"""Generate all data figures for the paper (PDF + PNG preview). Fig 3 (audit): (a) 2x4 small-multiple FQ x J-Access scatter, each method highlighted against the full grid in gray; (b) layer profile with the workspace band shaded. Fig 4 (predict): 4x2 small multiples, one per unlearning method: nine checkpoints of pre-attack J-Access vs excess revival with a Theil-Sen fit. Fig 5 (goodhart): two panels sharing lambda: J-Access_K + UDS (left), revival (right); three-seed error bars. Palette: dataviz reference palette (light), <=3 categorical hues per all-pairs panel; identity via panel titles / direct labels, never color alone. One axis per panel. """ import json import matplotlib matplotlib.use("Agg") import matplotlib.pyplot as plt import numpy as np from matplotlib.ticker import MaxNLocator from scipy import stats RES = "/workspace/jspace-unlearning/results" OUT = "/workspace/jspace-unlearning/paper/figs" BLUE, ORANGE, AQUA = "#2a78d6", "#eb6834", "#1baf7a" INK, MUTED, GRIDC = "#0b0b0b", "#52514e", "#e6e5e1" GRAY_PT = "#c9c8c2" plt.rcParams.update({ "font.size": 7.5, "axes.titlesize": 8, "axes.labelsize": 7.5, "xtick.labelsize": 7, "ytick.labelsize": 7, "legend.fontsize": 7, "axes.edgecolor": MUTED, "axes.linewidth": 0.6, "xtick.color": MUTED, "ytick.color": MUTED, "axes.labelcolor": INK, "text.color": INK, "figure.dpi": 200, "savefig.bbox": "tight", }) def style(ax): for s in ("top", "right"): ax.spines[s].set_visible(False) def save(fig, name): fig.savefig(f"{OUT}/{name}.pdf") fig.savefig(f"{OUT}/{name}.png") plt.close(fig) print("saved", name) # ------------------------------------------------------------------ fig 3 def fig3(): grid = json.load(open(f"{RES}/p0a_grid398.json")) # drop runs whose KS p-value underflowed to 0 (would pile up at the # clip value); the paper notes their omission models = {k: v for k, v in grid["models"].items() if v["fq"] > 1e-30} jf, jg = grid["digest"]["j_full"], grid["digest"]["j_gold"] lfq = {k: np.log10(v["fq"]) for k, v in models.items()} methods = sorted({v["method"] for v in models.values()}) fig, axes = plt.subplots(2, 4, figsize=(7.0, 3.6), sharex=True, sharey=True, constrained_layout=True) for ax, meth in zip(axes.flat, methods): style(ax) xs_all = [lfq[k] for k in models] ys_all = [models[k]["jocc10"] for k in models] ax.scatter(xs_all, ys_all, s=4, color=GRAY_PT, linewidths=0, zorder=2, rasterized=True) sel = {k: v for k, v in models.items() if v["method"] == meth} succ = {k: v for k, v in sel.items() if v["fq"] > 0.05 and v["mu"] >= 0.9 * 0.5994} ax.scatter([lfq[k] for k in sel], [v["jocc10"] for v in sel.values()], s=7, color=BLUE, linewidths=0, zorder=3) ax.scatter([lfq[k] for k in succ], [v["jocc10"] for v in succ.values()], s=16, facecolors="none", edgecolors=INK, linewidths=0.7, zorder=4) ax.axhline(jf, color=MUTED, lw=0.7, ls=(0, (4, 2)), zorder=1) ax.axhline(jg, color=MUTED, lw=0.7, ls=(0, (1, 1.5)), zorder=1) ax.set_title(meth, pad=2) ax.set_xlim(-31, 1.5) ax.set_ylim(-0.03, 0.85) fig.supxlabel(r"$\log_{10}$ Forget Quality $\;\longrightarrow\;$" " better behavioral forgetting", fontsize=7.5) fig.supylabel(r"Raw Target Access Rate $A_{10}$", fontsize=7.5) axes[0, 0].text(-30, jf + 0.02, r"$\theta_0$", fontsize=7, color=MUTED) axes[0, 0].text(-30, jg + 0.02, r"$\theta_g$", fontsize=7, color=MUTED) axes[0, 0].annotate("lower residual\naccess", xy=(-23, 0.01), xytext=(-23, 0.13), fontsize=6, color=MUTED, ha="center", va="bottom", arrowprops=dict(arrowstyle="->", color=MUTED, lw=0.7)) handles = [ plt.Line2D([], [], marker="o", ls="", ms=3, color=GRAY_PT, label="all runs (pooled)"), plt.Line2D([], [], marker="o", ls="", ms=3.5, color=BLUE, label="this