| from __future__ import annotations |
| import argparse,csv,json |
| from pathlib import Path |
| from statistics import mean,stdev |
| import torch |
| from spectral_world_models.models import build_model |
| from spectral_world_models.train import train_one_model |
| from spectral_world_models.v9_causal_generalization import generate_family_npz |
| from spectral_world_models.v11_causal_calibration import REGIMES,evaluate_causal_calibration |
| DEFAULT_MODELS=['neural_operator','swm_structured_cf_v9','swm_structured_cf_v11','no_spectral_transition'] |
| def stats(x): return mean(x),stdev(x) if len(x)>1 else 0. |
| def main(): |
| ap=argparse.ArgumentParser(description='V11 causal-gain calibration benchmark') |
| ap.add_argument('--epochs',type=int,default=3); ap.add_argument('--batch-size',type=int,default=64); ap.add_argument('--seeds',type=int,nargs='+',default=list(range(10))); ap.add_argument('--cf-horizon',type=int,default=30); ap.add_argument('--cf-pairs',type=int,default=64); ap.add_argument('--device',default='auto'); ap.add_argument('--models',nargs='+',default=DEFAULT_MODELS); a=ap.parse_args() |
| root=Path(__file__).resolve().parents[1]; out=root/'results/v11_causal_calibration'; out.mkdir(parents=True,exist_ok=True); data=root/'data/v9_train_family.npz' |
| if not data.exists(): generate_family_npz(data) |
| device=('cuda' if torch.cuda.is_available() else 'cpu') if a.device=='auto' else a.device; dev=torch.device(device); raw=[] |
| for seed in a.seeds: |
| for m in a.models: |
| print(f'V11 seed={seed} model={m}'); sd=out/f'seed_{seed}' |
| r=train_one_model(m,data,sd,epochs=a.epochs,batch_size=a.batch_size,seed=seed,device=device,cf_train_horizon=12,cf_pairs=a.cf_pairs,cf_every=4,lambda_cf_dir=.03,lambda_cf_mag=.01,lambda_cf_branch=.08) |
| ck=torch.load(sd/f'{m}.pt',map_location=dev); model=build_model(m).to(dev); model.load_state_dict(ck['model']); row={'seed':seed,'model':m,'params':r['params']} |
| for rn,cfg in REGIMES.items(): row.update({f'{rn}_{k}':v for k,v in evaluate_causal_calibration(model,dev,cfg,n=a.cf_pairs,horizon=a.cf_horizon,seed=11101).items()}) |
| raw.append(row) |
| fields=list(raw[0]); |
| with (out/'metrics_by_seed.csv').open('w',newline='') as f: w=csv.DictWriter(f,fieldnames=fields); w.writeheader(); w.writerows(raw) |
| summary=[] |
| for m in a.models: |
| rs=[r for r in raw if r['model']==m]; row={'model':m,'params':rs[0]['params'],'n_seeds':len(rs)} |
| for k in fields: |
| if k in ('seed','model','params'): continue |
| mu,sd=stats([float(r[k]) for r in rs]); row[k+'_mean']=mu; row[k+'_std']=sd |
| summary.append(row) |
| sf=list(summary[0]); |
| with (out/'metrics_summary.csv').open('w',newline='') as f: w=csv.DictWriter(f,fieldnames=sf); w.writeheader(); w.writerows(summary) |
| with (out/'run_config.json').open('w') as f: json.dump(vars(a)|{'device_resolved':device,'architecture_base':'v9 structured transport','objective':'scale-aware trajectory causal calibration'},f,indent=2) |
| print('wrote',out/'metrics_summary.csv') |
| if __name__=='__main__': main() |
|
|
