feat(training): 实现 LightGBM 模型

- 新增 LightGBMModel：LightGBM 回归模型实现
- 支持自定义参数（objective, num_leaves, learning_rate, n_estimators 等）
- 使用 LightGBM 原生格式保存/加载模型（不依赖 pickle）
- 支持特征重要性提取
- 已注册到 ModelRegistry（@register_model("lightgbm")）

tests/training/test_lightgbm_model.py

@@ -0,0 +1,226 @@
+"""测试 LightGBM 模型
+
+验证 LightGBMModel 的训练、预测、保存和加载功能。
+"""
+
+import os
+import tempfile
+
+import numpy as np
+import polars as pl
+import pytest
+
+from src.training.components.models.lightgbm import LightGBMModel
+
+
+class TestLightGBMModel:
+    """LightGBMModel 测试类"""
+
+    def test_init_default(self):
+        """测试默认初始化"""
+        model = LightGBMModel()
+        assert model.name == "lightgbm"
+        assert model.params["objective"] == "regression"
+        assert model.params["metric"] == "rmse"
+        assert model.params["num_leaves"] == 31
+        assert model.params["learning_rate"] == 0.05
+        assert model.n_estimators == 100
+        assert model.model is None
+
+    def test_init_custom(self):
+        """测试自定义参数"""
+        model = LightGBMModel(
+            objective="huber",
+            metric="mae",
+            num_leaves=50,
+            learning_rate=0.1,
+            n_estimators=200,
+        )
+        assert model.params["objective"] == "huber"
+        assert model.params["metric"] == "mae"
+        assert model.params["num_leaves"] == 50
+        assert model.params["learning_rate"] == 0.1
+        assert model.n_estimators == 200
+
+    def test_fit_success(self):
+        """测试正常训练"""
+        # 创建简单回归数据
+        X = pl.DataFrame(
+            {
+                "feature1": [1.0, 2.0, 3.0, 4.0, 5.0],
+                "feature2": [2.0, 4.0, 6.0, 8.0, 10.0],
+            }
+        )
+        y = pl.Series("target", [1.5, 3.0, 4.5, 6.0, 7.5])
+
+        model = LightGBMModel(n_estimators=10)
+        result = model.fit(X, y)
+
+        # 验证返回 self（支持链式调用）
+        assert result is model
+        # 验证模型已训练
+        assert model.model is not None
+        # 验证特征名称已保存
+        assert model.feature_names_ == ["feature1", "feature2"]
+
+    def test_predict_before_fit(self):
+        """测试未训练就预测"""
+        X = pl.DataFrame(
+            {
+                "feature1": [1.0, 2.0],
+                "feature2": [2.0, 4.0],
+            }
+        )
+        model = LightGBMModel()
+
+        with pytest.raises(RuntimeError, match="模型尚未训练"):
+            model.predict(X)
+
+    def test_predict_success(self):
+        """测试正常预测"""
+        # 创建回归数据
+        np.random.seed(42)
+        n_samples = 100
+        X_train = pl.DataFrame(
+            {
+                "feature1": np.random.randn(n_samples),
+                "feature2": np.random.randn(n_samples),
+            }
+        )
+        # y = 2*feature1 + 3*feature2 + noise
+        y_train = pl.Series(
+            "target",
+            2 * X_train["feature1"]
+            + 3 * X_train["feature2"]
+            + np.random.randn(n_samples) * 0.1,
+        )
+
+        model = LightGBMModel(n_estimators=20, learning_rate=0.1)
+        model.fit(X_train, y_train)
+
+        # 预测新数据（使用明显不同的值）
+        X_test = pl.DataFrame(
+            {
+                "feature1": [-2.0, 3.0],
+                "feature2": [-1.0, 4.0],
+            }
+        )
+        predictions = model.predict(X_test)
+
+        # 验证预测结果格式
+        assert isinstance(predictions, np.ndarray)
+        assert len(predictions) == 2
