iomgaa
c66a00c924
- save_pools: extended with split_mode and config params; per_category mode writes categories metadata (seed, train_ratio, test_source) for incremental append and consistency validation - load_pools: compatible with both old format (no split_mode) and new format; extra metadata fields ignored during load - build_or_load_pools: signature changed to (config, strategy, db_path); baseline_run_id read from seed.json (not config.run_id); per_category mode does consistency check on reload and supports incremental category append via strategy.build_incremental - Added _to_pool_config, _read_baseline_run_id, _validate_per_category_consistency helpers - Tests: TestPerCategorySaveLoad with 5 test cases covering roundtrip, missing config error, global split_mode field, legacy format compat, multi-type categories Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
582 lines
22 KiB
Python
582 lines
22 KiB
Python
"""三池切分单元测试。
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验证:
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- 三池互斥（question_id 无重叠）
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- test 池自然分布（correct_ratio=None）
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- save/load 往返一致
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- 旧格式拒绝（无 test 键 → ValueError）
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- build_or_load_pools 冻结复用（pools.json 存在时不重切）
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"""
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from __future__ import annotations
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import json
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from typing import TYPE_CHECKING
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import pytest
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from app.harness.pools import (
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GlobalPoolStrategy,
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PerCategoryPoolStrategy,
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build_pools,
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load_pools,
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save_pools,
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)
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from core.types import GeneratedQuestion, PoolConfig
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if TYPE_CHECKING:
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from pathlib import Path
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def _make_question(qid: str, task_type: str = "Action Reasoning") -> GeneratedQuestion:
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"""构造测试用 GeneratedQuestion。
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参数:
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qid: 题目 ID。
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task_type: 题型。
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返回:
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GeneratedQuestion 实例。
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"""
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return GeneratedQuestion(
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question_id=qid,
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video_id="video_001",
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task_type=task_type,
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question=f"Question {qid}?",
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options=("A. opt1", "B. opt2", "C. opt3", "D. opt4"),
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answer="A",
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source_nodes=("node_1",),
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difficulty="medium",
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)
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def _make_question_set(
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n: int,
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task_types: list[str] | None = None,
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) -> list[GeneratedQuestion]:
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"""构造 n 道题，交替分配题型。
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参数:
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n: 题目数量。
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task_types: 可选题型列表，轮转分配；None 默认 2 类。
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返回:
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题目列表。
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"""
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types = task_types or ["Action Reasoning", "Scene Understanding"]
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return [_make_question(f"q_{i:04d}", types[i % len(types)]) for i in range(n)]
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def _make_correctness(
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questions: list[GeneratedQuestion],
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correct_ratio: float = 0.5,
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) -> dict[str, bool]:
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"""构造 correctness 字典，前 correct_ratio 比例标对。
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参数:
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questions: 题目列表。
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correct_ratio: 对题占比。
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返回:
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question_id -> bool。
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"""
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n_correct = round(len(questions) * correct_ratio)
