feat(training): 新增 TabM 模型支持及数据质量优化

- 添加 TabMModel、TabPFNModel 深度学习模型实现
- 新增 DataQualityAnalyzer 进行训练前数据质量诊断
- 改进数据处理器 NaN/null 双重处理，增强数据鲁棒性
- 支持 train_skip_days 参数跳过训练初期数据不足期
- Pipeline 自动清理标签为 NaN 的样本

tests/test_tabm_integration.py

@@ -0,0 +1,310 @@
+"""TabM 集成测试
+
+测试 TabMModel 和 TabMRegressionTask 的完整训练流程。
+"""
+
+import os
+import sys
+from pathlib import Path
+
+import numpy as np
+import polars as pl
+import pytest
+import torch
+
+# 确保 src 在路径中
+sys.path.insert(0, str(Path(__file__).parent.parent))
+
+from src.training.components.models import TabMModel
+from src.training.tasks import TabMRegressionTask
+
+
+# ==========================================
+# 测试数据准备
+# ==========================================
+
+
+def create_sample_data(n_samples: int = 1000, n_features: int = 20, seed: int = 42):
+    """创建样本数据用于测试
+
+    Args:
+        n_samples: 样本数量
+        n_features: 特征数量
+        seed: 随机种子
+
+    Returns:
+        (train_X, train_y, val_X, val_y, test_X, test_y)
+    """
+    np.random.seed(seed)
+    torch.manual_seed(seed)
+
+    # 创建特征矩阵
+    X_train = pl.DataFrame(
+        np.random.randn(n_samples, n_features).astype(np.float32),
+        schema=[f"feature_{i}" for i in range(n_features)],
+    )
+    y_train = pl.Series("target", np.random.randn(n_samples).astype(np.float32))
+
+    X_val = pl.DataFrame(
+        np.random.randn(n_samples // 2, n_features).astype(np.float32),
+        schema=[f"feature_{i}" for i in range(n_features)],
+    )
+    y_val = pl.Series("target", np.random.randn(n_samples // 2).astype(np.float32))
+
+    X_test = pl.DataFrame(
+        np.random.randn(n_samples // 2, n_features).astype(np.float32),
+        schema=[f"feature_{i}" for i in range(n_features)],
+    )
+    y_test = pl.Series("target", np.random.randn(n_samples // 2).astype(np.float32))
+
+    return X_train, y_train, X_val, y_val, X_test, y_test
+
+
+# ==========================================
+# TabMModel 测试
+# ==========================================
+
+
+class TestTabMModel:
+    """TabMModel 单元测试"""
+
+    def test_initialization(self):
+        """测试模型初始化"""
+        params = {
+            "n_blocks": 2,
+            "d_block": 128,
+            "ensemble_size": 8,  # 小规模集成用于测试
+            "batch_size": 64,
+            "epochs": 2,
+        }
+
+        model = TabMModel(params)
+
+        assert model.name == "tabm"
+        assert model.params == params
+        assert model.device.type in ["cuda", "cpu"]
+        assert model.model is None  # 未训练时为 None
+
+    def test_fit_and_predict(self):
+        """测试训练和预测"""
+        # 创建小规模数据
+        X_train, y_train, X_val, y_val, X_test, _ = create_sample_data(
+            n_samples=200, n_features=10, seed=42
+        )
+
+        params = {
+            "n_blocks": 1,
+            "d_block": 64,
+            "ensemble_size": 4,
+            "batch_size": 32,
+            "epochs": 2,
+            "early_stopping_patience": 10,
+        }
+
+        model = TabMModel(params)
+
+        # 训练
+        model.fit(X_train, y_train, eval_set=(X_val, y_val))
+
+        # 验证模型已训练
+        assert model.model is not None
+        assert len(model.training_history_["train_loss"]) > 0
+
+        # 预测
+        predictions = model.predict(X_test)
+
+        # 验证预测结果
+        assert isinstance(predictions, np.ndarray)
+        assert len(predictions) == len(X_test)
+        assert predictions.shape == (len(X_test),)
+
+    def test_save_and_load(self, tmp_path):
+        """测试模型保存和加载"""
+        # 创建数据
+        X_train, y_train, X_val, y_val, _, _ = create_sample_data(
+            n_samples=200, n_features=10, seed=42
+        )
+
+        params = {
+            "n_blocks": 1,
+            "d_block": 64,
+            "ensemble_size": 4,
+            "batch_size": 32,
+            "epochs": 2,
+        }
+
+        # 训练模型
+        model = TabMModel(params)
+        model.fit(X_train, y_train, eval_set=(X_val, y_val))
+
+        # 保存
+        save_path = tmp_path / "test_tabm_model"
+        model.save(str(save_path))
+
+        # 加载
+        loaded_model = TabMModel.load(str(save_path))
+
+        # 验证加载的模型
+        assert loaded_model.params == params
+        assert loaded_model.feature_names_ == model.feature_names_
