| |
| """IDBD vs Autostep vs value 的真实回路对照分析。 |
| |
| 回答: **步长分化到底带不带来收益?** |
| |
| 臂: |
| idbd IDBD, mu=0.05, alpha0=0.2 (我们之前唯一能找到的活点) |
| auto Autostep, mu=1e-2, alpha0=0.1 (论文推荐默认, **未针对本任务调参**) |
| auto2 Autostep, mu=0.05, alpha0=0.2 (IDBD 的活点; 看 Autostep 是否不敏感) |
| value 手工价值函数 (固定公式排序器) —— 已证等价于随机 |
| |
| 判据: |
| 1) 步长是否分化: alpha_std > 0 且 max/min > 1.5 |
| 2) 分化是否带来收益: 相对 value 的 any_time 提升, Welch t 检验 |
| 3) Autostep 是否免调参: 两档 (mu=1e-2/alpha0=0.1) 与 (mu=0.05/alpha0=0.2) |
| 的**表现是否接近** —— 接近 = 免调参成立 |
| 4) 可靠标准: std < mean/2 |
| """ |
| import glob |
| import json |
| import math |
| import os |
| import re |
| import sys |
|
|
| import numpy as np |
|
|
| ROOT = sys.argv[1] if len(sys.argv) > 1 else "/work/liuyuanjie/headless" |
|
|
|
|
| def t_sf(x, df): |
| """t 分布上尾概率 (stdlib 数值积分, 避免依赖 scipy)。""" |
| if x <= 0: |
| return 0.5 + (0.5 - t_sf(-x, df)) |
| c = math.gamma((df + 1) / 2) / (math.sqrt(df * math.pi) * math.gamma(df / 2)) |
| n, hi = 20000, x + 60.0 |
| h = (hi - x) / n |
| s = 0.0 |
| for i in range(n + 1): |
| t = x + i * h |
| w = 1 if i in (0, n) else (4 if i % 2 else 2) |
| s += w * c * (1 + t * t / df) ** (-(df + 1) / 2) |
| return s * h / 3 |
|
|
|
|
| rows = {} |
| for f in glob.glob(os.path.join(ROOT, "results/rl_*/lm4_wave_results.json")): |
| tag = os.path.basename(os.path.dirname(f)) |
| m = re.match(r"rl_(.+?)_s(\d+)$", tag) |
| if not m: |
| continue |
| arm, seed = m.group(1), int(m.group(2)) |
| try: |
| d = json.load(open(f)) |
| except Exception as e: |
| print(" [坏文件]", tag, e) |
| continue |
| v = d.get("replay") |
| if isinstance(v, dict) and v.get("any_time_acc") is not None: |
| rows.setdefault(arm, []).append((seed, v)) |
|
|
| if not rows: |
| sys.exit("没有可用结果") |
|
|
| print("=" * 100) |
| print("步长自适应在真实回路的效果 (lm4, replay 臂, stream=revisit, 12 轮)") |
| print("=" * 100) |
| print("%-8s %-3s %9s %8s %6s %9s %9s | %-24s %s" |
| % ("arm", "n", "any_time", "std", "CV%", "replay", "最差遗忘", |
| "alpha std / max-min", "algo")) |
| print("-" * 100) |
| for arm, vs in sorted(rows.items()): |
| at = np.array([v["any_time_acc"] for _, v in vs], float) |
| if len(at) < 2: |
| print("%-8s %-3d %9.4f %8s %6s %9s %9s" % (arm, len(at), at.mean(), |
| "-", "-", "-", "-") + " (待更多 seed)") |
| continue |
| rp = np.array([v["final_mean_acc"] for _, v in vs], float) |
| wf = np.array([v["worst_case_forget"] for _, v in vs], float) |
| st = vs[0][1].get("proposer_stats", {}) |
| al = st.get("alpha", {}) |
| cv = 100 * at.std(ddof=1) / at.mean() if len(at) > 1 else float("nan") |
| print("%-8s %-3d %9.4f %8.4f %6.1f %9.4f %9.4f | %-24s %s" |
| % (arm, len(at), at.mean(), at.std(ddof=1) if len(at) > 1 else 0, cv, |
| rp.mean(), wf.mean(), |
| "%.4f / %.2fx" % (al.get("std", 0), al.get("ratio_max_min", 0)), |
| st.get("algo", "-"))) |
|
|
| print() |
| print("=== 判据 1: 步长是否分化 (alpha_std>0 且 ratio>1.5) ===") |
| for arm, vs in sorted(rows.items()): |
| al = vs[0][1].get("proposer_stats", {}).get("alpha", {}) |
| s, r = al.get("std", 0), al.get("ratio_max_min", 0) |
| print(" %-8s std=%.4f ratio=%.2f %s" % (arm, s, r, |
| "分化 ✓" if (s > 0 and r > 1.5) else ("未分化" if s == 0 else "弱"))) |
|
|
| if len(rows) > 1: |
| print() |
| print("=== 判据 2: 两两 Welch t 检验 (any_time) ===") |
| keys = sorted(rows) |
| print(" %-26s %9s %7s %9s %s" % ("对比", "Δmean", "t", "p", "判定")) |
| for i, a in enumerate(keys): |
| for b in keys[i + 1:]: |
| A = np.array([v["any_time_acc"] for _, v in rows[a]], float) |
| B = np.array([v["any_time_acc"] for _, v in rows[b]], float) |
| if len(A) < 2 or len(B) < 2: |
| print(" %-26s %+9.4f %7s %9s %s" |
| % ("%s - %s" % (a, b), A.mean() - B.mean(), "-", "-", |
| "n<2, 待更多 seed")) |
| continue |
| se = math.sqrt(A.var(ddof=1) / len(A) + B.var(ddof=1) / len(B)) |
| t = (A.mean() - B.mean()) / se |
| df = se ** 4 / ((A.var(ddof=1) / len(A)) ** 2 / (len(A) - 1) |
| + (B.var(ddof=1) / len(B)) ** 2 / (len(B) - 1)) |
| p = 2 * t_sf(abs(t), df) |
| print(" %-26s %+9.4f %7.2f %9.4f %s" |
| % ("%s - %s" % (a, b), A.mean() - B.mean(), t, p, |
| "显著" if p < 0.05 else "不显著")) |
|
|
| print() |
| print("=== 判据 3: Autostep 免调参性 (两档超参的表现差) ===") |
| if "auto" in rows and "auto2" in rows: |
| a1 = np.mean([v["any_time_acc"] for _, v in rows["auto"]]) |
| a2 = np.mean([v["any_time_acc"] for _, v in rows["auto2"]]) |
| print(" auto (论文默认 mu=1e-2,a0=0.1) %.4f vs auto2 (mu=0.05,a0=0.2) %.4f" |
| % (a1, a2)) |
| print(" 差 %.4f -> %s" % (abs(a1 - a2), |
| "免调参成立 (两档表现接近) ✓" if abs(a1 - a2) < 0.03 |
| else "仍对超参敏感 ✗")) |
| else: |
| print(" (需要 auto 与 auto2 两个臂都完成)") |
|
|
| print() |
| print("=== 可靠标准 (leo: std < mean/2) ===") |
| for arm, vs in sorted(rows.items()): |
| at = np.array([v["any_time_acc"] for _, v in vs], float) |
| s = at.std(ddof=1) if len(at) > 1 else 0 |
| print(" %-8s std=%.4f mean/2=%.4f %s" |
| % (arm, s, at.mean() / 2, "✓" if s < at.mean() / 2 else "✗")) |
|
|
