| from pathlib import Path |
| import csv |
| import matplotlib |
| matplotlib.use('Agg') |
| import matplotlib.pyplot as plt |
| from matplotlib.ticker import FuncFormatter |
|
|
| source=Path('/home/eai/code/training_recipe/2026-09-21_b1k_task00_pi05_codegen_success_sft_h20/config/validation_history.csv') |
| out=Path(__file__).parent |
| rows=list(csv.DictReader(source.open())) |
| steps=[int(r['step']) for r in rows] |
| val=[float(r['validation_loss']) for r in rows] |
| train=[float(r['train_eval_loss']) for r in rows] |
| best=min(range(len(val)),key=val.__getitem__) |
| plt.rcParams.update({'font.family':'DejaVu Sans','font.size':10,'axes.spines.top':False,'axes.spines.right':False,'axes.labelcolor':'#334155','text.color':'#0f172a','xtick.color':'#475569','ytick.color':'#475569','axes.edgecolor':'#cbd5e1'}) |
| fig,axs=plt.subplots(1,2,figsize=(13.4,5.6),gridspec_kw={'width_ratios':[1,1.2]}) |
| fig.subplots_adjust(left=.075,right=.976,top=.77,bottom=.21,wspace=.25) |
| fig.suptitle('Pi0.5 路 turning_on_radio',x=.075,y=.975,ha='left',fontsize=20,fontweight='bold') |
| fig.text(.075,.9,'Best checkpoint: step 6,000 | Training stopped: step 10,000',fontsize=12,color='#475569') |
| for ax in axs: |
| ax.plot(steps,val,color='#2563eb',lw=2.5,marker='o',ms=3.6,label='Validation 路 26 held-out instances') |
| ax.plot(steps,train,color='#d97706',lw=2.3,marker='o',ms=3.3,label='Training subset 路 26 instances') |
| ax.axvline(steps[best],color='#64748b',lw=1.15,ls='--') |
| ax.set_xlabel('Training step',labelpad=9) |
| ax.set_ylabel('Mean flow loss 路 23 action dimensions',labelpad=9) |
| ax.xaxis.set_major_formatter(FuncFormatter(lambda x,p:f'{x:,.0f}')) |
| ax.grid(axis='y',color='#e2e8f0',lw=.8) |
| ax.set_axisbelow(True) |
| axs[0].set_title('Full run 路 logarithmic loss scale',loc='left',fontsize=12,pad=14) |
| axs[0].set_yscale('log');axs[0].set_xlim(-150,10300);axs[0].set_ylim(.0035,.8) |
| axs[0].set_xticks([0,2000,4000,6000,8000,10000]) |
| axs[1].set_title('Detail after step 1,000 路 linear loss scale',loc='left',fontsize=12,pad=14) |
| axs[1].set_xlim(1000,10300);axs[1].set_ylim(.0035,.0155) |
| axs[1].set_xticks([1000,3000,6000,8000,10000]) |
| axs[1].axvspan(6000,10000,color='#fee2e2',alpha=.32,zorder=-1) |
| axs[1].scatter([6000],[val[best]],s=80,facecolor='white',edgecolor='#2563eb',lw=2,zorder=5) |
| axs[1].annotate(f'Best: {val[best]:.6f}',xy=(6000,val[best]),xytext=(4100,.01125),fontsize=10,color='#1d4ed8',arrowprops={'arrowstyle':'-','color':'#2563eb','lw':1.1}) |
| axs[1].annotate(f'Final: {val[-1]:.6f}\n+{(val[-1]/val[best]-1)*100:.1f}% vs. best',xy=(10000,val[-1]),xytext=(7850,.0124),fontsize=10,color='#1d4ed8',arrowprops={'arrowstyle':'-','color':'#2563eb','lw':1.1}) |
| handles,labels=axs[0].get_legend_handles_labels() |
| fig.legend(handles,labels,loc='lower left',bbox_to_anchor=(.07,.075),frameon=False,ncol=2,fontsize=10) |
| fig.text(.075,.035,'Comparable evaluation losses: no augmentation, fixed noise, episode averages. Points every 500 steps; no smoothing.',fontsize=9,color='#64748b') |
| fig.savefig(out/'loss_curve.png',dpi=180,facecolor='white') |
| fig.savefig(out/'loss_curve.svg',facecolor='white') |
| (out/'validation_history.csv').write_bytes(source.read_bytes()) |
| print(out/'loss_curve.png') |
|
|
