import json,sys,ast,collections
def P(v):
    if isinstance(v,dict): return v
    try: return ast.literal_eval(v)
    except: return {}
rows=[json.loads(l) for l in open(sys.argv[1])]
G=collections.defaultdict(list); FO=collections.defaultdict(list); R25=collections.defaultdict(list)
def gauss(sc):
    k=(sc.get("kernels") or {}).get("gaussian")
    if isinstance(k,dict): return k.get("metric_score", k.get("mean_metric_score"))
    return sc.get("metric_score")
for r in rows:
    sc=P(r["score"]); m=P(r["meta"]); sp=m.get("split","?")
    g=gauss(sc)
    if g is not None: G[sp].append(g); G["overall"].append(g)
    fo=sc.get("format_ok", sc.get("format_reward"))
    if fo is not None:
        ok=(fo>=0.6) if isinstance(fo,(int,float)) else bool(fo)
        FO[sp].append(ok); FO["overall"].append(ok)
    tol=sc.get("tolerance") or {}
    r25=tol.get("rel25", tol.get("rel25_rate"))
    if r25 is not None: R25[sp].append(bool(r25)); R25["overall"].append(bool(r25))
print("  split      n    gaussian  fmt_ok  rel25")
for sp in ["overall","univar","bivar","multivar"]:
    b=G.get(sp,[])
    if not b: continue
    f=FO.get(sp,[]); rr=R25.get(sp,[])
    fa=sum(f)/len(f) if f else float('nan'); ra=sum(rr)/len(rr) if rr else float('nan')
    print(f"  {sp:9s} {len(b):4d}  {sum(b)/len(b):.4f}   {fa:.3f}  {ra:.4f}")