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return {q.question_id: (i < n_correct) for i, q in enumerate(questions)}
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class TestBuildPoolsMutualExclusion:
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"""三池 question_id 互斥验证。"""
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def test_build_pools_mutual_exclusion(self) -> None:
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"""三池切分后，任意两池不共享 question_id。"""
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questions = _make_question_set(200)
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correctness = _make_correctness(questions, 0.5)
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pools = build_pools(
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questions,
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correctness,
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diag_cfg={
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"size": 30,
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"correct_ratio": 0.5,
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"task_types": None,
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"seed": 42,
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"min_per_class": None,
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},
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val_cfg={
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"size": 30,
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"correct_ratio": 0.5,
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"task_types": None,
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"seed": 42,
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"min_per_class": None,
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},
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test_cfg={"size": 30},
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baseline_run_id="run_baseline",
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)
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diag_ids = {q.question_id for q in pools.diagnosis}
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val_ids = {q.question_id for q in pools.validation}
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test_ids = {q.question_id for q in pools.test}
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assert diag_ids & val_ids == set(), "诊断池与验证池有重叠"
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assert diag_ids & test_ids == set(), "诊断池与测试池有重叠"
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assert val_ids & test_ids == set(), "验证池与测试池有重叠"
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assert len(diag_ids) == 30
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assert len(val_ids) == 30
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assert len(test_ids) == 30
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class TestBuildPoolsTestNaturalDistribution:
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"""test 池使用自然分布（correct_ratio=None）。"""
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def test_build_pools_test_natural_distribution(self) -> None:
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"""test 池不强制对错比例，保留候选池的自然分布。
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构造 correctness 为 50% 对/50% 错，diag/val 用 correct_ratio=0.3
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强制裁剪，test 池走自然分布（correct_ratio=None）。验证 test 池
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不受 correct_ratio 约束。
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"""
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questions = _make_question_set(300)
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correctness = _make_correctness(questions, 0.5)
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pools = build_pools(
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questions,
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correctness,
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diag_cfg={
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"size": 20,
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"correct_ratio": 0.3,
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"task_types": None,
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"seed": 42,
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"min_per_class": None,
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},
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val_cfg={
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"size": 20,
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"correct_ratio": 0.3,
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"task_types": None,
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"seed": 42,
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"min_per_class": None,
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},
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test_cfg={"size": 20},
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baseline_run_id="run_baseline",
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)
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# diag/val 被 correct_ratio=0.3 裁剪：round(20*0.3) = 6 对, 14 错
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diag_correct = sum(1 for q in pools.diagnosis if correctness[q.question_id])
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val_correct = sum(1 for q in pools.validation if correctness[q.question_id])
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assert diag_correct == 6, "诊断池应强制 30% 对题"
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assert val_correct == 6, "验证池应强制 30% 对题"
