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

tests/test_tabm_rank_model.py

@@ -0,0 +1,88 @@
+"""测试 TabMRankModel 基础功能"""
+
+import pytest
+import numpy as np
+import polars as pl
+import torch
+
+from src.training.components.models import TabMRankModel
+
+
+class TestTabMRankModel:
+    """TabMRankModel 测试类"""
+
+    def test_model_initialization(self):
+        """测试模型初始化"""
+        model = TabMRankModel(params={"ensemble_size": 16})
+        assert model.name == "tabm_rank"
+        assert model.params["ensemble_size"] == 16
+        assert model.device in [torch.device("cpu"), torch.device("cuda")]
+
+    def test_prepare_group_from_dates(self):
+        """测试从日期生成 group 数组"""
+        df = pl.DataFrame(
+            {
+                "trade_date": [
+                    "20240101",
+                    "20240101",
+                    "20240102",
+                    "20240102",
+                    "20240102",
+                ],
+                "value": [1, 2, 3, 4, 5],
+            }
+        )
+        group = TabMRankModel.prepare_group_from_dates(df)
+        assert np.array_equal(group, np.array([2, 3]))
+
+    def test_convert_to_quantile_labels(self):
+        """测试转换分位数标签"""
+        df = pl.DataFrame(
+            {
+                "trade_date": ["20240101"] * 5 + ["20240102"] * 5,
+                "return": [0.1, 0.05, 0.0, -0.05, -0.1] + [0.2, 0.1, 0.0, -0.1, -0.2],
+            }
+        )
+        result = TabMRankModel.convert_to_quantile_labels(df, "return", n_quantiles=5)
+        assert "return_rank" in result.columns
+        assert result["return_rank"].dtype == pl.Int64
+
+    def test_save_load(self, tmp_path):
+        """测试模型保存和加载（跳过实际训练）"""
+        params = {
+            "ensemble_size": 8,
+            "n_blocks": 2,
+            "d_block": 64,
+            "epochs": 1,
+        }
+        model = TabMRankModel(params=params)
+        model.feature_names_ = ["feat1", "feat2"]
+
+        # 模拟训练历史
+        model.training_history_["train_loss"] = [0.5, 0.4]
+        model.training_history_["val_ndcg"] = [0.3, 0.35]
+
+        # 测试保存前需要初始化模型
+        # 这里只测试元数据保存
+        save_path = tmp_path / "test_model"
+        import pickle
+
+        with open(save_path.with_suffix(".meta"), "wb") as f:
+            pickle.dump(
+                {
+                    "params": model.params,
+                    "feature_names": model.feature_names_,
+                    "training_history": model.training_history_,
+                    "device": str(model.device),
+                },
+                f,
+            )
+
+        # 测试加载元数据
+        loaded_model = TabMRankModel.load(save_path)
+        assert loaded_model.params == params
+        assert loaded_model.feature_names_ == ["feat1", "feat2"]
+
+
+if __name__ == "__main__":
+    pytest.main([__file__, "-v"])