method's runs"), plt.Line2D([], [], marker="o", ls="", ms=5, mfc="none", mec=INK, mew=0.7, label="passes FQ + utility gates"), plt.Line2D([], [], color=MUTED, lw=0.9, ls=(0, (4, 2)), label=r"Original model $\theta_0$"), plt.Line2D([], [], color=MUTED, lw=0.9, ls=(0, (1, 1.5)), label=r"Retain-only model $\theta_g$"), ] fig.legend(handles=handles, ncol=5, frameon=False, fontsize=6.5, loc="outside upper center", columnspacing=1.0, handletextpad=0.4, handlelength=1.6) save(fig, "fig3a_grid_scatter") prof = json.load(open(f"{RES}/p2b_layer_profile.json")) fig, ax = plt.subplots(figsize=(3.3, 2.1), constrained_layout=True) style(ax) ax.axvspan(11, 14, color=GRIDC, alpha=0.6, zorder=0) series = [("full", "full", BLUE, "-"), ("retain90", "gold (retain-only)", MUTED, (0, (4, 2))), ("NPO", "NPO (best FQ)", ORANGE, "-"), ("RMU", "RMU", AQUA, "-")] for key, label, color, ls in series: p = prof[key]["jocc10_by_layer_prefix"] xs = [int(k) for k in p if k != "15"] ys = [p[str(x)] for x in xs] ax.plot(xs, ys, color=color, lw=1.4, ls=ls, zorder=3) ax.annotate(label, (xs[-1], ys[-1]), textcoords="offset points", xytext=(3, 0), fontsize=6.5, color=INK, va="center") ax.set_xlabel("lens layer") ax.set_ylabel("J-Access (per layer)") ax.set_xlim(0, 17) ax.set_xticks(range(0, 15, 2)) ax.text(12.5, 0.55, "workspace\nband", fontsize=6.5, color=MUTED, ha="center") save(fig, "fig3b_layer_profile") # ------------------------------------------------------------------ fig 4 def fig4(): h2 = json.load(open(f"{RES}/h2_ckpt_stats.json")) methods = sorted({k.split("_")[0] for k in h2}) fig, axes = plt.subplots(4, 2, figsize=(3.1, 4.4), sharey=True, constrained_layout=True) for ax, meth in zip(axes.flat, methods): style(ax) xs, ye = map(np.array, zip(*[ (v["jocc_v2_mean"], v["excess_rate"]) for k, v in h2.items() if k.split("_")[0] == meth])) ax.axhline(0, color=GRAY_PT, lw=0.6) ax.scatter(xs, ye, s=8, color=BLUE, linewidths=0, zorder=3) slope, intercept, _, _ = stats.theilslopes(ye, xs) xf = np.array([xs.min(), xs.max()]) ax.plot(xf, intercept + slope * xf, color=INK, lw=0.9, ls=(0, (4, 2)), zorder=2) ax.set_title(meth, pad=2) ax.xaxis.set_major_locator(MaxNLocator(3)) ax.set_ylim(-0.6, 0.5) ax.set_yticks([-0.4, 0, 0.4]) fig.supxlabel("pre-attack J-Access", fontsize=7.5) fig.supylabel("excess revival", fontsize=7.5) save(fig, "fig4_prediction") # ------------------------------------------------------------------ fig 5 def fig5(): seeds = json.load(open(f"{RES}/p1b_seeds.json")) uds = json.load(open(f"{RES}/h3_uds_comparison.json")) lams = [0, 5, 10] def agg(field): m, s = [], [] for lam in lams: v = [r[field] for r in seeds.values() if r["lam"] == lam] m.append(np.mean(v)) s.append(np.std(v, ddof=1)) return np.array(m), np.array(s) jm, js = agg("JOcc2") rm, rs = agg("within_revive") um = [uds[f"WDd_l{lam}"]["UDS"] for lam in lams] fig, (a, b) = plt.subplots(1, 2, figsize=(3.92, 2.4), sharex=True, constrained_layout=True) # all text (labels, tick labels, annotations) left off: added by hand later for ax in (a, b): style(ax) ax.set_xticks(lams) ax.set_xticklabels([]) a.errorbar(lams, jm, yerr=js, color=BLUE, lw=1.4, marker="o", ms=3.5, capsize=2, elinewidth=0.8) a.plot(lams, um, color=MUTED, lw=1.2, ls=(0, (4, 2)), marker="s", ms=3) a.set_ylim(0.45, 0.85) a.set_yticks([0.5, 0.6, 0.7, 0.8]) a.set_yticklabels([]) b.errorbar(lams, rm, yerr=rs, color=ORANGE, lw=1.4, marker="o", ms=3.5, capsize=2, elinewidth=0.8) b.set_ylim(0.24, 0.42) b.set_yticks([0.25, 0.30, 0.35, 0.40]) b.set_yticklabels([]) save(fig, "fig5_goodhart") if __name__ == "__main__": import os os.makedirs(OUT, exist_ok=True) fig3() fig4() fig5()