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# test 池自然分布：不受 correct_ratio 约束
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assert len(pools.test) == 20
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class TestSaveLoadPoolsRoundtrip:
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"""save/load 往返一致验证。"""
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def test_save_load_pools_roundtrip(self, tmp_path: Path) -> None:
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"""save_pools → load_pools 后全字段一致。"""
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questions = _make_question_set(100)
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correctness = _make_correctness(questions, 0.5)
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original = build_pools(
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questions,
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correctness,
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diag_cfg={
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"size": 15,
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"correct_ratio": 0.5,
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"task_types": None,
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"seed": 42,
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"min_per_class": None,
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},
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val_cfg={
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"size": 15,
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"correct_ratio": 0.5,
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"task_types": None,
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"seed": 42,
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"min_per_class": None,
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},
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test_cfg={"size": 15},
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baseline_run_id="run_001",
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)
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pools_path = tmp_path / "pools.json"
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save_pools(original, pools_path)
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restored = load_pools(pools_path)
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# 标量字段
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assert restored.baseline_run_id == original.baseline_run_id
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assert restored.baseline_val_accuracy == pytest.approx(original.baseline_val_accuracy)
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assert restored.correctness == original.correctness
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# 三池逐题比对
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for pool_name in ("diagnosis", "validation", "test"):
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orig_list = getattr(original, pool_name)
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rest_list = getattr(restored, pool_name)
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assert len(rest_list) == len(orig_list), f"{pool_name} 长度不一致"
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for o, r in zip(orig_list, rest_list, strict=False):
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assert o.question_id == r.question_id
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assert o.video_id == r.video_id
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assert o.task_type == r.task_type
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assert o.question == r.question
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assert o.options == r.options
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assert o.answer == r.answer
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assert o.source_nodes == r.source_nodes
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assert o.difficulty == r.difficulty
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class TestLoadPoolsOldFormatReject:
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"""旧格式 pools.json（无 test 键）→ ValueError。"""
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def test_load_pools_old_format_reject(self, tmp_path: Path) -> None:
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"""缺少 test 键的 pools.json 必须抛出 ValueError。"""
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old_format = {
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"baseline_run_id": "run_old",
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"baseline_val_accuracy": 0.5,
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"correctness": {},
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"diagnosis": [],
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"validation": [],
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}
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pools_path = tmp_path / "pools.json"
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pools_path.write_text(json.dumps(old_format), encoding="utf-8")
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with pytest.raises(ValueError, match="旧格式"):
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load_pools(pools_path)
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class TestBuildOrLoadPoolsFrozen:
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"""build_or_load_pools 冻结复用：pools.json 存在时原样加载不重切。"""
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def test_build_or_load_pools_frozen(self, tmp_path: Path) -> None:
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"""pools.json 已存在时，build_or_load_pools 返回冻结内容。"""
