feat(models): 实现机器学习模型训练框架

- 添加核心抽象：Processor、Model、Splitter、Metric 基类
- 实现阶段感知机制（TRAIN/TEST/ALL），防止数据泄露
- 内置 8 个数据处理器和 3 种时序划分策略
- 支持 LightGBM、CatBoost 模型
- PluginRegistry 装饰器注册，插件式架构
- 22 个单元测试

src/models/pipeline.py

@@ -0,0 +1,70 @@
+"""数据处理流水线
+
+管理多个处理器的顺序执行，支持阶段感知处理。
+"""
+
+from typing import List, Dict
+import polars as pl
+
+from src.models.core import BaseProcessor, PipelineStage
+
+
+class ProcessingPipeline:
+    """数据处理流水线
+
+    按顺序执行多个处理器，自动处理阶段标记。
+    关键特性：在测试阶段使用训练阶段学习到的参数，防止数据泄露。
+    """
+
+    def __init__(self, processors: List[BaseProcessor]):
+        """初始化流水线
+
+        Args:
+            processors: 处理器列表（按执行顺序）
+        """
+        self.processors = processors
+        self._fitted_processors: Dict[int, BaseProcessor] = {}
+
+    def fit_transform(
+        self, data: pl.DataFrame, stage: PipelineStage = PipelineStage.TRAIN
+    ) -> pl.DataFrame:
+        """在训练数据上fit所有处理器并transform"""
+        result = data
+        for i, processor in enumerate(self.processors):
+            if processor.stage in [PipelineStage.ALL, stage]:
+                result = processor.fit_transform(result)
+                self._fitted_processors[i] = processor
+            elif stage == PipelineStage.TRAIN and processor.stage == PipelineStage.TEST:
+                processor.fit(result)
+                self._fitted_processors[i] = processor
+        return result
+
+    def transform(
+        self, data: pl.DataFrame, stage: PipelineStage = PipelineStage.TEST
+    ) -> pl.DataFrame:
+        """在测试数据上应用已fit的处理器"""
+        result = data
+        for i, processor in enumerate(self.processors):
+            if processor.stage in [PipelineStage.ALL, stage]:
+                if i in self._fitted_processors:
+                    result = self._fitted_processors[i].transform(result)
+                else:
+                    result = processor.transform(result)
+        return result
+
+    def save_processors(self, path: str) -> None:
+        """保存所有已fit的处理器状态"""
+        import pickle
+
+        with open(path, "wb") as f:
+            pickle.dump(self._fitted_processors, f)
+
+    def load_processors(self, path: str) -> None:
+        """加载处理器状态"""
+        import pickle
+
+        with open(path, "rb") as f:
+            self._fitted_processors = pickle.load(f)
+
+
+__all__ = ["ProcessingPipeline"]