| """Large-label figures from a mechanically normalized phase-2 result file.""" |
| import argparse |
| import json |
| from pathlib import Path |
| import matplotlib |
| matplotlib.use("Agg") |
| import matplotlib.pyplot as plt |
| import numpy as np |
|
|
| BLUE="#0F4D92" |
| RED="#B64342" |
| TEAL="#42949E" |
| VIOLET="#9A4D8E" |
|
|
|
|
| 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"}) |
|
|
|
|
| def save(fig,folder,name): |
| folder.mkdir(parents=True,exist_ok=True) |
| for ext in ("pdf","png"): |
| fig.savefig(folder/(name+"."+ext),dpi=300,bbox_inches="tight",pad_inches=.12) |
| plt.close(fig) |
|
|
|
|
| def main(folder): |
| data=json.loads((folder/"plot_data.json").read_text()) |
| rows=data["rows"] |
| out=folder/"figures" |
| style() |
| n=max(r["n"] for r in rows) |
| budgets=sorted({r["budget"] for r in rows})[-2:] |
| fig,axs=plt.subplots(1,2,figsize=(13.4,5.8)) |
| fig.subplots_adjust(left=.10,right=.985,bottom=.28,top=.86,wspace=.33) |
| for ax,B in zip(axs,budgets): |
| selected=[r for r in rows if r["n"]==n and abs(r["budget"]-B)<1e-10] |
| if len(selected)!=1: |
| raise ValueError("Expected one frozen finalist per n,budget") |
| row=selected[0] |
| cases=sorted(row["cases"],key=lambda c:c["K"]) |
| K=np.array([c["K"] for c in cases]) |
| lo=np.array([c["L"] for c in cases]);hi=np.array([c["U"] for c in cases]) |
| if np.any(hi<lo): |
| raise ValueError("Invalid certified interval") |
| middle=(lo+hi)/2 |
| ax.errorbar(K,middle,yerr=np.stack([middle-lo,hi-middle]),fmt="o-", |
| color=BLUE,capsize=6,capthick=2,label="Certified optimum interval") |
| ax.axhline(row["model_tv"],color=RED,ls="--",label="Model TV (serial baseline)") |
| ax.fill_between(K,row["model_tv"],lo,where=lo>row["model_tv"], |
| color=TEAL,alpha=.14,label="Unavoidable excess") |
| ax.set(xlabel="Allowed rounds $K$",ylabel="Total variation",xticks=[2,3,4], |
| title=rf"$n={n},\ B={B:g}$") |
| ax.set_ylim(bottom=0) |
| kl_note=f"Certified KL upper ≈ {row['actual_kl']:.5f}" |
| if not row["budget_matched"]: |
| kl_note+="\nBudget inactive (weight cap)" |
| ax.text(.04,.05,kl_note,transform=ax.transAxes, |
| fontsize=18,color="#4D4D4D",va="bottom",linespacing=1.2) |
| handles,labels=axs[0].get_legend_handles_labels() |
| fig.legend(handles,labels,loc="lower center",bbox_to_anchor=(.54,.01),ncol=2,fontsize=18) |
| save(fig,out,"certified_depth_cost") |
|
|
| fig,ax=plt.subplots(figsize=(8.8,6.3)) |
| fig.subplots_adjust(left=.18,right=.98,bottom=.19,top=.88) |
| 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],key=lambda r:r["budget"]) |
| gaps=[next(c["gap_lower"] for c in r["cases"] if c["K"]==3) for r in group] |
| ax.plot([r["budget"] for r in group],gaps,color=color,marker=marker,label=rf"$n={n}$") |
| ax.axhline(0,color="#767676",lw=1,ls=":") |
| ax.set(xlabel="Joint-KL budget $B$",ylabel="Excess TV lower bound", |
| title="Selected finite-depth witnesses ($K=3$)") |
| ax.set_xticks(sorted({r["budget"] for r in rows})) |
| ax.legend(loc="best",fontsize=20) |
| save(fig,out,"certified_execution_gap") |
|
|
| candidates=data["candidates"] |
| fig,axs=plt.subplots(1,2,figsize=(13.4,6.2)) |
| fig.subplots_adjust(left=.10,right=.985,bottom=.34,top=.85,wspace=.32) |
| specs=[("single","Single alternative",RED,"^"), |
| ("manuscript","Theorem ladder",BLUE,"D"), |
| ("relaxed","Relaxed two-level",TEAL,"o")] |
| for ax,n in zip(axs,[min(r["n"] for r in rows),max(r["n"] for r in rows)]): |
| for family,label,color,marker in specs: |
| group=[r for r in candidates if r["n"]==n and r["family"]==family] |
| ax.scatter([r["actual_kl"] for r in group],[r["gap"] for r in group], |
| c=color,marker=marker,s=90,label=label,alpha=.9, |
| edgecolors="white",linewidths=.5) |
| ax.axhline(0,color="#767676",ls=":",lw=1) |
| ax.set(xlabel="Actual joint KL",ylabel="Excess TV (LP screening)", |
| title=rf"Candidate search: $n={n},\ K=3$") |
| handles,labels=axs[0].get_legend_handles_labels() |
| fig.legend(handles,labels,loc="lower center",bbox_to_anchor=(.54,.105),ncol=3,fontsize=18) |
| fig.text(.54,.012, |
| "All candidates: floating-point screening; only selected witnesses independently certified.\n" |
| "Finite candidate search; no claim of an outer optimum.", |
| ha="center",va="bottom",fontsize=17,color="#4D4D4D",linespacing=1.2) |
| save(fig,out,"candidate_search_diagnostics") |
| print("Exported three PDF/PNG figure pairs.",flush=True) |
|
|
|
|
| if __name__=="__main__": |
| ap=argparse.ArgumentParser() |
| ap.add_argument("--run-dir",type=Path,required=True) |
| main(ap.parse_args().run_dir) |
|
|