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questions = _make_question_set(60)
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correctness = _make_correctness(questions, 0.5)
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frozen = build_pools(
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questions,
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correctness,
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diag_cfg={
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"size": 10,
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"correct_ratio": 0.5,
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"task_types": None,
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"seed": 42,
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"min_per_class": None,
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},
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val_cfg={
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"size": 10,
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"correct_ratio": 0.5,
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"task_types": None,
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"seed": 42,
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"min_per_class": None,
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},
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test_cfg={"size": 10},
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baseline_run_id="run_frozen",
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)
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pools_path = tmp_path / "pools.json"
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save_pools(frozen, pools_path)
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# build_or_load_pools 中 pools.json 存在 → 直接 load_pools
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# 此处直接测试 load_pools 行为等价
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loaded = load_pools(pools_path)
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assert loaded.baseline_run_id == frozen.baseline_run_id
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assert loaded.baseline_val_accuracy == pytest.approx(frozen.baseline_val_accuracy)
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assert len(loaded.test) == len(frozen.test)
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assert len(loaded.validation) == len(frozen.validation)
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assert len(loaded.diagnosis) == len(frozen.diagnosis)
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# question_id 完全一致
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for pool_name in ("diagnosis", "validation", "test"):
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orig_ids = [q.question_id for q in getattr(frozen, pool_name)]
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load_ids = [q.question_id for q in getattr(loaded, pool_name)]
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assert orig_ids == load_ids, f"{pool_name} 冻结后 ID 顺序不一致"
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|
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class TestGlobalPoolStrategy:
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"""GlobalPoolStrategy 封装现有全局三分逻辑。"""
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def test_global_strategy_builds_three_pools(self) -> None:
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"""GlobalPoolStrategy.build 产出三个互斥池。"""
|
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questions = _make_question_set(200)
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correctness = _make_correctness(questions, 0.5)
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||
config = PoolConfig(
|
||
task_types=None,
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seed=42,
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baseline_run_id="run_baseline",
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diag_size=30,
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diag_correct_ratio=0.5,
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val_size=30,
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val_correct_ratio=0.5,
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test_size=30,
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eval_min_per_class=1,
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train_ratio=0.667,
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test_questions_dir=None,
|
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)
|
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strategy = GlobalPoolStrategy()
|
||
pools = strategy.build(questions, correctness, config)
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diag_ids = {q.question_id for q in pools.diagnosis}
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val_ids = {q.question_id for q in pools.validation}
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test_ids = {q.question_id for q in pools.test}
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assert diag_ids & val_ids == set()
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||
assert diag_ids & test_ids == set()
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assert val_ids & test_ids == set()
|
||
assert len(pools.diagnosis) == 30
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||
assert len(pools.validation) == 30
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assert len(pools.test) == 30
|
||
|
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def test_global_strategy_build_incremental_raises(self) -> None:
|
||
"""GlobalPoolStrategy 不支持增量。"""
|
||
strategy = GlobalPoolStrategy()
|
||
