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scripts/evaluate.py

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+r"""
+evaluate.py — M2 Go/No-Go. Loads atmosiq_best.pt, predicts on the TEST fold (spatial
+held-out nodes, full-graph), reports per-pollutant spatial-held-out R²/RMSE/MAE at
+horizon 0, and compares to the M1 XGBoost floor (refs.json). Blueprint §10 primary metric.
+Run: python data\models\evaluate.py --tensors data\tensors --ckpt data\atmosiq_best.pt
+"""
+from __future__ import annotations
+import os, sys, json, argparse, pathlib
+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"]
+
+def main():
+    ap=argparse.ArgumentParser()
+    ap.add_argument("--tensors",default="data/tensors"); ap.add_argument("--ckpt",default="data/atmosiq_best.pt")
+    ap.add_argument("--window",type=int,default=7); ap.add_argument("--refs",default="data/models/refs.json")
+    ap.add_argument("--test_days",type=int,default=0,help="0=all test days")
+    a=ap.parse_args()
+    dev="cuda" if torch.cuda.is_available() else "cpu"; print("device:",dev)
+    meta=json.load(open(pathlib.Path(a.tensors)/"meta.json"))
+    data=AtmosData(a.tensors, window=a.window, 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()
+    nz=np.load(pathlib.Path(a.tensors)/"norm.npz")
+    ylm=torch.tensor(nz["y_logmean"]).float().to(dev) if "y_logmean" in nz else None
+    yls=torch.tensor(nz["y_logstd"]).float().to(dev) if "y_logstd" in nz else None
+    print(f"loaded {a.ckpt} ({sum(p.numel() for p in net.parameters()):,} params)")
+
+    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_days=data.target_days(2)
+    if a.test_days: te_days=te_days[:a.test_days]
+    te_mask=(data.split==2).float().unsqueeze(-1).to(dev)
+    print(f"test days: {len(te_days)} | held-out test nodes: {int((data.split==2).sum())}")
+
+    # collect horizon-0 predictions (median = mu in log space -> expm1) vs truth on test nodes
+    Ys={p:[] for p in POLL}; Ps={p:[] for p in POLL}
+    with torch.no_grad():
+        for t in te_days:
+            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,:]                       # horizon 0 [N,7] (standardized log space)
+            if ylm is not None: mu=mu*yls.view(1,-1)+ylm.view(1,-1)   # de-standardize
+            pred=(torch.exp(mu.clamp(max=12))-1e-3).clamp_min(0)   # log(y+eps) inverse; clamp>=0 (fixes co negatives)
+            y=b["y"][:,0,:]; m=(b["m"][:,0,:]*te_mask)
+            for j,p in enumerate(POLL):
+                mm=m[:,j]>0
+                if mm.any():
+                    Ys[p].append(y[mm,j].cpu()); Ps[p].append(pred[mm,j].cpu())
+
+    refs=json.load(open(a.refs)) if os.path.exists(a.refs) else {}
+    print("\n===== M2 — SPATIAL-HELD-OUT R² (horizon 0, blueprint §10) =====")
+    print(f"{'pollutant':8} {'n':>6} {'R²':>8} {'RMSE':>8} {'MAE':>8}   vs XGBoost")
+    r2s=[]
+    for p in POLL:
+        if not Ys[p]: print(f"{p:8} {'--':>6}  no test labels"); continue
+        y=torch.cat(Ys[p]).numpy(); pr=torch.cat(Ps[p]).numpy()
+        pr=np.nan_to_num(pr,nan=np.nanmean(y))
+        ss_res=((y-pr)**2).sum(); ss_tot=((y-y.mean())**2).sum()+1e-9
+        r2=1-ss_res/ss_tot; rmse=np.sqrt(((y-pr)**2).mean()); mae=np.abs(y-pr).mean()
+        xg=refs.get(p,{}).get("xgboost")
+        cmp=f"xgb {xg:+.2f}  {'BEAT' if (xg is not None and r2>xg) else 'below' if xg is not None else ''}"
+        print(f"{p:8} {len(y):>6} {r2:>+8.3f} {rmse:>8.2f} {mae:>8.2f}   {cmp}")
+        if p not in ("nh3",): r2s.append(r2)     # nh3 excluded from Go/No-Go (sparse-aux)
+    print(f"\nmean primary R² (excl nh3): {np.mean(r2s):+.3f}")
+    print("Go/No-Go: GNN should beat XGBoost floor on primary pollutants (esp pm2_5) on SPATIAL held-out.")
+
+if __name__=="__main__": main()