iomgaa
d6a3107e4e
stratified_sample 先 build_units 聚合，以 QuestionUnit 为采样原子做 分层/去重/补足/rng.sample，返回前 flatten_units 展开为逐题列表； size/correct_ratio/min_per_class 均按 unit 计数，单元正确性走成员 AND， 孪生对两题永不被劈开。纯 single 输入下 build_units 1:1 折叠、顺序不变， rng 消耗与旧逐题实现字节级一致（新增回归测试守护）。 _backfill_per_class candidates 改按 unit 枚举去重；build_units/flatten_units 函数内延迟导入以规避 question_gen<->harness 循环依赖（沿用 adversarial_filter）。 load_benchmark 反序列化补 pair_id/question_role/flip_axis/unit_id 四字段， 用 .get 兼容旧 JSON（缺失退化为 single，unit_id 由 __post_init__ 回填）。 pools._sample_excluding 随之改为透传 flatten_units(candidates) 给已单元化的 stratified_sample（不再用 lone pair-original 代表），行为对 single-only 保持等价。
298 lines
11 KiB
Python
298 lines
11 KiB
Python
"""loader.stratified_sample 按 unit 采样 + load_benchmark 读回 pair 字段的单元测试。
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覆盖 question-gen v3 Phase 1 Task 4 的四项契约：
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(a) pair 采样同进同出——同一 pair_id 两题要么都被选、要么都不被选；
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(b) size / correct_ratio 按 **unit 计数**（pair 计 1 个 unit，unit 正确性走双向 AND）；
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(c) min_per_class 补足路径不拆 pair；
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(d) load_benchmark 反序列化读回 pair_id/question_role/flip_axis/unit_id，
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且旧 JSON 缺这些字段时按 single 默认兜底、不崩。
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另加一条纯 single 输入的字节级回归守护：单题场景采样序列必须与旧逐题行为一致。
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"""
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from __future__ import annotations
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import json
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import random
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from pathlib import Path
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import pytest
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from app.harness.question_units import build_units
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from app.question_gen.loader import load_benchmark, stratified_sample
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from core.types import GeneratedQuestion
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def _single(qid: str, task_type: str = "Single") -> GeneratedQuestion:
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"""构造一道 single 题（无 pair 归属）。"""
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return GeneratedQuestion(
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question_id=qid,
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video_id="v",
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task_type=task_type,
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question="?",
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options=("A", "B", "C", "D"),
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answer="A",
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source_nodes=(),
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difficulty="medium",
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)
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def _pair(pid: str, task_type: str = "AR") -> list[GeneratedQuestion]:
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"""构造一个合法孪生对（original + mirror），共享 pair_id / unit_id / flip_axis。"""
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base = dict(
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video_id="v",
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task_type=task_type,
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question="?",
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options=("A", "B", "C", "D"),
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answer="A",
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source_nodes=(),
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difficulty="hard",
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pair_id=pid,
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unit_id=pid,
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flip_axis="before_after",
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)
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return [
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GeneratedQuestion(question_id=f"{pid}_o", question_role="pair_original", **base),
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GeneratedQuestion(question_id=f"{pid}_m", question_role="pair_mirror", **base),
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]
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def _pair_roles_by_id(questions: list[GeneratedQuestion]) -> dict[str, set[str]]:
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"""将采样结果按 pair_id 聚合出现的角色集合（仅统计配对题）。"""
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loc: dict[str, set[str]] = {}
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for q in questions:
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if q.pair_id:
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loc.setdefault(q.pair_id, set()).add(q.question_role)
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return loc
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class TestPairAtomicSampling:
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def test_pair_never_split_in_natural_sample(self) -> None:
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"""自然分布采样：命中的 pair 必两题齐全，size 按 unit 计数。"""
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qs = [q for i in range(8) for q in _pair(f"p{i}")]
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result = stratified_sample(
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questions=qs,
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correctness={},
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size=4,
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correct_ratio=None,
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task_types=None,
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seed=7,
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min_per_class=None,
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)
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loc = _pair_roles_by_id(result)
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assert len(loc) == 4, "size=4 应命中 4 个 pair 单元"
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for pid, roles in loc.items():
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assert roles == {"pair_original", "pair_mirror"}, f"pair {pid} 被拆: {roles}"
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assert len(result) == 8, "4 个 pair 单元展开应为 8 道题"
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def test_size_counts_units_not_questions(self) -> None:
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"""混合 single + pair 时 size 仍按 unit 计数（pair 计 1）。"""
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qs = [_single(f"s{i}") for i in range(3)]
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qs += [q for i in range(3) for q in _pair(f"p{i}")]
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result = stratified_sample(
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questions=qs,
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correctness={},
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size=6, # 全部 6 个单元（3 single + 3 pair）
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correct_ratio=None,
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task_types=None,
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seed=1,
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min_per_class=None,
