| from edgeeda.viz import export_trials |
| import pandas as pd, matplotlib |
| matplotlib.use('Agg') |
| import matplotlib.pyplot as plt, os, glob, json |
|
|
| out='runs/plots_quick' |
| os.makedirs(out, exist_ok=True) |
|
|
| |
| df = export_trials('runs/experiment.sqlite') |
| print('rows:', len(df)) |
| print('columns:', list(df.columns)) |
|
|
| |
| runtimes = pd.to_numeric(df['runtime_sec'], errors='coerce').dropna() |
| if not runtimes.empty: |
| plt.figure(); runtimes.hist(bins=10) |
| plt.xlabel('runtime_sec'); plt.tight_layout(); plt.savefig(os.path.join(out,'runtime_hist.png'), dpi=200); plt.close() |
| print('wrote runtime_hist.png') |
| else: |
| print('no runtime data to plot') |
|
|
| |
| plt.figure(); df['return_code'].value_counts().plot(kind='bar') |
| plt.xlabel('return_code'); plt.tight_layout(); plt.savefig(os.path.join(out,'return_code_counts.png'), dpi=200); plt.close() |
| print('wrote return_code_counts.png') |
|
|
| |
| has_meta = df['metadata_path'].fillna('').apply(lambda x: bool(str(x).strip())) |
| plt.figure(); has_meta.value_counts().plot(kind='bar'); plt.xticks([0,1],['no metadata','has metadata']); plt.tight_layout(); plt.savefig(os.path.join(out,'metadata_counts.png'), dpi=200); plt.close() |
| print('wrote metadata_counts.png') |
|
|
| |
| if 'reward' in df.columns: |
| r = pd.to_numeric(df['reward'], errors='coerce').dropna() |
| if not r.empty: |
| df2 = df.copy() |
| df2['reward'] = pd.to_numeric(df2['reward'], errors='coerce') |
| df2 = df2.dropna(subset=['reward']).sort_values('id') |
| best = df2['reward'].cummax() |
| plt.figure(); plt.plot(df2['id'].values, best.values) |
| plt.xlabel('trial id'); plt.ylabel('best reward so far'); plt.tight_layout(); plt.savefig(os.path.join(out,'learning_curve.png'), dpi=200); plt.close() |
| print('wrote learning_curve.png') |
| else: |
| print('no rewards to plot') |
| else: |
| print('reward column missing') |
|
|
| |
| areas=[]; wnss=[] |
| for _, r in df.iterrows(): |
| mj = r.get('metrics') or r.get('metrics_json') or r.get('metrics_json') |
| if not mj: |
| continue |
| if isinstance(mj, str): |
| try: |
| m = json.loads(mj) |
| except Exception: |
| continue |
| else: |
| m = mj |
| a = m.get('design__die__area') or m.get('finish__design__die__area') |
| w = m.get('timing__setup__wns') or m.get('finish__timing__setup__wns') |
| if a is None or w is None: |
| continue |
| try: |
| areas.append(float(a)); wnss.append(float(w)) |
| except Exception: |
| pass |
| if areas: |
| plt.figure(); plt.scatter(areas, wnss); plt.xlabel('die area'); plt.ylabel('WNS'); plt.tight_layout(); plt.savefig(os.path.join(out,'area_vs_wns.png'), dpi=200); plt.close() |
| print('wrote area_vs_wns.png') |
| else: |
| print('no area/wns metrics to plot') |
|
|
| print('files:', glob.glob(out+'/*')) |
|
|
