Datasets:
Formats:
csv
Languages:
English
Size:
10K - 100K
Tags:
recursive-self-improvement
symmetry-reduction
finite-groups
exact-compilation
dynamic-programming
reproducible-research
| """Rebuild paper figures and a compact metric file from measured JSON outputs. | |
| Requires matplotlib and numpy; the research algorithms themselves do not. | |
| """ | |
| from pathlib import Path | |
| import json, statistics, hashlib | |
| import numpy as np | |
| import matplotlib | |
| matplotlib.use('Agg') | |
| import matplotlib.pyplot as plt | |
| from matplotlib.colors import ListedColormap, BoundaryNorm | |
| ROOT=Path(__file__).resolve().parent | |
| main=json.loads((ROOT/'holdout_full/results.json').read_text()) | |
| bounded=json.loads((ROOT/'holdout_budget/results.json').read_text()) | |
| heap=json.loads((ROOT/'heap_holdout_full/results.json').read_text()) | |
| heap_bounded=json.loads((ROOT/'heap_holdout_budget/results.json').read_text()) | |
| def stats(xs): | |
| return {'mean':statistics.mean(xs),'sd':statistics.stdev(xs), | |
| 'min':min(xs),'max':max(xs),'n':len(xs)} | |
| metrics={} | |
| for condition in ('structured','asymmetric','constant'): | |
| records=[s for r in main['results']+heap['results'] if r['condition']==condition | |
| for s in r['summaries']] | |
| budget_records=[s for r in bounded['results']+heap_bounded['results'] | |
| if r['condition']==condition for s in r['summaries']] | |
| methods={} | |
| for name in sorted({s['method'] for s in records}): | |
| rows=[s for s in records if s['method']==name] | |
| bs=[s for s in budget_records if s['method']==name] | |
| methods[name]={'work':stats([s['work'] for s in rows]), | |
| 'work_with_full_oracle':stats([s['total_with_shared_oracle'] for s in rows]), | |
| 'wall_ms':stats([s['wall_ns']/1e6 for s in rows]), | |
| 'fresh_correct':sum(s['fresh_correct'] for s in rows), | |
| 'wrong':sum(s['wrong'] for s in rows), | |
| 'budget_correct':sum(s['fresh_correct'] for s in bs), | |
| 'scalar_slots':stats([s['scalar_slots'] for s in rows])} | |
| metrics[condition]=methods | |
| palette={'recursive':'#146B75','fast':'#A45A32','best_first':'#617481'} | |
| labels={'recursive':'Learned symmetry','fast':'Simple fast compiler','best_first':'Best-first heap'} | |
| plt.rcParams.update({'font.family':'DejaVu Sans','font.size':10, | |
| 'axes.spines.top':False,'axes.spines.right':False, | |
| 'axes.labelcolor':'#283A45','text.color':'#283A45'}) | |
| fig,ax=plt.subplots(figsize=(8.2,4.0)) | |
| conditions=('structured','asymmetric','constant') | |
| for j,name in enumerate(('recursive','fast','best_first')): | |
| means=[metrics[c][name]['work']['mean']/1000 for c in conditions] | |
| errs=[metrics[c][name]['work']['sd']/1000 for c in conditions] | |
| x=np.arange(3)+(j-1)*.24 | |
| bars=ax.bar(x,means,yerr=errs,color=palette[name],label=labels[name], | |
| width=.22,error_kw={'elinewidth':.8,'capsize':2}) | |
| for bar,val in zip(bars,means): | |
| ax.text(bar.get_x()+bar.get_width()/2,val+27,f'{val:.1f}', | |
| ha='center',va='bottom',fontsize=8.5) | |
