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"
 
 ]
 

src/experiment/regression.py

@@ -52,36 +52,22 @@ TRAINING_TYPE = "regression"
 
 # 排除的因子列表
 EXCLUDED_FACTORS = [
-    # 'GTJA_alpha016',
-    # 'volatility_20',
-    # 'current_ratio',
-    # 'GTJA_alpha001',
-    # 'GTJA_alpha141',
-    # 'GTJA_alpha129',
-    # 'GTJA_alpha164',
-    # 'amivest_liq_20',
-    # 'GTJA_alpha012',
-    # 'debt_to_equity',
-    # 'turnover_deviation',
-    # 'GTJA_alpha073',
-    # 'GTJA_alpha043',
-    # 'GTJA_alpha032',
-    # 'GTJA_alpha028',
-    # 'GTJA_alpha090',
-    # 'GTJA_alpha108',
-    # 'GTJA_alpha105',
-    # 'GTJA_alpha091',
-    # 'GTJA_alpha119',
-    # 'GTJA_alpha104',
-    # 'GTJA_alpha163',
-    # 'GTJA_alpha157',
-    # 'cost_skewness',
-    # 'GTJA_alpha176',
-    # 'chip_transition',
-    # 'amount_skewness_20',
-    # 'GTJA_alpha148',
-    # 'mean_median_dev',
-    # 'downside_illiq_20',
+    # "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"
 ]
 
 # 模型参数配置

src/experiment/tabm_rank_train.py

@@ -46,12 +46,16 @@ TRAINING_TYPE = "tabm_rank"
 # Label 配置（从 common.py 统一导入）
 # LABEL_NAME 和 LABEL_FACTOR 已在 common.py 中绑定，只需从 common 导入
 
-# 分位数配置（提高分辨率以更好地区分头部）
+# 分位数配置（分桶模式下使用；Rank-Gauss 模式下不使用，但保留兼容性）
 N_QUANTILES = 50
 
-# 【Top-K 优化】标签工程配置 - 默认启用平方增益
-LABEL_TRANSFORM = "exponential"  # 启用平方增益标签 (rank^2)
-LABEL_SCALE = 20.0  # 保留参数（当前未使用，平方变换不需要缩放）
+# 标签工程配置
+# 可选值:
+#   - "rank_gauss": Rank-Gauss 连续化标签（推荐，神经网络更友好）
+#   - "exponential": 指数化增益标签 (rank^2)
+#   - None: 标准分位数标签 (0, 1, ..., n_quantiles-1)
+LABEL_TRANSFORM = "rank_gauss"
+LABEL_SCALE = 20.0  # 保留参数（rank_gauss / exponential 下均未使用）
 
 # 排除的因子列表
 EXCLUDED_FACTORS = ["GTJA_alpha041", "GTJA_alpha127"]

src/experiment/tabm_setrank_train.py

@@ -61,7 +61,7 @@ MODEL_PARAMS = {
     # ==================== MLP 结构 ====================
     "n_blocks": 3,
     "d_block": 256,
-    "dropout": 0.5,
+    "dropout": 0.3,
 
     # ==================== 集成机制 ====================
     "ensemble_size": 32,
@@ -71,9 +71,9 @@ MODEL_PARAMS = {
     "setrank_heads": 4,
     # 【优化1】将隐藏维度从 128 降到 64。
     # 截面特征对比不需要那么宽的维度，太宽会导致模型记忆当天特有的无效噪音。
-    "setrank_hidden": 128,
+    "setrank_hidden": 256,
     # 【优化2】增大 SetRank 层的 Dropout
-    "setrank_dropout": 0.5,
+    "setrank_dropout": 0.3,
 
     # ==================== AMP 与显存优化 ====================
     "use_amp": True,
@@ -85,7 +85,7 @@ MODEL_PARAMS = {
     "learning_rate": 5e-4,
     # 【优化4】核心操作！将 L2 惩罚（权重衰减）放大 10 倍甚至 100 倍！
     # 带有 Attention 的网络极容易对某些特定股票产生依赖，强烈的 Weight Decay 能逼迫模型关注全局特征。
-    "weight_decay": 1e-5,  # 原为 1e-5，现改为 1e-3
+    "weight_decay": 1e-4,  # 原为 1e-5，现改为 1e-3
 
     "epochs": 150,  # 不需要 500 次，从图中看 150 绝对够了