feat: add selector_scores observation column to question_gen_items

tests/unit/test_run_store_selector_scores.py

@@ -0,0 +1,56 @@
+"""selector_scores 列幂等迁移 + update_selector_scores 写入。"""
+
+import json
+
+from app.question_gen.run_store import QuestionGenStore
+
+
+def _store(tmp_path):
+    return QuestionGenStore(tmp_path / "q.db")
+
+
+def test_selector_scores_column_exists(tmp_path):
+    store = _store(tmp_path)
+    cols = {r[1] for r in store._conn.execute("PRAGMA table_info(question_gen_items)")}
+    assert "selector_scores" in cols
+    store.close()
+
+
+def test_update_selector_scores_writes_json(tmp_path):
+    store = _store(tmp_path)
+    store.record_run_start("run1", "sha", "{}")
+    store.record_item(
+        item_id="it1",
+        run_id="run1",
+        slot_id="s1",
+        video_id="v1",
+        family="ACTION_RECOGNITION",
+        task_type="Action Recognition",
+        skill_target="M1_AR",
+        attempt=1,
+        question_text="?",
+        sub_pattern="temporal_reasoning_failure",
+    )
+    payload = {
+        "correct_score": 0.8,
+        "chosen": [0.7, 0.6, 0.55],
+        "pool_size": 24,
+        "anneal_rounds": 0,
+        "hard_fail": False,
+    }
+    store.update_selector_scores("it1", json.dumps(payload))
+    row = store._conn.execute(
+        "SELECT selector_scores FROM question_gen_items WHERE item_id='it1'"
+    ).fetchone()
+    assert json.loads(row[0])["correct_score"] == 0.8
+    store.close()
+
+
+def test_update_selector_scores_unknown_item_raises(tmp_path):
+    store = _store(tmp_path)
+    try:
+        store.update_selector_scores("missing", "{}")
+        raise AssertionError("应抛 ValueError")
+    except ValueError:
+        pass
+    store.close()