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).
97 lines
4.2 KiB
Python
97 lines
4.2 KiB
Python
#!/usr/bin/env python3
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"""slim 电池端到端验收（纯离线）：
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1. 四次 glm_53 运行信号分解对比（全量 verify / rerun / conc2 / slim）——定位 score 差来源
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2. slim 样本 top-1 归因 vs 9 参考（主验收判据）
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3. 错误普查 + 与历次样本的 meanJSD
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"""
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import json
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import sys
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from collections import Counter
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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, distributions_by_cell, # noqa: E402
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jsd_bits, 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"),
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("deepseek_v4_flash_0731", "deepseek_v4_flash_0731_fusion_reference.json"),
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("deepseek_v4_pro", "deepseek_v4_pro_fusion_reference.json"),
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("glm_51", "glm_51_fusion_reference.json"),
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("glm_52", "glm52_vectron_fusion_reference.json"),
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("glm_53", "glm53_fusion_reference.json"),
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("kimi_k2_6", "kimi_k2_6_fusion_reference.json"),
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("kimi_k2_7code", "kimi_k2_7code_fusion_reference.json"),
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("kimi_k3", "kimi_k3_fusion_reference.json"),
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]
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NAMES = [m[0] for m in MODELS]
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# ---------- 1. 四次运行信号分解 ----------
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RUNS = [("全量verify", f"{BFD}/glm_53/verify.json"),
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("全量rerun", f"{BFD}/glm_53/rerun/verify_rerun.json"),
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("全量conc2", f"{BFD}/glm_53/conc2/verify_conc2.json"),
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("slim并发2", f"{BFD}/glm_53/slim/verify_slim.json")]
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print("【信号分解】s_dist 由 ratio=meanJSD/max(split_half,0.02) 阶梯量化")
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print(f"{'运行':12s} {'score':>7s} {'verdict':>15s} {'s_dist':>7s} {'meanJSD':>8s} "
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f"{'splitH':>7s} {'ratio':>6s} {'cells':>5s} {'s_idn':>6s} {'s_meta':>6s}")
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for name, path in RUNS:
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r = json.load(open(path))
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dist = r["signals"]["dist"]
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idn = r["signals"].get("identity", {})
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met = r["signals"].get("meta", {})
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print(f"{name:12s} {r['score']:7.4f} {r['verdict']:>15s} "
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f"{dist.get('s_dist', 0):7.3f} {dist.get('mean_jsd') or 0:8.4f} "
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f"{(dist.get('split_half') or 0):7.4f} {dist.get('relative_ratio') or 0:6.2f} "
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f"{dist.get('comparable_cells'):5d} {idn.get('s_idn', 0):6.3f} {met.get('s_meta', 0):6.3f}")
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print()
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# ---------- 2. slim 样本验收 ----------
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recs = [json.loads(l) for l in open(f"{BFD}/glm_53/slim/raw_answers.jsonl")]
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errs = [r for r in recs if r.get("error")]
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ec = Counter()
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for r in errs:
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e = str(r["error"])
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for code in ("400", "402", "429", "500", "502", "503", "504", "timeout"):
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if code in e.lower():
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ec[code] += 1
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break
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else:
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ec[e[:30]] += 1
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print(f"slim 记录 {len(recs)}（期望434），错误 {len(errs)}: {dict(ec) or '无'}")
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d_new = distributions_by_cell(build_d_normalized(recs))
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refs = {n: load_reference(f"{R}/{rf}")["cells"] for n, rf in MODELS}
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def score(dist, ref):
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js = []
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for c in set(dist) & set(ref):
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a, b = dist[c], ref[c]
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if sum(a.values()) >= 10 and sum(b.values()) >= 10:
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js.append(jsd_bits(a, b))
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return (sum(js) / len(js) if js else None), len(js)
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vals = {n: score(d_new, refs[n])[0] for n in NAMES}
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best = min(vals, key=vals.get)
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sib = min(v for n, v in vals.items() if n.startswith("glm") and n != "glm_53")
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print(f"top-1 归因: {best} {'✓ 正确' if best == 'glm_53' else '✗ 混淆!'} "
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f"own={vals['glm_53']:.4f} 同族间距={sib - vals['glm_53']:+.4f} "
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f"可比cell={score(d_new, refs['glm_53'])[1]}(期望16)")
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prior = {}
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for tag, p in [("样本1(全量verify)", f"{BFD}/glm_53/raw_answers.jsonl"),
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("样本2(rerun)", f"{BFD}/glm_53/rerun/raw_answers.jsonl"),
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("样本3(conc2)", f"{BFD}/glm_53/conc2/raw_answers.jsonl")]:
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prior[tag] = distributions_by_cell(build_d_normalized(
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[json.loads(l) for l in open(p)]))
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for tag, d in prior.items():
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js = [jsd_bits(d_new[c], d[c]) for c in set(d_new) & set(d)
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if sum(d_new[c].values()) >= 10 and sum(d[c].values()) >= 10]
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print(f" vs {tag}: meanJSD={sum(js) / len(js):.4f} ({len(js)} cell)")
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tot_v = sum(1 for s in build_d_normalized(recs) if s["cat"] == "valid")
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print(f"D 层 valid: {tot_v}/400")
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