| r"""diag.py — why co R² is -3.5 and pm2_5 flat. Checks prediction vs target scale per pollutant.""" |
| import os,sys,json,pathlib,argparse |
| import numpy as np, torch |
| sys.path.insert(0,str(pathlib.Path(__file__).resolve().parent)) |
| from model import AtmosIQNet |
| from dataset_v2 import AtmosData |
| POLL=["pm2_5","pm10","no2","so2","co","o3","nh3"] |
| ap=argparse.ArgumentParser(); ap.add_argument("--tensors",default="data/tensors"); ap.add_argument("--ckpt",default="data/atmosiq_best.pt") |
| a=ap.parse_args(); dev="cuda" if torch.cuda.is_available() else "cpu" |
| meta=json.load(open(pathlib.Path(a.tensors)/"meta.json")) |
| data=AtmosData(a.tensors,window=7,device=dev) |
| cfg={"calendar_num_dim":meta["dimsD_num"],"n_recon_channels":9+meta["dimsB"]+meta["dimsC_static"]+meta["dimsC_yearly"], |
| "categorical":{"land_use_class":4,"grap_stage":3,"n_landuse":meta["n_landuse"],"n_grap":meta["n_grap"]}} |
| net=AtmosIQNet(cfg).to(dev); net.load_state_dict(torch.load(a.ckpt,map_location=dev)); net.eval() |
| alln=torch.arange(data.N,device=data.store); ei,eh,nh=data.ei.to(dev),data.e_hat.to(dev),data.n_hat.to(dev) |
| te=(data.split==2).float().unsqueeze(-1).to(dev) |
| |
| print("=== target ranges (test fold, raw concentration) ===") |
| for j,p in enumerate(POLL): |
| m=(data.M[:,:,j]>0)&(data.split==2).unsqueeze(0) |
| vals=data.Y[:,:,j][m].float() |
| if len(vals): print(f" {p:6}: min={vals.min():.2f} median={vals.median():.2f} max={vals.max():.2f} mean={vals.mean():.2f}") |
| |
| print("\n=== predicted (expm1 mu) vs truth, horizon0, sample ===") |
| with torch.no_grad(): |
| for t in data.target_days(2)[:3]: |
| b=data.window(t,alln,ei,eh,nh) |
| out=net(b["groups_t"],b["ei"],b["e_hat"],b["n_hat"],b["wu"],b["wv"],b["blh"],b["comp"]) |
| mu=out["mu"][:,0,:]; pred=(torch.exp(mu.clamp(max=12))-1e-3).clamp_min(0); y=b["y"][:,0,:]; m=(b["m"][:,0,:]*te) |
| for j,p in enumerate(POLL): |
| mm=m[:,j]>0 |
| if mm.sum()>2: |
| print(f" day{t} {p:6}: pred[med={pred[mm,j].median():.2f} max={pred[mm,j].max():.1f}] truth[med={y[mm,j].median():.2f}] mu[med={mu[mm,j].median():.2f}]") |
| break |
