Thibaut Forest
Claude Fable 5
ADOPT campaign-2 frontier ft_vmin7r_wnr20: BATTERYSWAP_FT2 default ON
3f9bf12 | """General composer on cv.run_bgkf: registers vm7 variant + custom aux kinds | |
| in-process, then runs any (kind, variant, blend) combo. No repo edits. | |
| Usage: compose.py <label> kind=<k> [variant=base] [blend=<k2>:<w>]""" | |
| import sys, time | |
| sys.path.insert(0, '/Users/THIF/repos/BatterySwapAI2026-Example') | |
| import os | |
| os.chdir('/Users/THIF/repos/BatterySwapAI2026-Example') | |
| import numpy as np, pandas as pd | |
| from batteryswap_example import models_cv as mc | |
| from batteryswap_example import cv | |
| label = sys.argv[1] | |
| kind, variant, blend = 'logbag', 'base', None | |
| for a in sys.argv[2:]: | |
| k, v = a.split('=', 1) | |
| if k == 'kind': | |
| kind = v | |
| elif k == 'variant': | |
| variant = v | |
| elif k == 'blend': | |
| b, w = v.split(':') | |
| blend = (b, float(w)) | |
| cv.VARIANTS['vm7'] = lambda t: cv.BASE_FEATURES + ['vmin7_rank'] | |
| mc.MODELS['aux_ho'] = lambda: mc.LogBagFeats(['hot_90', 'oob_90']) | |
| mc.MODELS['aux_hot'] = lambda: mc.LogBagFeats(['hot_90']) | |
| mc.MODELS['aux_oob'] = lambda: mc.LogBagFeats(['oob_90']) | |
| mc.MODELS['aux_ho30'] = lambda: mc.LogBagFeats(['hot_90', 'oob_90'], | |
| n_estimators=30) | |
| mc.MODELS['aux_ho50'] = lambda: mc.LogBagFeats(['hot_90', 'oob_90'], | |
| n_estimators=50) | |
| t0 = time.time() | |
| df = cv.run_bgkf(kind=kind, variant=variant, blend=blend) | |
| df['cand'] = label | |
| df.to_csv(f'/tmp/cv_bgkfcand_{label}.csv', index=False) | |
| base = pd.read_csv('/tmp/cv_bgkf.csv').groupby('origin')[ | |
| ['total_cost', 'n_swaps']].sum() | |
| d = df.groupby('origin')[['total_cost', 'n_swaps']].sum() | |
| delta = (d['total_cost'] - base['total_cost']).dropna() | |
| se = delta.std() / np.sqrt(len(delta)) | |
| print(f'{label}: SCORE {d["total_cost"].mean():.1f} ' | |
| f'delta {delta.mean():+.1f} SE {se:.1f} ' | |
| f'{int((delta<0).sum())}W/{int((delta>0).sum())}L ' | |
| f'swaps {d["n_swaps"].mean():.1f} [{time.time()-t0:.0f}s]') | |