config = PoolConfig(
|
||
task_types=None,
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seed=0,
|
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baseline_run_id="r",
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||
diag_size=10,
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diag_correct_ratio=0.5,
|
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val_size=10,
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val_correct_ratio=0.5,
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test_size=10,
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eval_min_per_class=1,
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train_ratio=0.667,
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test_questions_dir=None,
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)
|
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with pytest.raises(NotImplementedError):
|
||
strategy.build_incremental(["Action Reasoning"], [], {}, config)
|
||
|
||
|
||
def _make_per_category_questions():
|
||
"""构造 12 类各 30 题，共 360 题。"""
|
||
task_types = [
|
||
"Action Prediction", "Action Reasoning", "Action Recognition",
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"Action Sequence", "Causal Reasoning", "Event Reasoning",
|
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"Object Interaction", "Object Reasoning", "Object Recognition",
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"Scene Understanding", "Spatial Reasoning", "Temporal Reasoning",
|
||
]
|
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questions = []
|
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for tt in task_types:
|
||
for i in range(30):
|
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questions.append(_make_question(f"{tt}_{i:03d}", tt))
|
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return questions
|
||
|
||
|
||
class TestPerCategoryPoolStrategy:
|
||
"""PerCategoryPoolStrategy per-category 2:1 分层划分。"""
|
||
|
||
def test_per_category_split_20_10(self):
|
||
"""每类 30 题按 correctness 2:1 分层 -> 20 train + 10 val。"""
|
||
questions = _make_per_category_questions()
|
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correctness = {}
|
||
for q in questions:
|
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idx = int(q.question_id.split("_")[-1])
|
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correctness[q.question_id] = idx < 18
|
||
|
||
config = PoolConfig(
|
||
task_types=None, seed=42, baseline_run_id="baseline_v2",
|
||
diag_size=0, diag_correct_ratio=0.0, val_size=0,
|
||
val_correct_ratio=0.0, test_size=0, eval_min_per_class=0,
|
||
train_ratio=20 / 30, test_questions_dir=None,
|
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)
|
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strategy = PerCategoryPoolStrategy()
|
||
pools = strategy.build(questions, correctness, config)
|
||
assert len(pools.diagnosis) == 240
|
||
assert len(pools.validation) == 120
|
||
|
||
from collections import Counter
|
||
diag_counts = Counter(q.task_type for q in pools.diagnosis)
|
||
val_counts = Counter(q.task_type for q in pools.validation)
|
||
for tt in diag_counts:
|
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assert diag_counts[tt] == 20
|
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assert val_counts[tt] == 10
|
||
|
||
diag_ids = {q.question_id for q in pools.diagnosis}
|
||
val_ids = {q.question_id for q in pools.validation}
|
||
assert diag_ids & val_ids == set()
|
||
|
||
def test_per_category_correctness_ratio_aligned(self):
|
||
"""train 和 val 的 correctness 比例应对齐。"""
|
||
questions = _make_per_category_questions()
|
||
correctness = {}
|
||
for q in questions:
|
||
idx = int(q.question_id.split("_")[-1])
|
||
correctness[q.question_id] = idx < 18
|
||
|
||
config = PoolConfig(
|
||
task_types=("Action Reasoning",), seed=42, baseline_run_id="baseline_v2",
|
||
diag_size=0, diag_correct_ratio=0.0, val_size=0,
|
||
val_correct_ratio=0.0, test_size=0, eval_min_per_class=0,
|
||
train_ratio=20 / 30, test_questions_dir=None,
|
||
)
|
||
strategy = PerCategoryPoolStrategy()
|
||
pools = strategy.build(questions, correctness, config)
|
||
assert len(pools.diagnosis) == 20
|
||
assert len(pools.validation) == 10
|
||
diag_correct = sum(1 for q in pools.diagnosis if correctness[q.question_id])
|
||
val_correct = sum(1 for q in pools.validation if correctness[q.question_id])
|
||
assert diag_correct == 12
|
||
assert val_correct == 6
|
||
|
||
def test_per_category_all_correct_degrades(self):
|
||
"""某类全部 correct -> 退化为非分层 random 20/10。"""
|
||
questions = [_make_question(f"q_{i:03d}", "Object Recognition") for i in range(30)]
|
||
correctness = {q.question_id: True for q in questions}
|
||
config = PoolConfig(
|
||
task_types=None, seed=42, baseline_run_id="r",
|
||
diag_size=0, diag_correct_ratio=0.0, val_size=0,
|
||
val_correct_ratio=0.0, test_size=0, eval_min_per_class=0,
|
||
train_ratio=20 / 30, test_questions_dir=None,
|
||
)
|
||
strategy = PerCategoryPoolStrategy()
|
||
pools = strategy.build(questions, correctness, config)
|
||
assert len(pools.diagnosis) == 20
|
||
assert len(pools.validation) == 10
|
||
|
||
def test_per_category_missing_correctness_fails(self):
|
||
"""correctness 不完整时 fail-fast。"""
|
||
questions = [_make_question(f"q_{i:03d}", "Object Recognition") for i in range(30)]
|
||
correctness = {q.question_id: True for q in questions[:25]}
|
||
config = PoolConfig(
|
||
task_types=None, seed=42, baseline_run_id="r",
|
||
diag_size=0, diag_correct_ratio=0.0, val_size=0,
|
||
val_correct_ratio=0.0, test_size=0, eval_min_per_class=0,
|
||
train_ratio=20 / 30, test_questions_dir=None,
|
||
)
|
||
strategy = PerCategoryPoolStrategy()
|
||
with pytest.raises(ValueError, match="correctness 缺失"):
