"""跨切分消融的评测结果分析器(读 11_eval 产出的 data/eval/*.jsonl)。 干两件既有单模型 stats.json 给不了的事: 1. **横向对比表**:把 by-query 2:1 / 1:2 + by-user 2:1 / 1:2 各自的 SFT 与 DAPO checkpoint 的 judge_acc / EM / F1 / strategy / hallucination / cost 并排,一眼看 「翻转比例」「按 user 解耦」「SFT→DAPO 增量」三个对比。 2. **DAPO seen / unseen 拆分(by-user 实验的核心问题)**:dataset_eval100 的 308 条 query 每一条要么属于该切分的 RL 训练集(DAPO 见过该 query)、要么属于 SFT 训练集 (DAPO 没在这条上更新过)。按 user 切分时 SFT/RL 用户 disjoint → 「unseen」= DAPO 从没见过的用户。拆开两组的 judge_acc,回答「DAPO 学到的策略能否迁移到没训过的用户」。 (by-query 切分用户重叠,unseen 只是 query 级 held-out,仍作参考但解释力弱。) 用法: python scripts/train/analyze_eval_splits.py # 全部 + CSV python scripts/train/analyze_eval_splits.py --eval-dir data/eval """ import argparse import csv import json import os import sys sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))) PROJECT_ROOT = os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))) from src.evaluation.eval_metrics import aggregate from src.utils import load_json, setup_logger logger = setup_logger(__name__) def _r(p): return p if os.path.isabs(p) else os.path.join(PROJECT_ROOT, p) # 每个被评 checkpoint: (展示名, eval jsonl, 该切分的 split 目录). split 目录用来判 seen/unseen # 和 by-user(by-user 切分的 rl/sft 是 disjoint 用户). 顺序即对比表行序。 SPLIT_DIR = { "1to2": "data/processed/metamem_5k/splits_1to2", # by-query 1:2 "bu_2to1": "data/processed/metamem_5k/splits_bu_2to1", # by-user 2:1 "bu_1to2": "data/processed/metamem_5k/splits_bu_1to2", # by-user 1:2 "2to1": "data/processed/metamem_5k/splits", # by-query 2:1 (原始,SFT 已评) } # by-user 切分用户 disjoint → unseen 解释力强; by-query 用户重叠 → 仅 query 级 held-out BY_USER = {"bu_2to1", "bu_1to2"} # 🔴 dataset_eval100 的 query 不带 ms_label → 11_eval 写出的记录 oracle_ms 全 None → # aggregate 的 strategy_accuracy / over·under_retrieval / hallucination / by_ms 全部失效 # (核心 MetaMem 指标). ms_label 在 data/labeled/ms_labels///_label.json # 已存在 → 在分析期按 query_id 回填 oracle_ms,把这些指标点亮(不动评测产出本身)。 MS_LABEL_DIR = "data/labeled/ms_labels/Qwen2.5-7B-Instruct" def backfill_oracle_ms(recs, ms_dir): """就地回填 oracle_ms(若记录里已是 None). 返回命中数.""" hit = 0 for r in recs: if r.get("oracle_ms"): hit += 1 continue p = os.path.join(ms_dir, r.get("user_id", ""), f'{r.get("query_id")}_label.json') if os.path.exists(p): try: r["oracle_ms"] = load_json(p)["ms_label"] hit += 1 except Exception: pass return hit ROWS = [ # (stage, split_tag, eval_jsonl_basename) ("sft", "2to1", "eval_metamem_trainset_qwen25_sft_lora"), # 既有(已跑) ("sft", "2to1", "eval_metamem_trainset_qwen25_sft_full"), # 既有(已跑) ("sft", "1to2", "eval_trainset_qwen25_sft_lora_1to2"), ("sft", "1to2", "eval_trainset_qwen25_sft_full_1to2"), ("sft", "bu_2to1", "eval_trainset_qwen25_sft_lora_bu_2to1"), ("sft", "bu_2to1", "eval_trainset_qwen25_sft_full_bu_2to1"), ("sft", "bu_1to2", "eval_trainset_qwen25_sft_lora_bu_1to2"), ("sft", "bu_1to2", "eval_trainset_qwen25_sft_full_bu_1to2"), ("dapo", "1to2", "eval_trainset_qwen25_dapo_lora_1to2"), ("dapo", "1to2", "eval_trainset_qwen25_dapo_full_1to2"), ("dapo", "bu_2to1", "eval_trainset_qwen25_dapo_lora_bu_2to1"), ("dapo", "bu_2to1", "eval_trainset_qwen25_dapo_full_bu_2to1"), ("dapo", "bu_1to2", "eval_trainset_qwen25_dapo_lora_bu_1to2"), ("dapo", "bu_1to2", "eval_trainset_qwen25_dapo_full_bu_1to2"), ] def _mode(name): return "full" if "_full" in name else "lora" def load_records(path): recs = [] with open(path) as f: for line in f: line = line.strip() if line: recs.append(json.loads(line)) return recs def main(): ap = argparse.ArgumentParser() ap.add_argument("--eval-dir", default="data/eval") ap.add_argument("--csv", default="data/eval/splits_comparison.csv") ap.add_argument("--ms-dir", default=MS_LABEL_DIR, help="ms_label dir to backfill oracle_ms (lights up strategy/by_ms metrics)") args = ap.parse_args() eval_dir = _r(args.eval_dir) ms_dir = _r(args.ms_dir) # cache split membership (rl_ids / sft_ids) per tag split_cache = {} for tag, d in SPLIT_DIR.items(): dd = _r(d) try: rl = set(load_json(os.path.join(dd, "rl_ids.json"))) sft = set(load_json(os.path.join(dd, "sft_ids.json"))) split_cache[tag] = (rl, sft) except Exception as e: