
title="RNN regime ($n$=200, $K$=256): neutral → 0;\nsmoothing floors $H$ (like the RNN)")
ax.legend(frameon=False, fontsize=8)
@ -105,15 +105,14 @@ def main() -> None:
for val, c, lab in [(0.0, NEU, "neutral (u=0)"), (0.006, KER, "smoothed (u=0.006)")]:
g, y = _mean_traj(sm, "reset", val, "forward_kl")
ax.plot(g, y, "-", color=c, lw=1.8, label=lab)
ax.axhline(rnn_KL, ls="--", color="#2ca02c", lw=1.3, label=f"real RNN (dry): {rnn_KL:.1f}")
ax.axhline(rnn_KL, ls="--", color="#2ca02c", lw=1.3, label=f"real RNN (no real data): {rnn_KL:.1f}")
ax.set(xlabel="generation", ylabel=r"forward-KL $D(p^*\Vert p)$",
title="Forward-KL: neutral diverges; smoothing plateaus\n(overshoots RNN → prior is truth-like, not uniform)")
ax.legend(frameon=False, fontsize=8)
fig.suptitle("learning kernel — neutral Wright–Fisher fails both neural models, oppositely: "
"the estimator sharpens (VAE) or smooths (RNN)", y=1.0, fontsize=12)
fig.tight_layout()
for d in ("results/kernel_sharpen", "results/kernel_smooth"):
letter_axes(fig)
savefig(fig, d, "kernel")