feat(training): 新增 TabM 排序学习模型支持并优化训练流程

- 新增 TabMRankModel、TabMRankTask 及配套损失函数与配置
- 将 DataQualityAnalyzer 从 experiment 迁移至 training 模块
- 调整数据处理器移除过度的 NaN/null 硬填充逻辑
- 优化 RankTask 评估指标使用分位数标签替代原始收益率
- 更新实验脚本处理器顺序与模型超参数配置

src/training/components/models/__init__.py

@@ -7,6 +7,11 @@ from src.training.components.models.lightgbm import LightGBMModel
 from src.training.components.models.lightgbm_lambdarank import LightGBMLambdaRankModel
 from src.training.components.models.tabpfn_model import TabPFNModel
 from src.training.components.models.tabm_model import TabMModel
+from src.training.components.models.tabm_rank_model import (
+    TabMRankModel,
+    EnsembleListNetLoss,
+    EnsembleLambdaLoss,
+)
 from src.training.components.models.cross_section_sampler import CrossSectionSampler
 from src.training.components.models.ensemble_quant_loss import EnsembleQuantLoss
 
@@ -15,6 +20,9 @@ __all__ = [
     "LightGBMLambdaRankModel",
     "TabPFNModel",
     "TabMModel",
+    "TabMRankModel",
+    "EnsembleListNetLoss",
+    "EnsembleLambdaLoss",
     "CrossSectionSampler",
     "EnsembleQuantLoss",
 ]