logger.warning(f"split {tag} ids missing ({d}): {e}") split_cache[tag] = (set(), set()) table = [] for stage, tag, base in ROWS: path = os.path.join(eval_dir, base + ".jsonl") if not os.path.exists(path): logger.info(f"skip (no eval jsonl yet): {base}") continue recs = load_records(path) if not recs: continue n_ms = backfill_oracle_ms(recs, ms_dir) # light up strategy/by_ms (oracle_ms None otherwise) rl_ids, sft_ids = split_cache.get(tag, (set(), set())) # tag each record seen = [r for r in recs if r.get("query_id") in rl_ids] # DAPO trained on this query unseen = [r for r in recs if r.get("query_id") in sft_ids] # DAPO never updated on it overall = aggregate(recs) row = { "model": base, "stage": stage, "split": tag, "mode": _mode(base), "n": overall["n"], "judge_acc": overall.get("judge_acc"), "judge_correct": overall.get("judge_correct_rate"), "em": round(overall["em"], 4), "f1": round(overall["f1"], 4), "strategy_acc": overall.get("strategy_accuracy"), "over_retr": overall.get("over_retrieval_rate"), "under_retr": overall.get("under_retrieval_rate"), "hallu": overall.get("hallucinated_rate"), "avg_retr_calls": overall.get("avg_retrieval_calls"), "n_seen": len(seen), "n_unseen": len(unseen), "judge_seen": aggregate(seen).get("judge_acc") if seen else None, "judge_unseen": aggregate(unseen).get("judge_acc") if unseen else None, "by_user": tag in BY_USER, "judge_by_ms": overall.get("judge_by_ms") or {}, } table.append(row) if not table: logger.warning("没有可分析的 eval jsonl(模型还没评)。先跑 run_eval_splits.sh。") return # ---- 1. 横向对比表 ---- def fmt(v): return " — " if v is None else f"{v:.3f}" print("\n" + "=" * 120) print("跨切分对比(评测集 dataset_eval100, 100 用户 / 308 QA, 同集可比)") print("=" * 120) hdr = (f"{'model':52s} {'stage':4s} {'split':8s} {'mode':4s} " f"{'judge':>6s} {'corr':>6s} {'EM':>6s} {'F1':>6s} {'strat':>6s} {'hallu':>6s}") print(hdr); print("-" * 120) for r in table: print(f"{r['model']:52s} {r['stage']:4s} {r['split']:8s} {r['mode']:4s} " f"{fmt(r['judge_acc']):>6s} {fmt(r['judge_correct']):>6s} {fmt(r['em']):>6s} " f"{fmt(r['f1']):>6s} {fmt(r['strategy_acc']):>6s} {fmt(r['hallu']):>6s}") # ---- 2. DAPO seen / unseen 拆分(只对 DAPO 行有意义) ---- print("\n" + "=" * 120) print("DAPO seen(RL训练过该query) vs unseen(仅SFT见过) judge_acc 拆分") print(" ⚠️ by-user 切分: unseen = DAPO 从没训过的【用户】(SFT/RL 用户 disjoint) → 真·泛化") print(" ⚠️ by-query 切分: 用户重叠, unseen 仅 query 级 held-out → 解释力弱,仅参考") print("=" * 120) print(f"{'model':52s} {'by_user':7s} {'n_seen':>6s} {'judge_seen':>11s} " f"{'n_unseen':>8s} {'judge_unseen':>13s} {'Δ(seen-unseen)':>15s}") print("-" * 120) for r in table: if r["stage"] != "dapo": continue js, ju = r["judge_seen"], r["judge_unseen"] delta = (f"{js - ju:+.3f}" if (js is not None and ju is not None) else " — ") print(f"{r['model']:52s} {str(r['by_user']):7s} {r['n_seen']:>6d} {fmt(js):>11s} " f"{r['n_unseen']:>8d} {fmt(ju):>13s} {delta:>15s}") # ---- 2b. 按 MS 的 judge_acc(校准信号: 期望 SM > PM > VM > NM) ---- print("\n" + "=" * 120) print("judge_acc by FIR state(校准信号: 模型记得越多答得越准 → 期望 SM ≥ PM ≥ VM ≥ NM)") print("=" * 120) print(f"{'model':52s} {'split':8s} {'mode':4s} {'SM':>7s} {'PM':>7s} {'VM':>7s} {'NM':>7s}") print("-" * 120) for r in table: bm = r["judge_by_ms"] print(f"{r['model']:52s} {r['split']:8s} {r['mode']:4s} " f"{fmt(bm.get('SM')):>7s} {fmt(bm.get('PM')):>7s} " f"{fmt(bm.get('VM')):>7s} {fmt(bm.get('NM')):>7s}") # ---- 3. CSV ---- csv_path = _r(args.csv) os.makedirs(os.path.dirname(csv_path), exist_ok=True) # flatten judge_by_ms into scalar columns; drop the dict itself fields = [k for k in table[0].keys() if k != "judge_by_ms"] + \ ["judge_SM", "judge_PM", "judge_VM", "judge_NM"] with open(csv_path, "w", newline="") as f: w = csv.DictWriter(f, fieldnames=fields) w.writeheader() for r in table: bm = r.get("judge_by_ms") or {} row = {k: v for k, v in r.items() if k != "judge_by_ms"} row.update({"judge_SM": bm.get("SM"), "judge_PM": bm.get("PM"), "judge_VM": bm.get("VM"), "judge_NM": bm.get("NM")}) w.writerow(row) print(f"\nCSV → {csv_path}") if __name__ == "__main__": main()