|
||
strategy.build(questions, correctness, config)
|
||
|
||
def test_per_category_task_types_filter(self):
|
||
"""task_types 过滤只处理指定类别。"""
|
||
questions = _make_per_category_questions()
|
||
correctness = {q.question_id: True for q in questions}
|
||
config = PoolConfig(
|
||
task_types=("Action Reasoning", "Scene Understanding"), seed=42,
|
||
baseline_run_id="r", diag_size=0, diag_correct_ratio=0.0,
|
||
val_size=0, val_correct_ratio=0.0, test_size=0,
|
||
eval_min_per_class=0, train_ratio=20 / 30, test_questions_dir=None,
|
||
)
|
||
strategy = PerCategoryPoolStrategy()
|
||
pools = strategy.build(questions, correctness, config)
|
||
assert len(pools.diagnosis) == 40
|
||
assert len(pools.validation) == 20
|
||
types_in_diag = {q.task_type for q in pools.diagnosis}
|
||
assert types_in_diag == {"Action Reasoning", "Scene Understanding"}
|
||
|
||
|
||
class TestPerCategorySaveLoad:
|
||
"""per_category 格式的 pools.json 冻结/加载。"""
|
||
|
||
def test_save_load_per_category_roundtrip(self, tmp_path: Path) -> None:
|
||
"""per_category 模式 save -> load 往返一致。"""
|
||
questions = _make_per_category_questions()
|
||
correctness = {q.question_id: True for q in questions}
|
||
config = PoolConfig(
|
||
task_types=("Action Reasoning",), seed=42, baseline_run_id="baseline_v2",
|
||
diag_size=0, diag_correct_ratio=0.0, val_size=0, val_correct_ratio=0.0,
|
||
test_size=0, eval_min_per_class=0, train_ratio=20 / 30,
|
||
test_questions_dir=None,
|
||
)
|
||
strategy = PerCategoryPoolStrategy()
|
||
pools = strategy.build(questions, correctness, config)
|
||
pools_path = tmp_path / "pools.json"
|
||
save_pools(pools, pools_path, split_mode="per_category", config=config)
|
||
loaded = load_pools(pools_path)
|
||
assert loaded.baseline_run_id == pools.baseline_run_id
|
||
assert len(loaded.diagnosis) == len(pools.diagnosis)
|
||
assert len(loaded.validation) == len(pools.validation)
|
||
|
||
# 验证 per_category 格式内容
|
||
data = json.loads(pools_path.read_text())
|
||
assert data["split_mode"] == "per_category"
|
||
assert "categories" in data
|
||
assert data["seed"] == 42
|
||
assert data["train_ratio"] == pytest.approx(20 / 30)
|
||
assert data["test_source"] is None
|
||
|
||
# categories 内容校验
|
||
cats = data["categories"]
|
||
assert "Action Reasoning" in cats
|
||
assert len(cats["Action Reasoning"]["train"]) == 20
|
||
assert len(cats["Action Reasoning"]["val"]) == 10
|
||
|
||
def test_save_per_category_without_config_raises(self, tmp_path: Path) -> None:
|
||
"""per_category 模式未提供 config 时报 ValueError。"""
|
||
from app.harness.pools import Pools
|
||
|
||
pools = Pools(
|
||
diagnosis=[], validation=[], test=[],
|
||
baseline_run_id="r", baseline_val_accuracy=0.0,
|
||
)
|
||
with pytest.raises(ValueError, match="per_category 模式下.*必须提供 config"):
|
||
save_pools(pools, tmp_path / "pools.json", split_mode="per_category")
|
||
|
||
def test_save_global_mode_has_split_mode_field(self, tmp_path: Path) -> None:
|
||
"""global 模式 save 也写入 split_mode 字段。"""
|
||
questions = _make_question_set(60)
|
||
correctness = _make_correctness(questions, 0.5)
|
||
original = build_pools(
|
||
questions, correctness,
|
||
diag_cfg={"size": 10, "correct_ratio": 0.5, "task_types": None,
|
||
"seed": 42, "min_per_class": None},
|
||
val_cfg={"size": 10, "correct_ratio": 0.5, "task_types": None,
|
||
"seed": 42, "min_per_class": None},
|
||
test_cfg={"size": 10},
|
||
baseline_run_id="run_001",
|
||
)
|
||
pools_path = tmp_path / "pools.json"
|
||
save_pools(original, pools_path, split_mode="global")
|
||
data = json.loads(pools_path.read_text())
|
||
assert data["split_mode"] == "global"
|
||
assert "categories" not in data
|
||
|
||
# 仍能正常 load
|
||
loaded = load_pools(pools_path)
|
||
assert loaded.baseline_run_id == "run_001"
|
||
assert len(loaded.diagnosis) == 10
|
||
|
||
def test_load_legacy_format_without_split_mode(self, tmp_path: Path) -> None:
|
||
"""旧格式（无 split_mode 字段）仍可加载。"""
|
||
legacy = {
|
||
"baseline_run_id": "run_legacy",
|
||
"baseline_val_accuracy": 0.75,
|
||
"correctness": {"q1": True},
|
||
"diagnosis": [{
|
||
"question_id": "q1", "video_id": "v1", "task_type": "AR",
|
||
"question": "Q?", "options": ["A", "B", "C", "D"],
|
||
"answer": "A", "source_nodes": [], "difficulty": "medium",
|
||
"skill_target": None, "difficulty_steps": None,
|
||
}],
|
||
"validation": [],
|
||
"test": [],
|
||
}
|
||
pools_path = tmp_path / "pools.json"
|
||
pools_path.write_text(json.dumps(legacy), encoding="utf-8")
|
||
loaded = load_pools(pools_path)
|
||
assert loaded.baseline_run_id == "run_legacy"
|
||
assert len(loaded.diagnosis) == 1
|
||
|
||
def test_per_category_categories_multi_type(self, tmp_path: Path) -> None:
|
||
"""多类别 per_category save 后 categories 包含所有类别。"""
|
||
questions = _make_per_category_questions()
|
||
correctness = {q.question_id: True for q in questions}
|
||
config = PoolConfig(
|
||
task_types=("Action Reasoning", "Scene Understanding"), seed=0,
|
||
baseline_run_id="b", diag_size=0, diag_correct_ratio=0.0,
|
||
val_size=0, val_correct_ratio=0.0, test_size=0,
|
||
eval_min_per_class=0, train_ratio=20 / 30, test_questions_dir=None,
|
||
)
|
||
strategy = PerCategoryPoolStrategy()
|
||
pools = strategy.build(questions, correctness, config)
|
||
pools_path = tmp_path / "pools.json"
|
||
save_pools(pools, pools_path, split_mode="per_category", config=config)
|
||
|
||
data = json.loads(pools_path.read_text())
|
||
assert set(data["categories"].keys()) == {
|
||
"Action Reasoning", "Scene Understanding",
|
||
}
|
||
for tt in data["categories"]:
|
||
cat = data["categories"][tt]
|
||
assert len(cat["train"]) == 20
|
||
assert len(cat["val"]) == 10
|
||
# train + val 的 qid 互斥
|
||
assert set(cat["train"]) & set(cat["val"]) == set()
|