"""Readable scientific figures; style inspired by ChenLiu-1996/figures4papers. Data are computed independently. No decorative error bars or smoothing. """ from __future__ import annotations import argparse import json from pathlib import Path import matplotlib matplotlib.use("Agg") import matplotlib.pyplot as plt import numpy as np from matplotlib.ticker import ScalarFormatter COLORS={"single":"#B64342","two_level":"#0F4D92","drift_matched_single":"#42949E"} LABELS={"single":"Single alternative","two_level":"Two-level ladder","drift_matched_single":"Drift-matched single"} STYLES={"single":"--","two_level":"-","drift_matched_single":"-."} 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,"axes.spines.top":False,"axes.spines.right":False, "axes.linewidth":1.5,"lines.linewidth":2.8,"lines.markersize":6.5, "legend.frameon":False,"pdf.fonttype":42,"ps.fonttype":42,"svg.fonttype":"none", "savefig.facecolor":"white","figure.facecolor":"white","axes.titlepad":14}) def export(fig, folder, name): folder.mkdir(parents=True,exist_ok=True) fig.savefig(folder/(name+".pdf"),bbox_inches="tight",pad_inches=.12) fig.savefig(folder/(name+".png"),bbox_inches="tight",pad_inches=.12,dpi=300) plt.close(fig) def e1_figure(data,folder): records=data["records"] n=np.array([r["n"] for r in records]) fig,axs=plt.subplots(1,2,figsize=(13.4,5.5)) fig.subplots_adjust(left=.085,right=.985,bottom=.28,top=.89,wspace=.32) series=[("model_tv",r"Model TV", "#0F4D92","o","-"), ("head_tv_upper_bound",r"Head-TV bound $\Delta_n$","#42949E","s","--"), ("execution_tv",r"Two-round output TV","#B64342","^","-")] for key,label,color,marker,linestyle in series: axs[0].plot(n,[r[key] for r in records],label=label,color=color,marker=marker,ls=linestyle,markevery=2) axs[0].set(xscale="log",xlabel="Number of pairs $n$",ylabel="Total variation",ylim=(0,1.035),title="(a) Model versus delivered samples") axs[1].plot(n,[r["kl"] for r in records],"o-",color="#0F4D92",label="Actual joint KL",markevery=2) axs[1].plot(n,[r["kl_upper_bound"] for r in records],"--",color="#B64342",label=r"Bound $-\log(1-w_n)$",linewidth=2) axs[1].set(xscale="log",xlabel="Number of pairs $n$",ylabel=r"$\mathrm{KL}(P\Vert R)$ (nats)",title="(b) Model error decreases") axs[1].text(.05,.08,"Actual KL nearly equals its bound",transform=axs[1].transAxes,fontsize=16,color="#4D4D4D") for ax in axs: ax.set_xticks([2**8,2**12,2**16,2**20],[r"$2^8$",r"$2^{12}$",r"$2^{16}$",r"$2^{20}$"]) ax.legend(loc="upper center",bbox_to_anchor=(.5,-.25),fontsize=18) export(fig,folder,"e1_model_vs_execution") def canon_model(name): aliases={"ladder":"two_level","two_level_ladder":"two_level","matched_single":"drift_matched_single","drift_matched":"drift_matched_single"} return aliases.get(name,name) def e2_figure(data,folder): records=data["records"] for r in records: r["model"]=canon_model(r["model"]) models=["single","two_level","drift_matched_single"] n=1024 fig,axs=plt.subplots(1,2,figsize=(13.4,5.6)) fig.subplots_adjust(left=.09,right=.985,bottom=.29,top=.88,wspace=.34) for model in models: rows=sorted([r for r in records if r["n"]==n and r["model"]==model and r["c"]>0],key=lambda r:r["c"]) c=np.array([r["c"] for