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7956103 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 | """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).")
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