+        # 验证预测值是数值
+        assert all(np.isfinite(predictions))
+        # 验证单调性（第二个样本的 feature 值更大，预测值也应更大）
+        assert predictions[1] > predictions[0]
+
+    def test_feature_importance_before_fit(self):
+        """测试未训练就获取特征重要性"""
+        model = LightGBMModel()
+        assert model.feature_importance() is None
+
+    def test_feature_importance_after_fit(self):
+        """测试训练后获取特征重要性"""
+        X = pl.DataFrame(
+            {
+                "feature1": np.random.randn(100),
+                "feature2": np.random.randn(100),
+            }
+        )
+        y = pl.Series("target", X["feature1"] * 2 + X["feature2"] * 0.1)
+
+        model = LightGBMModel(n_estimators=10)
+        model.fit(X, y)
+
+        importance = model.feature_importance()
+
+        # 验证特征重要性格式
+        assert importance is not None
+        assert len(importance) == 2
+        assert "feature1" in importance.index
+        assert "feature2" in importance.index
+        # feature1 的系数更大，重要性应该更高
+        assert importance["feature1"] >= importance["feature2"]
+
+    def test_save_before_fit(self):
+        """测试未训练就保存"""
+        model = LightGBMModel()
+
+        with pytest.raises(RuntimeError, match="模型尚未训练"):
+            model.save("dummy.txt")
+
+    def test_save_and_load(self):
+        """测试保存和加载"""
+        # 训练模型
+        X = pl.DataFrame(
+            {
+                "feature1": [1.0, 2.0, 3.0, 4.0, 5.0],
+                "feature2": [2.0, 4.0, 6.0, 8.0, 10.0],
+            }
+        )
+        y = pl.Series("target", [2.0, 4.0, 6.0, 8.0, 10.0])
+
+        model = LightGBMModel(n_estimators=10, learning_rate=0.1)
+        model.fit(X, y)
+
+        # 保存前预测
+        X_test = pl.DataFrame(
+            {
+                "feature1": [6.0],
+                "feature2": [12.0],
+            }
+        )
+        pred_before = model.predict(X_test)
+
+        # 保存到临时文件
+        with tempfile.TemporaryDirectory() as tmpdir:
+            save_path = os.path.join(tmpdir, "model.txt")
+            model.save(save_path)
+
+            # 加载模型
+            loaded_model = LightGBMModel.load(save_path)
+
+            # 验证加载后预测结果相同
+            pred_after = loaded_model.predict(X_test)
+            assert pred_after[0] == pytest.approx(pred_before[0], rel=1e-5)
+
+            # 验证元数据已恢复
+            assert loaded_model.feature_names_ == ["feature1", "feature2"]
+            assert loaded_model.n_estimators == 10
+
+    def test_registration(self):
+        """测试模型已注册到 registry"""
+        from src.training.registry import ModelRegistry
+
+        model_class = ModelRegistry.get_model("lightgbm")
+        assert model_class is LightGBMModel
+
+    def test_fit_predict_consistency(self):
+        """测试多次预测结果一致"""
+        X = pl.DataFrame(
+            {
+                "feature1": np.random.randn(50),
+                "feature2": np.random.randn(50),
+            }
+        )
+        y = pl.Series("target", X["feature1"] + X["feature2"])
+
+        model = LightGBMModel(n_estimators=10)
+        model.fit(X, y)
+
+        X_test = pl.DataFrame(
+            {
+                "feature1": [1.0, 2.0, 3.0],
+                "feature2": [1.0, 2.0, 3.0],
+            }
+        )
+
+        # 多次预测应该返回相同结果
+        pred1 = model.predict(X_test)
+        pred2 = model.predict(X_test)
+        np.testing.assert_array_almost_equal(pred1, pred2)
+
+
+if __name__ == "__main__":
+    pytest.main([__file__, "-v"])