feat(training): 添加训练模块基础架构

实现 Commit 1：训练模块基础架构

新增文件：

- src/training/__init__.py - 主模块导出

- src/training/components/__init__.py - components 子模块导出

- src/training/components/base.py - BaseModel/BaseProcessor 抽象基类

- src/training/registry.py - 模型和处理器注册中心

- tests/training/test_base.py - 基础架构单元测试

功能特性：

- BaseModel: 提供 fit, predict, feature_importance, save/load 接口

- BaseProcessor: 提供 fit, transform, fit_transform 接口

- ModelRegistry/ProcessorRegistry: 支持装饰器风格组件注册

- 支持即插即用的组件扩展机制

Ultraworked with [Sisyphus](https://github.com/code-yeongyu/oh-my-opencode)

Co-authored-by: Sisyphus <clio-agent@sisyphuslabs.ai>

src/training/registry.py

@@ -0,0 +1,184 @@
+"""组件注册中心
+
+提供装饰器风格的组件注册机制，支持即插即用。
+"""
+
+from typing import Dict, Type, Callable, Any
+from src.training.components.base import BaseModel, BaseProcessor
+
+
+class ModelRegistry:
+    """模型注册中心
+
+    管理所有可用的模型类，支持通过名称获取模型类。
+
+    Example:
+        >>> @register_model("lightgbm")
+        ... class LightGBMModel(BaseModel):
+        ...     pass
+        >>>
+        >>> model_class = ModelRegistry.get_model("lightgbm")
+        >>> model = model_class(**params)
+    """
+
+    _registry: Dict[str, Type[BaseModel]] = {}
+
+    @classmethod
+    def register(cls, name: str, model_class: Type[BaseModel]) -> None:
+        """注册模型类
+
+        Args:
+            name: 模型名称
+            model_class: 模型类（必须继承 BaseModel）
+
+        Raises:
+            ValueError: 名称已被注册或类不继承 BaseModel
+        """
+        if name in cls._registry:
+            raise ValueError(f"模型 '{name}' 已被注册")
+        if not issubclass(model_class, BaseModel):
+            raise ValueError(f"模型类必须继承 BaseModel")
+        cls._registry[name] = model_class
+
+    @classmethod
+    def get_model(cls, name: str) -> Type[BaseModel]:
+        """获取模型类
+
+        Args:
+            name: 模型名称
+
+        Returns:
+            模型类
+
+        Raises:
+            KeyError: 未找到该名称的模型
+        """
+        if name not in cls._registry:
+            available = ", ".join(cls._registry.keys())
+            raise KeyError(f"未知模型 '{name}'，可用模型: {available}")
+        return cls._registry[name]
+
+    @classmethod
+    def list_models(cls) -> list[str]:
+        """列出所有已注册的模型名称"""
+        return list(cls._registry.keys())
+
+    @classmethod
+    def clear(cls) -> None:
+        """清空注册表（主要用于测试）"""
+        cls._registry.clear()
+
+
+class ProcessorRegistry:
+    """处理器注册中心
+
+    管理所有可用的数据处理器类，支持通过名称获取处理器类。
+
+    Example:
+        >>> @register_processor("standard_scaler")
+        ... class StandardScaler(BaseProcessor):
+        ...     pass
+        >>>
+        >>> processor_class = ProcessorRegistry.get_processor("standard_scaler")
+        >>> processor = processor_class(**params)
+    """
+
+    _registry: Dict[str, Type[BaseProcessor]] = {}
+
+    @classmethod
+    def register(cls, name: str, processor_class: Type[BaseProcessor]) -> None:
+        """注册处理器类
+
+        Args:
+            name: 处理器名称
+            processor_class: 处理器类（必须继承 BaseProcessor）
+
+        Raises:
+            ValueError: 名称已被注册或类不继承 BaseProcessor
+        """
+        if name in cls._registry:
+            raise ValueError(f"处理器 '{name}' 已被注册")
+        if not issubclass(processor_class, BaseProcessor):
+            raise ValueError(f"处理器类必须继承 BaseProcessor")
+        cls._registry[name] = processor_class
+
+    @classmethod
+    def get_processor(cls, name: str) -> Type[BaseProcessor]:
+        """获取处理器类
+
+        Args:
+            name: 处理器名称
+
+        Returns:
+            处理器类
+
+        Raises:
+            KeyError: 未找到该名称的处理器
+        """
+        if name not in cls._registry:
+            available = ", ".join(cls._registry.keys())
+            raise KeyError(f"未知处理器 '{name}'，可用处理器: {available}")
+        return cls._registry[name]
+
+    @classmethod
+    def list_processors(cls) -> list[str]:
+        """列出所有已注册的处理器名称"""
+        return list(cls._registry.keys())
+
+    @classmethod
+    def clear(cls) -> None:
+        """清空注册表（主要用于测试）"""
+        cls._registry.clear()
+
+
+def register_model(name: str) -> Callable[[Type[BaseModel]], Type[BaseModel]]:
+    """模型注册装饰器
+
+    用于装饰继承 BaseModel 的类，将其注册到 ModelRegistry。
+
+    Args:
+        name: 模型名称
+
+    Returns:
+        装饰器函数
+
+    Example:
+        >>> @register_model("lightgbm")
+        ... class LightGBMModel(BaseModel):
+        ...     name = "lightgbm"
+        ...     def fit(self, X, y): ...
+        ...     def predict(self, X): ...
+    """
+
+    def decorator(cls: Type[BaseModel]) -> Type[BaseModel]:
+        ModelRegistry.register(name, cls)
+        return cls
+
+    return decorator
+
+
+def register_processor(
+    name: str,
+) -> Callable[[Type[BaseProcessor]], Type[BaseProcessor]]:
+    """处理器注册装饰器
+
+    用于装饰继承 BaseProcessor 的类，将其注册到 ProcessorRegistry。
+
+    Args:
+        name: 处理器名称
+
+    Returns:
+        装饰器函数
+
+    Example:
+        >>> @register_processor("standard_scaler")
+        ... class StandardScaler(BaseProcessor):
+        ...     name = "standard_scaler"
+        ...     def transform(self, X): ...
+    """
+
+    def decorator(cls: Type[BaseProcessor]) -> Type[BaseProcessor]:
+        ProcessorRegistry.register(name, cls)
+        return cls
+
+    return decorator