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)
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singles = [q for q in result if not q.pair_id]
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loc = _pair_roles_by_id(result)
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assert len(singles) == 3
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assert len(loc) == 3
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for roles in loc.values():
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assert roles == {"pair_original", "pair_mirror"}
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assert len(result) == 3 + 3 * 2
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def test_ratio_counts_units(self) -> None:
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"""correct_ratio 按 unit 计数：对/错单元按比例各取整数个。"""
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correct_pairs = [_pair(f"c{i}") for i in range(4)]
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wrong_pairs = [_pair(f"w{i}") for i in range(4)]
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qs = [q for p in correct_pairs + wrong_pairs for q in p]
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correctness: dict[str, bool] = {}
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for p in correct_pairs:
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for q in p:
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correctness[q.question_id] = True
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result = stratified_sample(
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questions=qs,
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correctness=correctness,
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size=4,
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correct_ratio=0.5,
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task_types=None,
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seed=3,
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min_per_class=None,
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)
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loc = _pair_roles_by_id(result)
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assert len(loc) == 4, "4 个 unit"
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for roles in loc.values():
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assert roles == {"pair_original", "pair_mirror"}
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correct_units = sum(
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1 for pid in loc if all(correctness.get(f"{pid}_{s}", False) for s in ("o", "m"))
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)
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assert correct_units == 2, "correct_ratio=0.5 * 4 unit = 2 个对单元"
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def test_unit_correctness_uses_and_not_any(self) -> None:
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"""unit 正确性走双向 AND：混合对（P 对 Q 错）不得计入对单元层。
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构造 2 个全对 pair + 1 个混合 pair（original 对、mirror 错）。请求 3 个对单元，
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若按 AND，对池仅 2 个 → 分层不足报错；若错误地按 any-member，对池 3 个 → 不报错。
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以是否抛错来判别语义，规避随机命中导致的假阳性。
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"""
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good = [_pair("g0"), _pair("g1")]
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mixed = _pair("mx")
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qs = [q for p in good for q in p] + mixed
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correctness: dict[str, bool] = {}
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for p in good:
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for q in p:
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correctness[q.question_id] = True
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correctness[mixed[0].question_id] = True
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correctness[mixed[1].question_id] = False
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with pytest.raises(ValueError, match="分层不足"):
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stratified_sample(
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questions=qs,
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correctness=correctness,
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size=3,
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correct_ratio=1.0,
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task_types=None,
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seed=1,
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min_per_class=None,
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)
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class TestBackfillPairAtomic:
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def test_backfill_keeps_pairs_atomic(self) -> None:
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"""min_per_class 补足稀疏 pair 题型时，补入的 pair 两题齐全不拆。"""
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ar = [q for i in range(3) for q in _pair(f"a{i}", task_type="AR")]
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main = [_single(f"m{i}", task_type="Main") for i in range(10)]
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result = stratified_sample(
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questions=ar + main,
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correctness={},
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size=2,
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correct_ratio=None,
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task_types=None,
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seed=5,
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min_per_class=2,
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)
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loc = _pair_roles_by_id(result)
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assert len(loc) >= 2, "AR 至少被补足到 2 个 pair 单元"
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for pid, roles in loc.items():
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assert roles == {"pair_original", "pair_mirror"}, f"补足拆散了 pair {pid}: {roles}"
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def test_backfill_no_duplicate_units(self) -> None:
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"""补足不得重复选入同一 pair 单元。"""
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ar = [q for i in range(4) for q in _pair(f"a{i}", task_type="AR")]
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result = stratified_sample(
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questions=ar,
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correctness={},
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size=1,
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correct_ratio=None,
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task_types=None,
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seed=9,
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min_per_class=3,
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)
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ids = [q.question_id for q in result]
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assert len(ids) == len(set(ids)), "存在重复题目"
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class TestLoadBenchmarkPairFields:
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def test_reads_pair_fields(self, tmp_path: Path) -> None:
