chore: lint and format per-category pool strategy implementation

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

app/harness/pools.py

@@ -254,9 +254,7 @@ def save_pools(
         ValueError: split_mode 为 "per_category" 但未提供 config。
     """
     if split_mode == "per_category" and config is None:
-        raise ValueError(
+        raise ValueError("per_category 模式下 save_pools 必须提供 config 参数以写入元数据。")
-            "per_category 模式下 save_pools 必须提供 config 参数以写入元数据。"
-        )
 
     data: dict = {
         "split_mode": split_mode,
@@ -285,9 +283,7 @@ def save_pools(
         data["categories"] = categories
         data["seed"] = config.seed
         data["train_ratio"] = config.train_ratio
-        data["test_source"] = (
+        data["test_source"] = str(config.test_questions_dir) if config.test_questions_dir else None
-            str(config.test_questions_dir) if config.test_questions_dir else None
-        )
 
     path.write_text(
         json.dumps(data, ensure_ascii=False, indent=2),
@@ -402,23 +398,17 @@ def _validate_per_category_consistency(
     """
     mismatches: list[str] = []
     if frozen_data.get("seed") != pool_config.seed:
-        mismatches.append(
+        mismatches.append(f"seed: 冻结={frozen_data.get('seed')}, 当前={pool_config.seed}")
-            f"seed: 冻结={frozen_data.get('seed')}, 当前={pool_config.seed}"
-        )
     if frozen_data.get("train_ratio") != pool_config.train_ratio:
         mismatches.append(
-            f"train_ratio: 冻结={frozen_data.get('train_ratio')}, "
+            f"train_ratio: 冻结={frozen_data.get('train_ratio')}, 当前={pool_config.train_ratio}"
-            f"当前={pool_config.train_ratio}"
         )
     if frozen_data.get("baseline_run_id") != baseline_run_id:
         mismatches.append(
-            f"baseline_run_id: 冻结={frozen_data.get('baseline_run_id')}, "
+            f"baseline_run_id: 冻结={frozen_data.get('baseline_run_id')}, 当前={baseline_run_id}"
-            f"当前={baseline_run_id}"
         )
     if frozen_data.get("split_mode") != "per_category":
-        mismatches.append(
+        mismatches.append(f"split_mode: 冻结={frozen_data.get('split_mode')}, 当前=per_category")
-            f"split_mode: 冻结={frozen_data.get('split_mode')}, 当前=per_category"
-        )
     if mismatches:
         raise ValueError(
             "per_category pools.json 与当前配置不一致:\n"
@@ -484,13 +474,13 @@ def build_or_load_pools(
                             "FROM predictions WHERE run_id=?",
                             (baseline_run_id,),
                         )
-                    correctness = {
+                    correctness = {r["question_id"]: r["prediction"] == r["answer"] for r in rows}
-                        r["question_id"]: r["prediction"] == r["answer"]
-                        for r in rows
-                    }
 
                     new_cats = strategy.build_incremental(
-                        sorted(new_types), questions, correctness, pool_config,
+                        sorted(new_types),
+                        questions,
+                        correctness,
+                        pool_config,
                     )
                     # 合并新类别到 categories
                     frozen_categories.update(new_cats)
@@ -525,7 +515,8 @@ def build_or_load_pools(
                     )
                     logger.info(
                         "per_category 增量追加 {} 个新类别: {}",
-                        len(new_types), sorted(new_types),
+                        len(new_types),
+                        sorted(new_types),
                     )
 
         return load_pools(pools_path)
@@ -596,7 +587,10 @@ class PerCategoryPoolStrategy:
 
         for task_type in sorted(groups.keys()):
             train, val = self._split_one_category(
-                groups[task_type], correctness, config.train_ratio, rng,
+                groups[task_type],
+                correctness,
+                config.train_ratio,
+                rng,
             )
             all_train.extend(train)
             all_val.extend(val)
@@ -609,9 +603,7 @@ class PerCategoryPoolStrategy:
             test = load_benchmark(config.test_questions_dir)
 
