chore: lint and format per-category pool strategy implementation

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

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 Sequence", "Causal Reasoning", "Event Reasoning",
-            "Object Interaction", "Object Reasoning", "Object Recognition",
-            "Scene Understanding", "Spatial Reasoning", "Temporal Reasoning",
+            "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:
@@ -52,11 +65,17 @@ class TestPerCategoryE2E:
             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,
+            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()
@@ -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",
-            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,
+            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()