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"""新格式 JSON 的 pair_id/question_role/flip_axis/unit_id 被正确读回。"""
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data = [
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{
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"question_id": "pp_o",
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"video_id": "v",
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"task_type": "AR",
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"question": "?",
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"options": ["A", "B", "C", "D"],
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"answer": "A",
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"pair_id": "pp",
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"question_role": "pair_original",
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"flip_axis": "before_after",
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"unit_id": "pp",
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},
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{
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"question_id": "pp_m",
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"video_id": "v",
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"task_type": "AR",
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"question": "?",
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"options": ["A", "B", "C", "D"],
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"answer": "A",
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"pair_id": "pp",
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"question_role": "pair_mirror",
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"flip_axis": "before_after",
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"unit_id": "pp",
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},
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]
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(tmp_path / "vid.json").write_text(json.dumps(data), encoding="utf-8")
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qs = load_benchmark(tmp_path)
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q0 = next(q for q in qs if q.question_id == "pp_o")
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assert q0.pair_id == "pp"
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assert q0.question_role == "pair_original"
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assert q0.flip_axis == "before_after"
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assert q0.unit_id == "pp"
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# 读回后应能被 build_units 重新聚合成 1 个 pair 单元
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units = build_units(qs)
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assert len(units) == 1
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assert units[0].kind == "pair"
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def test_legacy_json_defaults_to_single(self, tmp_path: Path) -> None:
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"""旧 JSON 缺 pair 字段时按 single 兜底、unit_id 回填为 question_id，不崩。"""
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data = [
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{
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"question_id": "x1",
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"video_id": "v",
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"task_type": "T",
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"question": "?",
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"options": ["A", "B", "C", "D"],
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"answer": "A",
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}
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]
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(tmp_path / "legacy.json").write_text(json.dumps(data), encoding="utf-8")
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qs = load_benchmark(tmp_path)
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assert qs[0].pair_id is None
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assert qs[0].question_role == "single"
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assert qs[0].flip_axis is None
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assert qs[0].unit_id == "x1"
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units = build_units(qs)
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assert len(units) == 1
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assert units[0].kind == "single"
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class TestPureSingleByteIdentical:
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def test_natural_matches_legacy_rng(self) -> None:
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"""纯 single 输入的自然分布采样，与旧逐题 rng.sample 序列字节级一致。"""
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qs = [_single(f"s{i}") for i in range(20)]
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seed = 123
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result = stratified_sample(
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questions=qs,
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correctness={},
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size=8,
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correct_ratio=None,
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task_types=None,
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seed=seed,
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min_per_class=None,
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)
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reference = random.Random(seed).sample(qs, 8)
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assert [q.question_id for q in result] == [q.question_id for q in reference]
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def test_ratio_matches_legacy_rng(self) -> None:
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"""纯 single 输入的比例分层采样，与旧逐题实现的抽样序列一致。"""
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qs = [_single(f"s{i}") for i in range(20)]
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correctness = {f"s{i}": i < 10 for i in range(20)}
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seed = 77
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result = stratified_sample(
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questions=qs,
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correctness=correctness,
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size=10,
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correct_ratio=0.6,
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task_types=None,
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seed=seed,
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min_per_class=None,
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)
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rng = random.Random(seed)
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correct = [q for q in qs if correctness.get(q.question_id, False)]
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wrong = [q for q in qs if not correctness.get(q.question_id, False)]
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reference = rng.sample(correct, 6) + rng.sample(wrong, 4)
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assert [q.question_id for q in result] == [q.question_id for q in reference]
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