import pandas as pd
import numpy as np
from typing import Dict, List, Union, Optional
from sklearn.preprocessing import StandardScaler, MinMaxScaler, RobustScaler
from sklearn.impute import SimpleImputer
from sklearn.model_selection import train_test_split
import logging
import hashlib
from datetime import datetime
import json
import os

class DataProcessor:
    """数据处理基类"""
    
    def __init__(self, config: Dict = None):
        self.config = config or {}
        self.logger = logging.getLogger(__name__)
        self._setup_logging()
        
    def _setup_logging(self):
        """设置日志"""
        logging.basicConfig(
            level=logging.INFO,
            format='%(asctime)s - %(name)s - %(levelname)s - %(message)s'
        )
    
    def load_data(self, file_path: str) -> pd.DataFrame:
        """加载数据"""
        try:
            file_type = file_path.split('.')[-1].lower()
            if file_type == 'csv':
                df = pd.read_csv(file_path, **self.config.get('csv_params', {}))
            elif file_type == 'parquet':
                df = pd.read_parquet(file_path)
            elif file_type == 'hdf5':
                df = pd.read_hdf(file_path)
            else:
                raise ValueError(f"Unsupported file type: {file_type}")
                
            self.logger.info(f"Successfully loaded data from {file_path}")
            return df
            
        except Exception as e:
            self.logger.error(f"Error loading data: {str(e)}")
            raise
            
    def save_data(self, df: pd.DataFrame, file_path: str):
        """保存数据"""
        try:
            file_type = file_path.split('.')[-1].lower()
            if file_type == 'csv':
                df.to_csv(file_path, index=False)
            elif file_type == 'parquet':
                df.to_parquet(file_path)
            elif file_type == 'hdf5':
                df.to_hdf(file_path, key='data')
                
            self.logger.info(f"Successfully saved data to {file_path}")
            
        except Exception as e:
            self.logger.error(f"Error saving data: {str(e)}")
            raise