r in rows]) axs[0].plot(c,[r["tv"] for r in rows],label=LABELS[model],color=COLORS[model],ls=STYLES[model],marker="o",markevery=2) axs[1].plot(c,[r["mean_drift"] for r in rows],label=LABELS[model],color=COLORS[model],ls=STYLES[model],marker="o",markevery=2) axs[0].set(xscale="log",xlabel="Pilot pairs $c$",ylabel="Delivered-sample TV",title=r"(a) Three-round output, $n=1024$") axs[1].set(xscale="log",xlabel="Pilot pairs $c$",ylabel=r"$\mathbb{E}[p_c(M)-\tau]$",title="(b) Actual-pilot posterior drift") for ax in axs: ax.set_xticks([1,8,64,512],["1","8","64","512"]) ax.set_ylim(bottom=0) handles,labels=axs[0].get_legend_handles_labels() fig.legend(handles,labels,loc="lower center",bbox_to_anchor=(.54,.035),ncol=3,fontsize=18,columnspacing=1.2,handlelength=2.2) export(fig,folder,"e2_pilot_mechanism") fig,axs=plt.subplots(2,2,figsize=(13.4,10.8)) fig.subplots_adjust(left=.1,right=.985,bottom=.14,top=.94,hspace=.48,wspace=.30) for ax,n in zip(axs.flat,[64,256,1024,4096]): for model in models: rows=sorted([r for r in records if r["n"]==n and r["model"]==model and r["c"]>0],key=lambda r:r["c"]) ax.plot([r["c"] for r in rows],[r["tv"] for r in rows],label=LABELS[model],color=COLORS[model],ls=STYLES[model],marker="o",markevery=2) ax.set(xscale="log",xlabel="Pilot pairs $c$",ylabel="Delivered-sample TV",title=f"$n={n}$") ax.set_ylim(bottom=0) handles,labels=axs.flat[0].get_legend_handles_labels() fig.legend(handles,labels,loc="lower center",bbox_to_anchor=(.54,.015),ncol=3,fontsize=18,columnspacing=1.2) export(fig,folder,"e2_all_dimensions") def e3_figure(data,folder): # Schema is kept explicit; fail instead of silently plotting the wrong field. records=data["cases"] fig,axs=plt.subplots(1,2,figsize=(13.4,5.6)) fig.subplots_adjust(left=.09,right=.985,bottom=.29,top=.88,wspace=.34) for ax,model in zip(axs,["single","two_level"]): rows=sorted([r for r in records if r["model"]==model and r["rounds"]==3],key=lambda r:r["n"]) for key,label,color,ls,marker in [("optimal_tv","Randomized-policy LP","#0F4D92","-","o"), ("geometric_heuristic","Geometric schedule","#B64342","--","s"), ("equal","Equal batches","#42949E","-.","^"), ("best_fixed_completion_grid","Best pilot in grid","#9A4D8E",":","D")]: values=[r[key] if key=="optimal_tv" else r["baselines"][key]["tv"] for r in rows] ax.plot([r["n"] for r in rows],values,label=label,color=color,ls=ls,marker=marker) ax.set(xlabel="Number of pairs $n$",ylabel="Delivered-sample TV",title=LABELS[model]+r", $K=3$") ax.set_xticks([2,4,6,8]); ax.set_ylim(bottom=0) handles,labels=axs[0].get_legend_handles_labels() fig.legend(handles,labels,loc="lower center",bbox_to_anchor=(.53,.015),ncol=2,fontsize=18,columnspacing=1.5) export(fig,folder,"e3_fixed_oracle_policy_optimum") if __name__ == "__main__": parser=argparse.ArgumentParser() parser.add_argument("--run-dir",type=Path,required=True) args=parser.parse_args() style() folder=args.run_dir/"figures" e1_figure(json.loads((args.run_dir/"e1.json").read_text()),folder) e2_figure(json.loads((args.run_dir/"e2.json").read_text()),folder) e3_figure(json.loads((args.run_dir/"e3.json").read_text()),folder) print("Exported four figures (PDF + 300-dpi PNG).")