| """Reproduce the released quantitative figures from main_table.csv.""" | |
| from pathlib import Path | |
| import csv | |
| import numpy as np | |
| import matplotlib | |
| matplotlib.use('Agg') | |
| import matplotlib.pyplot as plt | |
| PKG=Path(__file__).resolve().parent | |
| MODELS=['idefics3','qwen9b','qwen','internvl35','pixtral12b','llava15'] | |
| NAMES=['Idefics3-8B','Qwen3.5-9B','Qwen3.5-27B','InternVL3.5-8B','Pixtral-12B','LLaVA-1.5-7B'] | |
| with (PKG/'results/main_table.csv').open(encoding='utf-8-sig',newline='') as handle: table=list(csv.DictReader(handle)) | |
| for row in table: | |
| for key in row: | |
| if key not in {'model','scoring','cohort','scope','donor_category'} and row[key]: row[key]=float(row[key]) | |
| row['pairs']=int(row['pairs']);row['targets']=int(row['targets']) | |
| plt.rcParams.update({'font.family':'sans-serif','font.sans-serif':['Arial','DejaVu Sans'],'font.size':9,'svg.fonttype':'none','pdf.fonttype':42, | |
| 'axes.spines.top':False,'axes.spines.right':False}) | |
| fig,axes=plt.subplots(1,2,figsize=(10,5.1),sharey=True) | |
| for ax,scope,title in zip(axes,['all','human_majority_model_reliable'],['All 500 target images','Human majority + model checks']): | |
| for i,model in enumerate(MODELS): | |
| r=next(r for r in table if r['model']==model and r['scoring']=='format_tolerant' and r['cohort']=='new_500' and r['scope']==scope) | |
| t,n=[100*r[k]/r['pairs'] for k in ('T_follow','N-near_follow')] | |
| ax.plot([n,t],[i,i],color='#a5a5a5',lw=2) | |
| ax.scatter(t,i,color='#b74b43',s=50,label='Covering target' if i==0 else None) | |
| ax.scatter(n,i,color='#277b87',s=50,marker='s',label='Near target' if i==0 else None) | |
| ax.text(61,i,f"n={r['pairs']}; images={r['targets']}",fontsize=7,va='center') | |
| ax.set_yticks(range(6),NAMES);ax.set_xlim(-2,89);ax.set_xticks([0,20,40,60]);ax.set_xlabel('Donor-category answers (%)') | |
| ax.set_title(title);ax.grid(axis='x',alpha=.18) | |
| axes[0].invert_yaxis();axes[0].legend(loc='lower left',bbox_to_anchor=(0,1.08),ncol=2,fontsize=8) | |
| fig.suptitle('Spatial placement changes directed donor answers',y=1.02,fontsize=13) | |
| fig.tight_layout() | |
| directory=PKG/'figures';directory.mkdir(exist_ok=True) | |
| fig.savefig(directory/'main_comparison.png',dpi=300,bbox_inches='tight') | |
| fig.savefig(directory/'main_comparison.pdf',bbox_inches='tight') | |
| fig.savefig(directory/'main_comparison.svg',bbox_inches='tight') | |
| plt.close(fig) | |
| fig,axes=plt.subplots(1,2,figsize=(10,5),sharey=True) | |
| colors=['#43836d','#b74b43','#96938c','#d2b464','#866999'];labels=['Target correct','Donor answer','Other error','UNKNOWN','Ambiguous'] | |
| for ax,condition,title in zip(axes,['T','N-near'],['Covering target','Near target']): | |
| left=np.zeros(6) | |
| for suffix,color,label in zip(['correct','follow','other','unknown','ambiguous'],colors,labels): | |
| counts=np.array([next(r for r in table if r['model']==m and r['scoring']=='format_tolerant' and r['cohort']=='new_500' and r['scope']=='all')[condition+'_'+suffix]/10 for m in MODELS]) | |
| ax.barh(range(6),counts,left=left,color=color,label=label,height=.65);left+=counts | |
| ax.set_title(title);ax.set_xlim(0,100);ax.set_yticks(range(6),NAMES);ax.set_xlabel('Answers (%)') | |
| axes[0].invert_yaxis();axes[1].legend(loc='upper center',bbox_to_anchor=(-.15,-.15),ncol=3,fontsize=8) | |
| fig.tight_layout() | |
| fig.savefig(directory/'answer_types.png',dpi=300,bbox_inches='tight') | |
| fig.savefig(directory/'answer_types.pdf',bbox_inches='tight') | |
| fig.savefig(directory/'answer_types.svg',bbox_inches='tight') | |
| plt.close(fig) | |