| """Publication figures from completed exact/certified phase-three outputs.""" |
| import argparse |
| from fractions import Fraction |
| import json |
| from pathlib import Path |
| import matplotlib |
| matplotlib.use("Agg") |
| import matplotlib.pyplot as plt |
| import numpy as np |
|
|
| BLUE, RED, TEAL, VIOLET, GRAY = "#0F4D92", "#B64342", "#42949E", "#9A4D8E", "#555555" |
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|
|
| def val(x): |
| return float(Fraction(x["rational"] if isinstance(x, dict) else x)) |
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|
| def style(): |
| plt.rcParams.update({"font.family": "DejaVu Sans", "font.size": 19, |
| "axes.labelsize": 22, "axes.titlesize": 20, "xtick.labelsize": 18, |
| "ytick.labelsize": 18, "legend.fontsize": 18, "legend.frameon": False, |
| "axes.spines.top": False, "axes.spines.right": False, "axes.linewidth": 1.5, |
| "lines.linewidth": 2.8, "lines.markersize": 8, "axes.titlepad": 14, |
| "pdf.fonttype": 42, "ps.fonttype": 42, "savefig.facecolor": "white"}) |
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|
|
| def save(fig, folder, name): |
| folder.mkdir(parents=True, exist_ok=True) |
| for ext in ("pdf", "png"): |
| fig.savefig(folder / f"{name}.{ext}", dpi=300, bbox_inches="tight", pad_inches=.16) |
| plt.close(fig) |
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|
|
| def schedules(data, folder): |
| assert data["status"] == "complete" and data["self_test"]["passed"] |
| rows = data["rows"] |
| n = max(r["n"] for r in rows) |
| specs = [("geometric", "tv_exact", "Geometric, fixed labels", BLUE, "o", "-"), |
| ("equal", "tv_exact", "Equal, fixed labels", RED, "s", "-"), |
| ("best_fixed", "tv_exact", "Best fixed-label batches", TEAL, "^", "-"), |
| ("best_symmetrized", "symmetrized_tv_exact", "Best batches + random labels", VIOLET, "x", "--")] |
| fig, axes = plt.subplots(1, 2, figsize=(14.2, 7.5)) |
| fig.subplots_adjust(left=.09, right=.985, bottom=.37, top=.87, wspace=.30) |
| for ax, K in zip(axes, (3, 4)): |
| group = sorted((r for r in rows if r["n"] == n and r["K"] == K), |
| key=lambda r: Fraction(r["budget"])) |
| B = [val(r["budget"]) for r in group] |
| for key, field, label, color, marker, line in specs: |
| ax.plot(B, [val(r[key][field]) for r in group], color=color, |
| marker=marker, ls=line, label=label) |
| lo = np.array([val(r["all_policy"]["lower"]) for r in group]) |
| hi = np.array([val(r["all_policy"]["upper"]) for r in group]) |
| mid = (lo+hi)/2 |
| ax.errorbar(B, mid, yerr=np.array([mid-lo, hi-mid]), color=GRAY, |
| fmt="d:", capsize=5, markerfacecolor="white", zorder=6, |
| label="Certified all-policy optimum") |
| ax.set(xlabel="Joint-KL budget $B$", ylabel="Full-output TV", |
| title=rf"$n={n}$ pairs, $K={K}$ rounds", xticks=B) |
| ax.set_ylim(bottom=0) |
| h, l = axes[0].get_legend_handles_labels() |
| fig.legend(h, l, loc="lower center", bbox_to_anchor=(.53,.055), ncol=2, |
| columnspacing=1.5, handlelength=2.6, fontsize=18) |
| fig.text(.53, .015, "One frozen model per budget; identical model across round counts.", |
| ha="center", fontsize=17, color=GRAY) |
| save(fig, folder, "fixed_schedule_benchmark") |
|
|
| fig, axes = plt.subplots(1, 2, figsize=(13.8, 6.4)) |
| fig.subplots_adjust(left=.10, right=.985, bottom=.27, top=.86, wspace=.30) |
| for ax, K in zip(axes, (3, 4)): |
| for n, color, marker in zip(sorted({r["n"] for r in rows}), |
| (BLUE, RED, TEAL), ("o", "s", "^")): |
| group = sorted((r for r in rows if r["n"] == n and r["K"] == K), |
| key=lambda r: Fraction(r["budget"])) |
| lo = np.array([val(r["geometric_excess_over_optimum_lower"]) for r in group]) |
| hi = np.array([val(r["geometric_excess_over_optimum_upper"]) for r in group]) |
| mid = (lo+hi)/2 |
| ax.errorbar([val(r["budget"]) for r in group], mid, |
| yerr=np.array([mid-lo,hi-mid]), fmt=marker+"-", color=color, |
| capsize=5, label=rf"$n={n}$") |
