09b2add673
New evalharness/fingerprint/ package (from evalstone fp_fusion v1.1, 2026-09-07 pruning final): probe battery -> concurrent collection -> five scoring views (verify/attribution/variant/adversarial/robustness), bundled family aliases + 27 reference fingerprints (12 fp_fusion schema). - CLI: 'evalharness fingerprint run ...' (REMAINDER passthrough, single source of arg definitions) + 'fingerprint list' for bundled references - imports rewritten package-relative; direct 'python3 run_fp_fusion.py' execution kept working via package bootstrap - offline analysis/collection scripts made path-independent (previously pinned to a /opt/evalscope path absent on this host) - shell scripts: hardcoded API key -> FP_API_KEY/OPENAI_API_KEY env vars - --reference accepts short names resolved against bundled references/ - pyproject: +httpx dependency, package-data references/*.json - tests/test_fingerprint.py: 10 offline tests (battery definitions, assembly counts, normalization, signals, verdict ladder, CLI wiring) - README: fingerprint section + architecture entry Verified on H20-1: tests 10/10, installed CLI OK, full-protocol run vs vectron GLM-5.3 reproduces baseline (score 0.9451, s_idn 0.846).
101 lines
3.9 KiB
Python
101 lines
3.9 KiB
Python
#!/usr/bin/env python3
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"""9×9 交叉混淆矩阵：每个模型实测 D 层分布 vs 全部 9 个参考（纯离线）。
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混淆判定：某模型的全部 JSD 里，最低者（top-1）若不是自己的参考 → 记一次混淆。
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附带：glm_53 复跑样本作为第二独立样本验证比对稳定性。
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"""
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import json
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import sys
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from pathlib import Path # noqa: E402
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sys.path.insert(0, str(Path(__file__).resolve().parents[2])) # 仓库根/site-packages, 使 evalharness 包可导入
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from evalharness.fingerprint.engine import (build_d_normalized, compare_cells, # noqa: E402
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distributions_by_cell, load_reference)
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BFD = "/tmp/bfd"
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R = str(Path(__file__).resolve().parent / 'references')
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MODELS = [
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("deepseek_v4_flash", "deepseek_v4_flash_fusion_reference.json", "DS"),
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("deepseek_v4_flash_0731", "deepseek_v4_flash_0731_fusion_reference.json", "DS"),
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("deepseek_v4_pro", "deepseek_v4_pro_fusion_reference.json", "DS"),
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("glm_51", "glm_51_fusion_reference.json", "GLM"),
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("glm_52", "glm52_vectron_fusion_reference.json", "GLM"),
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("glm_53", "glm53_fusion_reference.json", "GLM"),
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("kimi_k2_6", "kimi_k2_6_fusion_reference.json", "Kimi"),
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("kimi_k2_7code", "kimi_k2_7code_fusion_reference.json", "Kimi"),
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("kimi_k3", "kimi_k3_fusion_reference.json", "Kimi"),
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]
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FAMILY = {n: f for n, _, f in MODELS}
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refs = {}
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for n, rf, _ in MODELS:
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refs[n] = load_reference(f"{R}/{rf}")["cells"]
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def dist_of(path):
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records = [json.loads(l) for l in open(path)]
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d = build_d_normalized(records)
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return distributions_by_cell(d)
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live = {n: dist_of(f"{BFD}/{n}/raw_answers.jsonl") for n, _, _ in MODELS}
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extra = {}
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try:
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extra["glm_53#rerun"] = dist_of(f"{BFD}/glm_53/rerun/raw_answers.jsonl")
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except FileNotFoundError:
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pass
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names = [n for n, _, _ in MODELS]
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results = {}
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for lname, dist in {**live, **extra}.items():
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results[lname] = {}
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for rname, ref_cells in refs.items():
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entries, mean_jsd = compare_cells(dist, ref_cells)
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results[lname][rname] = (mean_jsd, len(entries))
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print("JSD 矩阵（行=实测模型，列=参考，越低越像）:")
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print("live\\ref".ljust(22) + "".join(n[:13].rjust(14) for n in names))
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for lname in results:
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print(lname[:21].ljust(22) +
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"".join(f"{results[lname][n][0]:.3f}".rjust(14)
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if results[lname][n][0] is not None else "—".rjust(14)
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for n in names))
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print()
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print("=" * 100)
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correct, total, confusions = 0, 0, []
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print(f"{'模型':22s} {'own':>6s} {'top1 匹配':22s} {'判定':6s} {'最佳同族':22s} {'同族JSD':>8s} {'间距':>8s}")
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for lname in live:
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valid = {r: v[0] for r, v in results[lname].items() if v[0] is not None}
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if not valid:
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continue
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best = min(valid, key=valid.get)
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own = valid[lname]
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total += 1
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ok = best == lname
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correct += ok
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if not ok:
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confusions.append((lname, best, round(own, 3), round(valid[best], 3)))
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sibs = [r for r in valid if FAMILY[r] == FAMILY[lname] and r != lname]
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if sibs:
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bs = min(sibs, key=valid.get)
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print(f"{lname:22s} {own:6.3f} {best:22s} {'✓' if ok else '✗混淆':6s} "
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f"{bs:22s} {valid[bs]:8.3f} {valid[bs] - own:+8.3f}")
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else:
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print(f"{lname:22s} {own:6.3f} {best:22s} {'✓' if ok else '✗混淆':6s} {'(无同族)':22s}")
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print()
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print(f"【top-1 正确率】{correct}/{total} = {correct / total * 100:.0f}%")
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print(f"【混淆数】{len(confusions)}")
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for c in confusions:
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print(f" 混淆: {c[0]} 被认成 {c[1]} (own={c[2]}, conf={c[3]})")
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if "glm_53#rerun" in results:
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valid = {r: v[0] for r, v in results["glm_53#rerun"].items() if v[0] is not None}
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best = min(valid, key=valid.get)
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print(f"【稳定性】glm_53 第二独立样本 top1={best} "
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f"{'✓ 与首跑一致' if best == 'glm_53' else '✗ 不一致'} "
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f"(own={valid['glm_53']:.3f}, 次优={sorted(valid.values())[1]:.3f})")
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