feat(training): 实现 Trainer 模块化重构 (Trainer V2)

- 新增 FactorManager 组件：统一管理多种来源因子
- 新增 DataPipeline 组件：完整数据处理流程（注册、过滤、划分、预处理）
- 新增 Task 策略组件：BaseTask 抽象基类、RegressionTask、RankTask
- 新增 ResultAnalyzer 组件：特征重要性分析和结果组装
- 新增 TrainerV2：作为纯调度引擎协调各组件
- 支持回归和排序学习两种训练模式
- 采用组合模式解耦训练流程，消除代码重复

src/training/components/models/lightgbm_lambdarank.py

@@ -185,131 +185,6 @@ class LightGBMLambdaRankModel(BaseModel):
             return None
         return self.model.best_score
 
-    def plot_metric(
-        self,
-        metric: Optional[str] = None,
-        figsize: tuple = (10, 6),
-        title: Optional[str] = None,
-        ax=None,
-    ):
-        """绘制训练指标曲线
-
-        Args:
-            metric: 要绘制的指标名称，如 'ndcg@5'
-            figsize: 图大小，默认 (10, 6)
-            title: 图表标题
-            ax: matplotlib Axes 对象
-
-        Returns:
-            matplotlib Axes 对象
-        """
-        if self.model is None:
-            raise RuntimeError("模型尚未训练，请先调用 fit()")
-
-        if self.evals_result_ is None or not self.evals_result_:
-            raise RuntimeError("没有可用的评估结果")
-
-        import lightgbm as lgb
-        import matplotlib.pyplot as plt
-
-        if metric is None:
-            available_metrics = list(self.evals_result_.get("train", {}).keys())
-            ndcg_metrics = [m for m in available_metrics if "ndcg" in m.lower()]
-            if ndcg_metrics:
-                metric = ndcg_metrics[0]
-            elif available_metrics:
-                metric = available_metrics[0]
-            else:
-                raise ValueError("没有可用的评估指标")
-
-        if metric not in self.evals_result_.get("train", {}):
-            available = list(self.evals_result_.get("train", {}).keys())
-            raise ValueError(f"指标 '{metric}' 不存在。可用的指标: {available}")
-
-        if ax is None:
-            _, ax = plt.subplots(figsize=figsize)
-
-        lgb.plot_metric(self.evals_result_, metric=metric, ax=ax)
-
-        if title is None:
-            assert metric is not None
-            title = f"Training Metric ({metric.upper()}) over Iterations"
-        ax.set_title(title, fontsize=12, fontweight="bold")
-
-        return ax
-
-    def plot_all_metrics(
-        self,
-        metrics: Optional[list] = None,
-        figsize: tuple = (14, 10),
-        max_cols: int = 2,
-    ):
-        """绘制所有训练指标曲线
-
-        Args:
-            metrics: 要绘制的指标列表
-            figsize: 图大小，默认 (14, 10)
-            max_cols: 每行最多显示的子图数，默认 2
-
-        Returns:
-            matplotlib Figure 对象
-        """
-        if self.model is None:
-            raise RuntimeError("模型尚未训练，请先调用 fit()")
-
-        if self.evals_result_ is None or not self.evals_result_:
-            raise RuntimeError("没有可用的评估结果")
-
-        import lightgbm as lgb
-        import matplotlib.pyplot as plt
-
-        available_metrics = list(self.evals_result_.get("train", {}).keys())
-
-        if metrics is None:
-            ndcg_metrics = [m for m in available_metrics if "ndcg" in m.lower()]
-            metrics = ndcg_metrics[:4] if ndcg_metrics else available_metrics[:4]
-
-        if not metrics:
-            raise ValueError("没有可用的评估指标")
-
-        n_metrics = len(metrics)
-        n_cols = min(max_cols, n_metrics)
-        n_rows = (n_metrics + n_cols - 1) // n_cols
-
-        fig, axes = plt.subplots(n_rows, n_cols, figsize=figsize)
-        if n_metrics == 1:
-            axes = [axes]
-        else:
-            axes = (
-                axes.flatten()
-                if n_rows > 1
-                else [axes]
-                if n_cols == 1
-                else axes.flatten()
-            )
-
-        for idx, metric in enumerate(metrics):
-            if idx < len(axes):
-                ax = axes[idx]
-                if metric in available_metrics:
-                    self.plot_metric(metric=metric, ax=ax)
-                    ax.set_title(f"{metric.upper()}", fontsize=11, fontweight="bold")
-                else:
-                    ax.text(
-                        0.5,
-                        0.5,
-                        f"Metric '{metric}' not found",
-                        ha="center",
-                        va="center",
-                        transform=ax.transAxes,
-                    )
-
-        for idx in range(n_metrics, len(axes)):
-            axes[idx].axis("off")
-
-        plt.tight_layout()
-        return fig
-
     def feature_importance(self) -> Optional[pd.Series]:
         """返回特征重要性