| ax.axhline(0, color=GRAY, lw=1, ls=":") |
| ax.set(xlabel="Joint-KL budget $B$", ylabel="TV above all-policy optimum", |
| title=rf"Geometric fixed labels: $K={K}$", xticks=[.02,.05,.10]) |
| ax.ticklabel_format(axis="y", style="plain", useOffset=False) |
| h,l = axes[0].get_legend_handles_labels() |
| fig.legend(h,l,loc="lower center",bbox_to_anchor=(.54,.07),ncol=3,fontsize=20) |
| fig.text(.54,.015,"Arithmetic enclosures; connecting lines are guides, not fitted laws.", |
| ha="center",fontsize=17,color=GRAY) |
| save(fig,folder,"geometric_schedule_overhead") |
|
|
|
|
| def biased(data, folder): |
| cases = [c for c in data["cases"] if c.get("status") == "certified"] |
| if not cases: |
| raise ValueError("No certified biased cases available for a result figure") |
| ns = sorted({c["n"] for c in cases}) |
| fig, axes = plt.subplots(1, 2, figsize=(13.8, 7.2)) |
| fig.subplots_adjust(left=.10,right=.985,bottom=.34,top=.86,wspace=.31) |
| round_specs = [(2,BLUE,"o"),(3,RED,"s"),(4,TEAL,"^")] |
| for ax,n in zip(axes,ns[:2]): |
| group = [c for c in cases if c["n"] == n] |
| model = next(m for m in data["models"] if m["n"] == n) |
| biases = sorted({Fraction(c["a"]) for c in data["cases"] if c["n"] == n}) |
| for K,color,marker in round_specs: |
| if not any(c["K"] == K for c in data["cases"] if c["n"] == n): |
| continue |
| found = {Fraction(c["a"]):c for c in group if c["K"] == K} |
| lo = np.array([val(found[a]["certificate"]["tv_lower"]) if a in found else np.nan for a in biases]) |
| hi = np.array([val(found[a]["certificate"]["tv_upper"]) if a in found else np.nan for a in biases]) |
| mid=(lo+hi)/2 |
| ax.errorbar(list(map(float,biases)),mid,yerr=np.array([mid-lo,hi-mid]), |
| fmt=marker+"-",color=color,capsize=5,label=rf"$K={K}$") |
| ax.axhline(val(model["diagnostics"]["model_tv"]),color=GRAY,ls="--",label="Serial model TV") |
| ax.set(xlabel="Representative bias $a$",ylabel="Certified optimum TV", |
| title=rf"$n={n}$ pairs",xticks=list(map(float,biases))) |
| endpoints = [val(model["diagnostics"]["model_tv"])] |
| endpoints += [val(c["certificate"][key]) for c in group for key in ("tv_lower","tv_upper")] |
| span = max(endpoints)-min(endpoints) |
| padding = max(span*.16, .0005) |
| ax.set_ylim(min(endpoints)-padding,max(endpoints)+padding) |
| missing = sum(c.get("status") != "certified" for c in data["cases"] if c["n"] == n) |
| if missing: |
| ax.text(.04,.04,f"{missing} missing cases (see report)",transform=ax.transAxes,fontsize=17) |
| if len(ns)==1: |
| n=ns[0] |
| group=sorted((m for m in data["models"] if m["n"]==n),key=lambda m:Fraction(m["a"])) |
| axes[1].plot([float(Fraction(m["a"])) for m in group], |
| [val(m["diagnostics"]["all_context_head_error"]) for m in group], |
| color=VIOLET,marker="d") |
| axes[1].axhline(.25,color=GRAY,ls=":") |
| axes[1].set(xlabel="Representative bias $a$",ylabel="Worst conditional-head TV", |
| title="All-context accuracy check",xticks=[.25,.4,.5]) |
| axes[1].set_ylim(bottom=0) |
| h,l=axes[0].get_legend_handles_labels() |
| fig.legend(h,l,loc="lower center",bbox_to_anchor=(.54,.14),ncol=4,fontsize=18) |
| fig.text(.54,.015,"Vertical axes zoomed; n=6 evaluated at K=3,4 only.\nFixed mismatch mixture and KL at each n.\nUniform head errors are measured, not assumed equal across biases.", |
| ha="center",va="bottom",fontsize=17,color=GRAY,linespacing=1.2) |
| save(fig,folder,"biased_depth_sensitivity") |
|
|
|
|
| if __name__ == "__main__": |
| parser=argparse.ArgumentParser() |
| parser.add_argument("--run-dir",type=Path,required=True) |
| parser.add_argument("--figure-dir",type=Path) |
| args=parser.parse_args() |
| style() |
| output=args.figure_dir or args.run_dir/"figures" |
| schedules(json.loads((args.run_dir/"schedules.json").read_text()),output) |
| biased(json.loads((args.run_dir/"biased_results.json").read_text()),output) |
| print("Exported three PDF/PNG figure pairs.",flush=True) |
|
|