| ax.set_xticks(range(3),['Structured, nonconstant','Asymmetric control','Constant control']) | |
| ax.set_ylabel('Charged algorithm work (thousands)') | |
| ax.grid(axis='y',color='#DFE6E8',linewidth=.6);ax.set_axisbelow(True) | |
| ax.set_ylim(0,1060) | |
| ax.legend(loc='upper right',fontsize=9,frameon=False) | |
| fig.tight_layout() | |
| fig.savefig(ROOT/'work_comparison.pdf',bbox_inches='tight') | |
| fig.savefig(ROOT/'work_comparison.png',dpi=160,bbox_inches='tight') | |
| plt.close(fig) | |
| fig,axes=plt.subplots(1,2,figsize=(8.2,3.6),gridspec_kw={'width_ratios':[1.2,1]}) | |
| arr=[] | |
| for j in range(1,5): | |
| K={a*(16//2**j) for a in range(2**j)} | |
| arr.append([0 if x in K else 1 if x-16 in K else 2 for x in range(32)]) | |
| cmap=ListedColormap(['#CFE9E5','#DAA66F','#E4E8EB']) | |
| im=axes[0].imshow(arr,cmap=cmap,norm=BoundaryNorm([-.5,.5,1.5,2.5],3),aspect='auto') | |
| axes[0].set_yticks(range(4),['h1 (size 2)','h2 (size 4)', | |
| 'h3 (size 8)','h4 (size 16)']) | |
| axes[0].set_xticks([0,7,16,23,31],['r0','r7','s','r7s','r15s']) | |
| axes[0].axvline(15.5,color='white',linewidth=2) | |
| axes[0].set_title('Distinct successor kernels',fontsize=11,fontweight='bold',pad=12) | |
| axes[0].set_xlabel('Boundary elements of D16') | |
| cbar=fig.colorbar(im,ax=axes[0],ticks=[0,1,2],orientation='horizontal',fraction=.12,pad=.22) | |
| cbar.ax.set_xticklabels(['Cost 0','Cost c','Cost M']) | |
| axes[1].plot([2,3,4],[512,256,128],marker='o',linewidth=2,color=palette['recursive'],label='Discover new symmetry') | |
| axes[1].plot([2,3,4],[512,512,512],marker='s',linestyle='--',color=palette['fast'],label='Only inherit h1 symmetry') | |
| axes[1].set_xticks([2,3,4],['Acquire h2','Acquire h3','Acquire h4']) | |
| axes[1].set_ylabel('Candidate pairs (not full work)') | |
| axes[1].set_ylim(0,600);axes[1].grid(axis='y',color='#DFE6E8',linewidth=.6) | |
| axes[1].set_title('Future acquisition',fontsize=11,fontweight='bold',pad=12) | |
| axes[1].legend(fontsize=8,loc='lower left') | |
| fig.tight_layout(w_pad=2) | |
| fig.savefig(ROOT/'recursive_ladder.pdf',bbox_inches='tight') | |
| fig.savefig(ROOT/'recursive_ladder.png',dpi=160,bbox_inches='tight') | |
| plt.close(fig) | |
| metrics['_provenance']={'source_sha256':main['source_sha256'], | |
| 'heap_sha256':heap['source_sha256'], | |
| 'seeds':list(range(101,121)), | |
| 'scope':'Means and sample SDs of actual instrumented runs. Full oracle work reported separately.'} | |
| (ROOT/'paper_metrics.json').write_text(json.dumps(metrics,indent=2)) | |
| print('Figures and paper_metrics.json built from verified measured files.') | |
| for condition in ('structured','asymmetric','constant'): | |
| best=min(('fast','best_first','dense','sequential'),key=lambda n:metrics[condition][n]['work']['mean']) | |
| a=metrics[condition]['recursive']['work']['mean'];b=metrics[condition][best]['work']['mean'] | |
| print(condition,'best simple',best,'ratio baseline/proposed',b/a, | |
| 'ratio incl full oracle',metrics[condition][best]['work_with_full_oracle']['mean']/metrics[condition]['recursive']['work_with_full_oracle']['mean']) | |