"""ProStock ML Pipeline 组件库

提供组件化、低耦合、插件式的机器学习流水线组件。
包括处理器、模型、划分策略等可复用组件。

示例:
    >>> from src.pipeline import (
    ...     PluginRegistry, ProcessingPipeline,
    ...     PipelineStage, BaseProcessor
    ... )

    >>> # 获取注册的处理器
    >>> scaler_class = PluginRegistry.get_processor("standard_scaler")
    >>> scaler = scaler_class()

    >>> # 创建处理流水线
    >>> pipeline = ProcessingPipeline([
    ...     PluginRegistry.get_processor("dropna")(),
    ...     PluginRegistry.get_processor("winsorizer")(lower=0.01, upper=0.99),
    ...     PluginRegistry.get_processor("standard_scaler")(),
    ... ])
"""

# 导入核心抽象类和划分策略
from src.pipeline.core import (
    PipelineStage,
    TaskType,
    BaseProcessor,
    BaseModel,
    BaseSplitter,
    BaseMetric,
    TimeSeriesSplit,
    WalkForwardSplit,
    ExpandingWindowSplit,
)

# 导入注册中心
from src.pipeline.registry import PluginRegistry

# 导入处理流水线
from src.pipeline.pipeline import ProcessingPipeline

# 导入并注册内置处理器
from src.pipeline.processors.processors import (
    DropNAProcessor,
    FillNAProcessor,
    Winsorizer,
    StandardScaler,
    MinMaxScaler,
    RankTransformer,
    Neutralizer,
)

# 导入并注册内置模型
from src.pipeline.models.models import (
    LightGBMModel,
    CatBoostModel,
)

__all__ = [
    # 核心抽象
    "PipelineStage",
    "TaskType",
    "BaseProcessor",
    "BaseModel",
    "BaseSplitter",
    "BaseMetric",
    # 划分策略
    "TimeSeriesSplit",
    "WalkForwardSplit",
    "ExpandingWindowSplit",
    # 注册中心
    "PluginRegistry",
    # 处理流水线
    "ProcessingPipeline",
    # 处理器
    "DropNAProcessor",
    "FillNAProcessor",
    "Winsorizer",
    "StandardScaler",
    "MinMaxScaler",
    "RankTransformer",
    "Neutralizer",
    # 模型
    "LightGBMModel",
    "CatBoostModel",
]