test(integration): add PerCategoryPoolStrategy end-to-end test

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

tests/integration/test_pool_strategy.py

@@ -0,0 +1,127 @@
+"""PerCategoryPoolStrategy 端到端集成测试。
+
+验证从构造题目 → 伪造 baseline → 池构建 → 冻结 → 加载的完整流程。
+"""
+
+from __future__ import annotations
+
+import json
+from collections import Counter
+from pathlib import Path
+
+import pytest
+
+from app.harness.pools import (
+    PerCategoryPoolStrategy,
+    load_pools,
+    save_pools,
+)
+from core.types import GeneratedQuestion, PoolConfig
+
+
+def _make_question(qid: str, task_type: str) -> GeneratedQuestion:
+    """构造测试用 GeneratedQuestion。"""
+    return GeneratedQuestion(
+        question_id=qid, video_id="v1", task_type=task_type,
+        question=f"Q {qid}?",
+        options=("A. a", "B. b", "C. c", "D. d"),
+        answer="A", source_nodes=("n1",), difficulty="medium",
+    )
+
+
+class TestPerCategoryE2E:
+    """端到端：构建 → 冻结 → 加载 → 校验。"""
+
+    def test_full_flow(self, tmp_path: Path) -> None:
+        """完整流程：12 类各 30 题 → 策略构建 → 冻结 → 加载 → 三池校验。"""
+        task_types = [
+            "Action Prediction", "Action Reasoning", "Action Recognition",
+            "Action Sequence", "Causal Reasoning", "Event Reasoning",
+            "Object Interaction", "Object Reasoning", "Object Recognition",
+            "Scene Understanding", "Spatial Reasoning", "Temporal Reasoning",
+        ]
+        questions = []
+        for tt in task_types:
+            for i in range(30):
+                questions.append(_make_question(f"{tt}_{i:03d}", tt))
+
+        # 每类前 18 correct，后 12 wrong
+        correctness = {}
+        for q in questions:
+            idx = int(q.question_id.split("_")[-1])
+            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,
+        )
+
+        strategy = PerCategoryPoolStrategy()
+        pools = strategy.build(questions, correctness, config)
+
+        # 验证总量
+        assert len(pools.diagnosis) == 240
+        assert len(pools.validation) == 120
+
+        # 验证逐类均匀
+        diag_by_type = Counter(q.task_type for q in pools.diagnosis)
+        val_by_type = Counter(q.task_type for q in pools.validation)
+        for tt in task_types:
+            assert diag_by_type[tt] == 20
+            assert val_by_type[tt] == 10
+
+        # 验证互斥
+        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()
+
+        # 验证 correctness 对齐
+        for tt in task_types:
+            tt_diag = [q for q in pools.diagnosis if q.task_type == tt]
+            tt_val = [q for q in pools.validation if q.task_type == tt]
+            diag_ratio = sum(1 for q in tt_diag if correctness[q.question_id]) / len(tt_diag)
+            val_ratio = sum(1 for q in tt_val if correctness[q.question_id]) / len(tt_val)
+            assert abs(diag_ratio - val_ratio) < 0.05, (
+                f"{tt}: train ratio {diag_ratio:.2f} vs val ratio {val_ratio:.2f}"
+            )
+
+        # 冻结 → 加载
+        pools_path = tmp_path / "pools.json"
+        save_pools(pools, pools_path, split_mode="per_category", config=config)
+        loaded = load_pools(pools_path)
+        assert len(loaded.diagnosis) == 240
+        assert len(loaded.validation) == 120
+
+        # 验证冻结格式
+        data = json.loads(pools_path.read_text())
+        assert data["split_mode"] == "per_category"
+        assert "categories" in data
+        assert len(data["categories"]) == 12
+
+    def test_single_category_flow(self, tmp_path: Path) -> None:
+        """单类别训练流程：30 题 → 20 train + 10 val。"""
+        questions = [_make_question(f"q_{i:03d}", "Object Recognition") for i in range(30)]
+        correctness = {q.question_id: (i < 20) for i, q in enumerate(questions)}
+
+        config = PoolConfig(
+            task_types=("Object Recognition",), seed=42, baseline_run_id="bl",
+            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
+
+        # 冻结 → 加载
+        pools_path = tmp_path / "pools.json"
+        save_pools(pools, pools_path, split_mode="per_category", config=config)
+        loaded = load_pools(pools_path)
+        assert len(loaded.diagnosis) == 20
+        assert len(loaded.validation) == 10