import yaml
from typing import Dict, List
import os
import logging
from pathlib import Path

class MethodReader:
    """方法配置读取器"""
    
    def __init__(self):
        """初始化方法读取器"""
        self.logger = logging.getLogger(__name__)
        self.method_config = self._load_metrics()
        
        
    def _load_metrics(self) -> Dict:
        """加载方法配置文件"""
        try:
            config_path = Path('model/metrics.yaml')
            if not config_path.exists():
                raise FileNotFoundError(f"Method config file not found at {config_path}")
                
            with open(config_path, 'r', encoding='utf-8') as f:
                config = yaml.safe_load(f)
                
            self.logger.info("Successfully loaded method config")
            return config
            
        except Exception as e:
            self.logger.error(f"Error loading method config: {str(e)}")
            raise
            
    
            
    def get_metrics(self) -> Dict:
        """获取预处理方法列表"""
        try:
            metrics = []
            
            # 分类方法
            classification_metrics = self.method_config.get('classification', {})
            if classification_metrics:
                metrics.append({
                    "name": "classification_metrics",
                    "description": "分类方法评价指标",
                    "metric": classification_metrics
                })
                
            # 回归方法
            regression_metrics = self.method_config.get('regression', {})
            if regression_metrics:
                metrics.append({
                    "name": "regression_metrics",
                    "description": "回归方法评价指标",
                    "metric": regression_metrics
                })
                
            # 聚类方法
            clustering_metrics = self.method_config.get('clustering', {})
            if clustering_metrics:
                metrics.append({
                    "name": "clustering_metrics",
                    "description": "聚类方法评价指标",
                    "metric": clustering_metrics
                })
                
            return {
                "status": "success",
                "metric": metrics
            }
            
        except Exception as e:
            self.logger.error(f"Error getting preprocessing methods: {str(e)}")
            return {
                "status": "error",
                "error": str(e)
            }