feat(training): TabM 排序模型架构优化与 Rank-Gauss 标签工程

- TabMSetRank: 将 TabM 输出改为隐藏层特征，经 SetRankHead 交互后通过 final_mlp 输出 Ensemble 排序分
- SetRankHead 引入可学习残差缩放因子（Zero-init）与 Pre-Norm 结构，提升训练稳定性
- TabMRankTask 新增 Rank-Gauss 连续标签变换，支持标准分位数/指数增益/Rank-Gauss 三种标签模式
- 修复 NDCG 评估在负值标签下的计算问题
- 调整实验脚本超参数（dropout、hidden dim、weight decay）及排除因子列表
- 迁移废弃的 torch.cuda.amp 到 torch.amp，并将数据预加载至 GPU 减少循环拷贝

src/experiment/learn_to_rank.py

@@ -54,9 +54,22 @@ N_QUANTILES = 20
 
 # 排除的因子列表
 EXCLUDED_FACTORS = [
-    # 'debt_to_equity',
-    # 'GTJA_alpha016',
-    # 'GTJA_alpha141',
+    "amivest_liq_20",
+    "atr_price_impact",
+    "hui_heubel_ratio",
+    "corwin_schultz_spread_20",
+    "roll_spread_20",
+    "gibbs_effective_spread",
+    "overnight_illiq_20",
+    "illiq_volatility_20",
+    "amount_cv_20",
+    "amount_skewness_20",
+    "low_vol_days_20",
+    "liquidity_shock_momentum",
+    "downside_illiq_20",
+    "upside_illiq_20",
+    "illiq_asymmetry_20",
+    "pastor_stambaugh_proxy"
 
 ]