feat(experiment): 添加模型保存功能及因子信息持久化

- 新增 SAVE_MODEL 配置控制是否保存模型
- 新增 get_model_save_path() 生成模型保存路径
- 新增 save_model_with_factors() 保存模型及关联因子信息
- 新增 load_model_factors() 加载因子信息用于模型复现
- 更新训练脚本使用新的模型保存方式
- 清理 data/sync.py 中的废弃代码

src/experiment/learn_to_rank.py

@@ -32,6 +32,7 @@ from src.training import (
     NullFiller,
     StandardScaler,
     check_data_quality,
+    CrossSectionalStandardScaler,
 )
 from src.training.components.models import LightGBMLambdaRankModel
 from src.training.config import TrainingConfig
@@ -53,10 +54,15 @@ from src.experiment.common import (
     STOCK_FILTER_REQUIRED_COLUMNS,
     OUTPUT_DIR,
     SAVE_PREDICTIONS,
-    PERSIST_MODEL,
+    SAVE_MODEL,
+    get_model_save_path,
+    save_model_with_factors,
     TOP_N,
 )
 
+# 训练类型标识
+TRAINING_TYPE = "rank"
+
 
 # %% md
 # ## 2. 本地辅助函数
@@ -219,21 +225,23 @@ N_QUANTILES = 20  # 将 label 分为 20 组
 MODEL_PARAMS = {
     "objective": "lambdarank",
     "metric": "ndcg",
-    "ndcg_at": 10,  # 评估 NDCG@k
-    "learning_rate": 0.01,
-    "num_leaves": 31,
-    "max_depth": 4,
-    "min_data_in_leaf": 20,
-    "n_estimators": 2000,
-    "early_stopping_round": 100,
+    "ndcg_at": 15,
+    "learning_rate": 0.002,
+    "num_leaves": 63,
+    "max_depth": 5,
+    "min_data_in_leaf": 63,
+    "n_estimators": 1000,
+    "early_stopping_round": 150,
     "subsample": 0.8,
     "colsample_bytree": 0.8,
-    "reg_alpha": 0.1,
+    "reg_alpha": 0.5,
     "reg_lambda": 1.0,
     "verbose": -1,
     "random_state": 42,
-    "lambdarank_truncation_level": 10,
-    "label_gain": [i for i in range(1, N_QUANTILES + 1)],
+    "lambdarank_truncation_level": 30,
+    "label_gain": [
+        i for i in range(1, N_QUANTILES + 1)
+    ],  # 如果收益率被分为了比如 5 档，建议用[0, 1, 3, 7, 15] 这种指数型 gain
 }
 
 # 注意：stock_pool_filter, STOCK_FILTER_REQUIRED_COLUMNS, OUTPUT_DIR 等配置
@@ -287,7 +295,7 @@ model = LightGBMLambdaRankModel(params=MODEL_PARAMS)
 processors = [
     NullFiller(feature_cols=feature_cols, strategy="mean"),
     Winsorizer(feature_cols=feature_cols, lower=0.01, upper=0.99),
-    StandardScaler(feature_cols=feature_cols),
+    CrossSectionalStandardScaler(feature_cols=feature_cols),
 ]
 
 # 8. 创建数据划分器
@@ -310,7 +318,7 @@ pool_manager = StockPoolManager(
 # 10. 创建 ST 过滤器
 st_filter = STFilter(data_router=engine.router)
 
-# 11. 创建训练器
+# 11. 创建训练器（禁用自动保存，我们将在训练后手动保存以包含因子信息）
 trainer = Trainer(
     model=model,
     pool_manager=pool_manager,
@@ -319,7 +327,7 @@ trainer = Trainer(
     splitter=splitter,
     target_col=target_col,
     feature_cols=feature_cols,
-    persist_model=PERSIST_MODEL,
+    persist_model=False,  # 禁用自动保存，手动保存以包含因子信息
 )
 # %% md
 # ### 4.1 股票池筛选
@@ -582,6 +590,20 @@ print(
 print(f"\n  预览（前15行）:")
 print(topn_to_save.head(15))
 
+# 保存模型和因子信息（如果启用）
+if SAVE_MODEL:
+    print("\n" + "=" * 80)
+    print("保存模型和因子信息")
+    print("=" * 80)
+    model_save_path = get_model_save_path(TRAINING_TYPE)
+    if model_save_path:
+        save_model_with_factors(
+            model=model,
+            model_path=model_save_path,
+            selected_factors=SELECTED_FACTORS,
+            factor_definitions=FACTOR_DEFINITIONS,
+        )
+
 print("\n训练流程完成！")
 # %% md
 # ## 5. 总结