"""
因子挖掘抽象层：支持多种挖掘算法（DEAP、DL、RL等）
"""
from abc import ABC, abstractmethod
from typing import List, Dict, Optional, Any
import pandas as pd
from dataclasses import dataclass

from factor_mining.FactorFormula import FactorFormula


@dataclass
class MiningConfig:
    """挖掘配置基类"""
    ret_horizon: int = 1
    ic_window: int = 30
    ic_method: str = "spearman"  # "spearman" or "pearson"
    seed: Optional[int] = None


class FactorMiner(ABC):
    """因子挖掘器抽象基类"""
    
    def __init__(self, config: MiningConfig):
        self.config = config
    
    @abstractmethod
    def mine(
        self,
        data: pd.DataFrame,
        feature_cols: List[str],
        price_col: str = "close"
    ) -> List[FactorFormula]:
        """
        挖掘因子
        
        Parameters:
        -----------
        data : DataFrame
            数据
        feature_cols : List[str]
            特征列名列表
        price_col : str
            价格列名
        
        Returns:
        --------
        List[FactorFormula]: 挖掘出的因子公式列表
        """
        pass
    
    @abstractmethod
    def get_name(self) -> str:
        """获取挖掘器名称"""
        pass


class MiningPipeline:
    """挖掘流程管理器"""
    
    def __init__(self):
        self.miners: Dict[str, FactorMiner] = {}
    
    def register_miner(self, miner: FactorMiner):
        """注册挖掘器"""
        name = miner.get_name()
        if name in self.miners:
            raise ValueError(f"挖掘器 '{name}' 已存在")
        self.miners[name] = miner
    
    def get_miner(self, name: str) -> Optional[FactorMiner]:
        """获取挖掘器"""
        return self.miners.get(name)
    
    def list_miners(self) -> List[str]:
        """列出所有挖掘器"""
        return list(self.miners.keys())
    
    def mine(
        self,
        miner_name: str,
        data: pd.DataFrame,
        feature_cols: List[str],
        price_col: str = "close"
    ) -> List[FactorFormula]:
        """
        使用指定挖掘器进行挖掘
        
        Parameters:
        -----------
        miner_name : str
            挖掘器名称
        data : DataFrame
            数据
        feature_cols : List[str]
            特征列名列表
        price_col : str
            价格列名
        
        Returns:
        --------
        List[FactorFormula]: 挖掘出的因子公式列表
        """
        miner = self.get_miner(miner_name)
        if miner is None:
            raise ValueError(f"挖掘器 '{miner_name}' 不存在")
        
        return miner.mine(data, feature_cols, price_col)


# 全局挖掘流程管理器
_pipeline = MiningPipeline()


def register_miner(miner: FactorMiner):
    """注册挖掘器到全局管理器"""
    _pipeline.register_miner(miner)


def get_pipeline() -> MiningPipeline:
    """获取全局挖掘流程管理器"""
    return _pipeline