| r"""add_ystats.py — compute per-pollutant log-target mean/std (TRAIN fold) from existing |
| tensors and add to norm.npz. Avoids re-running full assemble. Run once before retraining. |
| Run: python data\models\add_ystats.py --tensors data\tensors |
| """ |
| import argparse, pathlib, numpy as np |
| ap=argparse.ArgumentParser(); ap.add_argument("--tensors",default="data/tensors"); a=ap.parse_args() |
| t=pathlib.Path(a.tensors) |
| Y=np.load(t/"targets.npy").astype(np.float32) |
| M=np.load(t/"target_mask.npy") |
| split=np.load(t/"node_split.npy") |
| tr=(split==0) |
| yl=np.log(np.clip(Y,0,None)+1e-3) |
| ylm=np.zeros(7,np.float32); yls=np.ones(7,np.float32) |
| POLL=["pm2_5","pm10","no2","so2","co","o3","nh3"] |
| for j in range(7): |
| v=yl[:,tr,j][M[:,tr,j]>0] |
| if len(v): ylm[j]=v.mean(); yls[j]=v.std()+1e-6 |
| print(f" {POLL[j]:6}: log-mean={ylm[j]:+.2f} log-std={yls[j]:.2f}") |
| nz=dict(np.load(t/"norm.npz")) |
| nz["y_logmean"]=ylm; nz["y_logstd"]=yls |
| np.savez(t/"norm.npz",**nz) |
| print(f"-> updated {t/'norm.npz'} with y_logmean/y_logstd") |
