"""app/harness/gate_ladder.py 单元测试。 覆盖冷启动交错、Beta(1,1) 平滑、warm 信息量排序、 GatePools 原子读写与指纹校验、BaselineCache 四维内容寻址与先盘后存、 gamma-EMA 更新、防泄露过滤等核心语义。 """ from __future__ import annotations from typing import TYPE_CHECKING import pytest from app.harness.gate_ladder import ( BaselineCache, GatePools, LadderEntry, build_cold_entries, build_or_load_gate_pools, order_ladder, skill_hash, ) from core.types import GeneratedQuestion if TYPE_CHECKING: from pathlib import Path # ── 工具函数 ────────────────────────────────────────────────────────── def _make_q(qid: str, task_type: str = "AR") -> GeneratedQuestion: """构造最小 GeneratedQuestion 实例。""" return GeneratedQuestion( question_id=qid, video_id="v1", task_type=task_type, question="dummy", options=("A", "B", "C", "D"), answer="A", source_nodes=("n1",), difficulty="easy", ) # ── 冷启动 ──────────────────────────────────────────────────────────── class TestColdStart: """冷启动排序：2:1 交错 + 探针插尾 + Beta(1,1) 平滑。""" def test_cold_start_interleaving(self) -> None: """错题:对题 = 2:1 交错顺序。 6 错 3 对（probe_quota=0 无探针）→ 交错序应为 W W R W W R W W R。 """ wrong_ids = [f"w{i}" for i in range(6)] right_ids = [f"r{i}" for i in range(3)] questions = [_make_q(qid) for qid in wrong_ids + right_ids] correctness = dict.fromkeys(wrong_ids, False) correctness.update(dict.fromkeys(right_ids, True)) entries = build_cold_entries(questions, correctness, probe_quota=0.0, seed=42) assert len(entries) == 9 # 验证 2:1 交错模式（seed 固定后 shuffle 结果确定） pattern = ["W" if not correctness[e.question_id] else "R" for e in entries] # 前 9 个交错应为 W W R W W R W W R assert pattern == ["W", "W", "R", "W", "W", "R", "W", "W", "R"] def test_cold_start_p_hat_beta(self) -> None: """p_hat 遵循 Beta(1,1) 平滑：错=1/3，对=2/3。""" questions = [_make_q("q1"), _make_q("q2")] correctness = {"q1": False, "q2": True} entries = build_cold_entries(questions, correctness, probe_quota=0.0, seed=0) p_map = {e.question_id: e.p_hat for e in entries} assert p_map["q1"] == pytest.approx(1 / 3) assert p_map["q2"] == pytest.approx(2 / 3) def test_cold_start_probe_at_tail(self) -> None: """probe_quota > 0 时探针题追加在尾部。""" wrong_ids = [f"w{i}" for i in range(10)] right_ids = [f"r{i}" for i in range(2)] questions = [_make_q(qid) for qid in wrong_ids + right_ids] correctness = dict.fromkeys(wrong_ids, False) correctness.update(dict.fromkeys(right_ids, True)) entries = build_cold_entries(questions, correctness, probe_quota=0.3, seed=7) # 10 错 * 0.3 = 3 个探针在尾部 n_probe = int(10 * 0.3) assert n_probe == 3 # 尾部 3 个都应为错题 tail = entries[-n_probe:] for e in tail: assert not correctness[e.question_id] # ── warm 排序 ────────────────────────────────────────────────────────── class TestWarmOrdering: """warm 阶段：信息量 p_hat(1-p_hat) 降序 + p_hat 区间过滤。""" def test_warm_ordering_information(self) -> None: """p_hat=0.5 信息量最高，排在最前。""" entries = [ LadderEntry("a", 0.1), LadderEntry("b", 0.5), LadderEntry("c", 0.9), LadderEntry("d", 0.3), ] ordered = order_ladder(entries, p_low=0.0, p_high=1.0) assert ordered[0].question_id == "b" # 0.5*(1-0.5)=0.25 最高 # d: 0.3*0.7=0.21, a: 0.1*0.9=0.09, c: 0.9*0.1=0.09 assert ordered[1].question_id == "d" def test_warm_filter_bounds(self) -> None: """p_hat 不在 [p_low, p_high] 区间的题被剔除。""" entries = [ LadderEntry("low", 0.05), LadderEntry("mid", 0.5), LadderEntry("high", 0.95), ] ordered = order_ladder(entries, p_low=0.1, p_high=0.9) ids = [e.question_id for e in ordered] assert "mid" in ids