feat(training): LightGBM支持验证集早停

- 为fit方法添加eval_set参数，支持验证集评估和早停

- 因子引擎简化初始化，移除metadata_path参数

- 回归实验精简因子定义，移除冗余因子库

src/training/components/models/lightgbm.py

@@ -49,12 +49,18 @@ class LightGBMModel(BaseModel):
         self.model = None
         self.feature_names_: Optional[list] = None
 
-    def fit(self, X: pl.DataFrame, y: pl.Series) -> "LightGBMModel":
+    def fit(
+        self,
+        X: pl.DataFrame,
+        y: pl.Series,
+        eval_set: Optional[tuple] = None,
+    ) -> "LightGBMModel":
         """训练模型
 
         Args:
             X: 特征矩阵 (Polars DataFrame)
             y: 目标变量 (Polars Series)
+            eval_set: 验证集元组 (X_val, y_val)，用于早停
 
         Returns:
             self (支持链式调用)
@@ -76,6 +82,14 @@ class LightGBMModel(BaseModel):
 
         train_data = lgb.Dataset(X_np, label=y_np)
 
+        # 准备验证集
+        valid_sets = None
+        if eval_set is not None:
+            X_val, y_val = eval_set
+            X_val_np = X_val.to_numpy()
+            y_val_np = y_val.to_numpy()
+            valid_sets = lgb.Dataset(X_val_np, label=y_val_np, reference=train_data)
+
         # 从 params 中提取 num_boost_round，默认 100
         num_boost_round = self.params.pop("n_estimators", 100)
 
@@ -83,6 +97,7 @@ class LightGBMModel(BaseModel):
             self.params,
             train_data,
             num_boost_round=num_boost_round,
+            valid_sets=[valid_sets] if valid_sets else None,
         )
 
         return self