+        assert loaded_model.model is not None
+
+        # 预测结果应该一致
+        pred1 = model.predict(X_val)
+        pred2 = loaded_model.predict(X_val)
+
+        np.testing.assert_allclose(pred1, pred2, rtol=1e-5)
+
+
+# ==========================================
+# TabMRegressionTask 测试
+# ==========================================
+
+
+class TestTabMRegressionTask:
+    """TabMRegressionTask 单元测试"""
+
+    def test_initialization(self):
+        """测试任务初始化"""
+        params = {
+            "n_blocks": 2,
+            "d_block": 128,
+            "ensemble_size": 8,
+            "batch_size": 64,
+            "epochs": 2,
+        }
+
+        task = TabMRegressionTask(model_params=params, label_name="target")
+
+        assert task.model_params == params
+        assert task.label_name == "target"
+        assert task.model is None
+
+    def test_prepare_labels(self):
+        """测试标签准备（回归任务不做转换）"""
+        params = {
+            "ensemble_size": 4,
+            "epochs": 2,
+        }
+
+        task = TabMRegressionTask(model_params=params, label_name="target")
+
+        # 创建测试数据
+        data = {
+            "train": {
+                "X": pl.DataFrame({"a": [1, 2, 3], "b": [4, 5, 6]}),
+                "y": pl.Series("target", [0.1, 0.2, 0.3]),
+            }
+        }
+
+        result = task.prepare_labels(data)
+
+        # 回归任务不做转换，数据应该保持不变
+        assert result == data
+
+    def test_fit_train_and_predict(self):
+        """测试完整训练和预测流程"""
+        # 创建数据
+        X_train, y_train, X_val, y_val, X_test, y_test = create_sample_data(
+            n_samples=300, n_features=10, seed=42
+        )
+
+        params = {
+            "n_blocks": 1,
+            "d_block": 64,
+            "ensemble_size": 4,
+            "batch_size": 32,
+            "epochs": 3,
+        }
+
+        task = TabMRegressionTask(model_params=params, label_name="target")
+
+        # 准备数据格式
+        train_data = {"X": X_train, "y": y_train}
+        val_data = {"X": X_val, "y": y_val}
+
+        # 训练
+        task.fit(train_data, val_data)
+
+        # 验证模型已训练
+        assert task.get_model() is not None
+
+        # 预测
+        predictions = task.predict({"X": X_test})
+
+        # 验证预测结果
+        assert len(predictions) == len(X_test)
+
+
+# ==========================================
+# 集成测试
+# ==========================================
+
+
+class TestTabMIntegration:
+    """TabM 集成测试"""
+
+    def test_full_workflow(self):
+        """测试完整工作流程"""
+        # 创建数据
+        X_train, y_train, X_val, y_val, X_test, y_test = create_sample_data(
+            n_samples=500, n_features=15, seed=42
+        )
+
+        params = {
+            "n_blocks": 2,
+            "d_block": 128,
+            "ensemble_size": 8,
+            "batch_size": 64,
+            "epochs": 5,
+        }
+
+        # 1. 创建 Task
+        task = TabMRegressionTask(model_params=params, label_name="target")
+
+        # 2. 准备数据
+        train_data = {"X": X_train, "y": y_train}
+        val_data = {"X": X_val, "y": y_val}
+
+        # 3. 训练
+        task.fit(train_data, val_data)
+
+        # 4. 验证训练历史
+        model = task.get_model()
+        assert len(model.training_history_["train_loss"]) > 0
+        assert len(model.training_history_["val_loss"]) > 0
+
+        # 5. 预测
+        predictions = task.predict({"X": X_test})
+
+        # 6. 验证预测质量
+        # 简单验证：预测值不应全为常数
+        assert np.std(predictions) > 1e-6, "预测值全为常数，可能是模型未正常训练"
+
+        # 验证预测值与真实值存在一定相关性
+        correlation = np.corrcoef(predictions, y_test.to_numpy())[0, 1]
+        # 注意：随机数据的相关性可能很低，这是正常的
+        print(f"预测与真实值相关系数: {correlation:.4f}")
+
+    def test_gpu_availability(self):
+        """测试 GPU 可用性"""
+        device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+        params = {
+            "ensemble_size": 2,
+            "epochs": 1,
+        }
+
+        model = TabMModel(params)
+
+        assert model.device == device
+        expected_type = "cuda" if torch.cuda.is_available() else "cpu"
+        assert model.device.type == expected_type
+
+
+# ==========================================
+# 运行测试
+# ==========================================
+
+if __name__ == "__main__":
+    pytest.main([__file__, "-v", "--tb=short"])