assert "low" not in ids assert "high" not in ids # ── GatePools 持久化 ────────────────────────────────────────────────── class TestGatePoolsPersistence: """GatePools.save/load 原子性与指纹校验。""" def test_gate_pools_save_load_atomic(self, tmp_path: Path) -> None: """save -> load 往返保真，且使用原子写（中间 .tmp 文件不残留）。""" entries = { "AR": [LadderEntry("q1", 0.33), LadderEntry("q2", 0.67)], "CR": [LadderEntry("q3", 0.5)], } pools = GatePools(entries=entries, seed=42, fingerprint="abc123") path = tmp_path / "gate_pools.json" pools.save(path) # .tmp 文件不应残留 assert not (tmp_path / "gate_pools.json.tmp").exists() assert path.exists() loaded = GatePools.load(path) assert loaded.seed == 42 assert loaded.fingerprint == "abc123" assert len(loaded.entries["AR"]) == 2 assert loaded.entries["AR"][0].question_id == "q1" assert loaded.entries["AR"][0].p_hat == pytest.approx(0.33) assert loaded.entries["CR"][0].question_id == "q3" def test_gate_pools_fingerprint_mismatch(self, tmp_path: Path) -> None: """指纹不一致 -> RuntimeError（不静默重建）。""" questions = [_make_q("q1", "AR"), _make_q("q2", "AR")] correctness = {"q1": True, "q2": False} # 第一次构建 build_or_load_gate_pools( workspace_dir=tmp_path, questions=questions, test_qids=set(), baseline_correctness=correctness, task_types=["AR"], probe_quota=0.0, seed=1, baseline_run_id="run_001", ) # 改 baseline_run_id 导致指纹变化 -> 应报错 with pytest.raises(RuntimeError, match="指纹不一致"): build_or_load_gate_pools( workspace_dir=tmp_path, questions=questions, test_qids=set(), baseline_correctness=correctness, task_types=["AR"], probe_quota=0.0, seed=1, baseline_run_id="run_002", ) # ── ladder_for ──────────────────────────────────────────────────────── class TestLadderFor: """ladder_for 取题序与排除逻辑。""" def test_ladder_for_excludes_qids(self) -> None: """exclude_qids 中的题被排除。""" entries = { "AR": [ LadderEntry("q1", 0.5), LadderEntry("q2", 0.4), LadderEntry("q3", 0.6), ], } pools = GatePools(entries=entries, seed=0, fingerprint="x") result = pools.ladder_for("AR", exclude_qids={"q2"}, p_low=0.0, p_high=1.0, cold=True) assert "q2" not in result assert "q1" in result assert "q3" in result def test_ladder_for_missing_task_type(self) -> None: """不存在的 task_type -> ValueError。""" pools = GatePools(entries={}, seed=0, fingerprint="x") with pytest.raises(ValueError, match="无阶梯"): pools.ladder_for("MISSING", set(), 0.0, 1.0, cold=True) def test_ladder_for_warm_uses_order_ladder(self) -> None: """cold=False 时走 warm 信息量排序。""" entries = { "AR": [ LadderEntry("low", 0.1), LadderEntry("mid", 0.5), LadderEntry("high", 0.9), ], } pools = GatePools(entries=entries, seed=0, fingerprint="x") result = pools.ladder_for("AR", set(), p_low=0.0, p_high=1.0, cold=False) # 信息量排序：mid(0.25) > low(0.09) = high(0.09) assert result[0] == "mid" # ── gamma-EMA 更新 ───────────────────────────────────────────────────── class TestGammaEMA: """gamma-EMA 更新 p_hat。""" def test_gamma_ema_update(self) -> None: """p_hat <- gamma * p_hat + (1-gamma) * obs。""" entries = {"AR": [LadderEntry("q1", 0.5)]} pools = GatePools(entries=entries, seed=0, fingerprint="x") # 观测为正确(1.0), gamma=0.8 pools.update_probs({"q1": True}, gamma=0.8) expected = 0.8 * 0.5 + 0.2 * 1.0 # 0.6 assert pools.entries["AR"][0].p_hat == pytest.approx(expected) # 再次观测为错误(0.0), gamma=0.8 pools.update_probs({"q1": False}, gamma=0.8) expected2 = 0.8 * expected + 0.2 * 0.0 # 0.48 assert pools.entries["AR"][0].p_hat == pytest.approx(expected2) def test_update_probs_no_observation_unchanged(self) -> None: """无观测的题 p_hat 不变。""" entries = {"AR": [LadderEntry("q1", 0.5), LadderEntry("q2", 0.3)]} pools = GatePools(entries=entries, seed=0, fingerprint="x") pools.update_probs({"q1": True}, gamma=0.9) assert pools.entries["AR"][1].p_hat == pytest.approx(0.3) # ── 防泄露 ───────────────────────────────────────────────────────────── class TestLeakPrevention: """防泄露铁律：gate 内 rollout 永不回流 p_hat（由调用方过滤）。""" def test_update_probs_excludes_gate_runs(self) -> None: """调用方须过滤 run_id 含 '_gate_' 的观测。 update_probs 本身只接收已过滤的 observations，这里验证 如果调用方正确过滤，gate run 数据不会影响 p_hat。 """ entries = {"AR": [LadderEntry("q1", 0.5)]} pools = GatePools(entries=entries, seed=0, fingerprint="x") # 模拟：所有 run 的原始观测（含 gate run） raw_observations = { "run_normal": {"q1": True}, # 普通 run "run_gate_01": {"q1": False}, # gate run（run_id 含 _gate_） } # 调用方按 run_id 过滤：排除含 "_gate_" 的 run filtered = {} for run_id, obs in raw_observations.items(): if "_gate_" not in run_id: filtered.update(obs) # 只有普通 run 的观测进入 update_probs assert filtered == {"q1": True} pools.update_probs(filtered, gamma=0.8) expected = 0.8 * 0.5 + 0.2 * 1.0 assert pools.entries["AR"][0].p_hat == pytest.approx(expected) # ── BaselineCache ────────────────────────────────────────────────────── class TestBaselineCache: """BaselineCache 四维内容寻址与先盘后存。""" def test_baseline_cache_content_addressed(self, tmp_path: Path) -> None: """四维键唯一寻址：任一维度变化 -> miss。""" path = tmp_path / "baseline_cache.json" cache = BaselineCache(path) cache.put("AR", "hash1", "v1", "q1", True) assert cache.get("AR", "hash1", "v1", "q1") is True # 改 skill_hash -> miss assert cache.get("AR", "hash2", "v1", "q1") is None # 改 prompts_version -> miss assert cache.get("AR", "hash1", "v2", "q1") is None # 改 task_type -> miss assert cache.get("CR", "hash1", "v1", "q1") is None # 改 qid -> miss assert cache.get("AR", "hash1", "v1", "q2") is None def test_baseline_cache_disk_first(self, tmp_path: Path) -> None: """先盘后存：磁盘写成功后内存才更新，新实例可从磁盘读到。""" path = tmp_path / "baseline_cache.json" cache = BaselineCache(path) cache.put("AR", "h1", "v1", "q1", True) # 内存可读 assert cache.get("AR", "h1", "v1", "q1") is True # 新实例从磁盘加载也能读到（证明先落盘） cache2 = BaselineCache(path) assert cache2.get("AR", "h1", "v1", "q1") is True # .tmp 文件不应残留 assert not (tmp_path / "baseline_cache.json.tmp").exists() def test_baseline_cache_empty_init(self, tmp_path: Path) -> None: """不存在的文件 -> 空缓存初始化。""" path = tmp_path / "nonexistent.json" cache = BaselineCache(path) assert cache.get("AR", "h1", "v1", "q1") is None def test_baseline_cache_overwrite(self, tmp_path: Path) -> None: """同键重复写入覆盖旧值。""" path = tmp_path / "baseline_cache.json" cache = BaselineCache(path) cache.put("AR", "h1", "v1", "q1", True) assert cache.get("AR", "h1", "v1", "q1") is True cache.put("AR", "h1", "v1", "q1", False) assert cache.get("AR", "h1", "v1", "q1") is False # ── skill_hash ───────────────────────────────────────────────────────── class TestSkillHash: """skill_hash SHA1 摘要。""" def test_deterministic(self) -> None: """相同输入产生相同摘要。""" assert skill_hash("hello") == skill_hash("hello") def test_different_content(self) -> None: """不同输入产生不同摘要。""" assert skill_hash("hello") != skill_hash("world") def test_is_sha1_hex(self) -> None: """输出为 40 字符十六进制。""" h = skill_hash("test") assert len(h) == 40 assert all(c in "0123456789abcdef" for c in h)