         # Phase 5: 计算 baseline_val_accuracy
-        val_correct = sum(
+        val_correct = sum(1 for q in all_val if correctness.get(q.question_id, False))
-            1 for q in all_val if correctness.get(q.question_id, False)
-        )
         baseline_val_accuracy = val_correct / len(all_val) if all_val else 0.0
 
         return Pools(
@@ -654,9 +646,7 @@ class PerCategoryPoolStrategy:
         # 校验 correctness 完整性
         missing = [q.question_id for q in questions if q.question_id not in correctness]
         if missing:
-            raise ValueError(
+            raise ValueError(f"correctness 缺失 {len(missing)} 题: {missing[:5]}")
-                f"correctness 缺失 {len(missing)} 题: {missing[:5]}"
-            )
 
         correct_qs = [q for q in questions if correctness[q.question_id]]
         wrong_qs = [q for q in questions if not correctness[q.question_id]]
@@ -667,7 +657,9 @@ class PerCategoryPoolStrategy:
             label = "全部正确" if n_correct == n_total else "全部错误"
             logger.warning(
                 "类别 {} {} ({} 题)，退化为非分层随机划分",
-                questions[0].task_type, label, n_total,
+                questions[0].task_type,
+                label,
+                n_total,
             )
             shuffled = list(questions)
             rng.shuffle(shuffled)
@@ -683,12 +675,8 @@ class PerCategoryPoolStrategy:
         train = correct_qs[:train_correct] + wrong_qs[:train_wrong]
         val = correct_qs[train_correct:] + wrong_qs[train_wrong:]
 
-        assert len(train) == n_train, (
+        assert len(train) == n_train, f"train 数量不匹配: {len(train)} != {n_train}"
-            f"train 数量不匹配: {len(train)} != {n_train}"
+        assert len(val) == n_val, f"val 数量不匹配: {len(val)} != {n_val}"
-        )
-        assert len(val) == n_val, (
-            f"val 数量不匹配: {len(val)} != {n_val}"
-        )
 
         return train, val
 
@@ -721,7 +709,10 @@ class PerCategoryPoolStrategy:
 
         for task_type in sorted(groups.keys()):
             train, val = self._split_one_category(
-                groups[task_type], correctness, config.train_ratio, rng,
+                groups[task_type],
+                correctness,
+                config.train_ratio,
+                rng,
             )
             result[task_type] = {
                 "train": [q.question_id for q in train],

tests/integration/test_pool_strategy.py

@@ -7,9 +7,10 @@ from __future__ import annotations
 
 import json
 from collections import Counter
-from pathlib import Path
+from typing import TYPE_CHECKING
 
-import pytest
+if TYPE_CHECKING:
+    from pathlib import Path
 
 from app.harness.pools import (
     PerCategoryPoolStrategy,
@@ -22,10 +23,14 @@ 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_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",
+        answer="A",
+        source_nodes=("n1",),
+        difficulty="medium",
     )
 
 
@@ -35,10 +40,18 @@ class TestPerCategoryE2E:
     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 Prediction",
-            "Action Sequence", "Causal Reasoning", "Event Reasoning",
+            "Action Reasoning",
-            "Object Interaction", "Object Reasoning", "Object Recognition",
+            "Action Recognition",
-            "Scene Understanding", "Spatial Reasoning", "Temporal Reasoning",
+            "Action Sequence",
+            "Causal Reasoning",
+            "Event Reasoning",
+            "Object Interaction",
+            "Object Reasoning",
+            "Object Recognition",
+            "Scene Understanding",
+            "Spatial Reasoning",
+            "Temporal Reasoning",
         ]
         questions = []
         for tt in task_types:
@@ -52,11 +65,17 @@ class TestPerCategoryE2E:
             correctness[q.question_id] = idx < 18
 
         config = PoolConfig(
-            task_types=None, seed=42, baseline_run_id="baseline_v2",
+            task_types=None,
-            diag_size=0, diag_correct_ratio=0.0,
+            seed=42,
-            val_size=0, val_correct_ratio=0.0,
+            baseline_run_id="baseline_v2",
-            test_size=0, eval_min_per_class=0,
+            diag_size=0,
-            train_ratio=20 / 30, test_questions_dir=None,
+            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()
@@ -107,11 +126,17 @@ class TestPerCategoryE2E:
         correctness = {q.question_id: (i < 20) for i, q in enumerate(questions)}
 
         config = PoolConfig(
-            task_types=("Object Recognition",), seed=42, baseline_run_id="bl",
+            task_types=("Object Recognition",),
-            diag_size=0, diag_correct_ratio=0.0,
+            seed=42,
-            val_size=0, val_correct_ratio=0.0,
+            baseline_run_id="bl",
-            test_size=0, eval_min_per_class=0,
+            diag_size=0,
-            train_ratio=20 / 30, test_questions_dir=None,
+            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()