"""
  i2i - Swing算法实现
  基于用户行为的物品相似度计算
  参考item_sim.py的数据格式，适配真实数据
  """
  import sys
  import os
  sys.path.append(os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))))
  
  import pandas as pd
  import math
  from collections import defaultdict
  import argparse
  import json
  from datetime import datetime, timedelta
  from db_service import create_db_connection
  from offline_tasks.config.offline_config import (
      DB_CONFIG, OUTPUT_DIR, I2I_CONFIG, get_time_range,
      DEFAULT_LOOKBACK_DAYS, DEFAULT_I2I_TOP_N
  )

  from offline_tasks.scripts.debug_utils import (
      setup_debug_logger, log_dataframe_info, log_dict_stats,

      save_readable_index, load_name_mappings_from_file, log_algorithm_params,

      log_processing_step
  )

  
  
  def calculate_time_weight(event_time, reference_time, decay_factor=0.95, days_unit=30):
      """
      计算时间衰减权重
      
      Args:
          event_time: 事件发生时间
          reference_time: 参考时间（通常是当前时间）
          decay_factor: 衰减因子
          days_unit: 衰减单位（天）
      
      Returns:
          时间权重
      """
      if pd.isna(event_time):
          return 1.0
      
      time_diff = (reference_time - event_time).days
      if time_diff < 0:
          return 1.0
      
      # 计算衰减权重
      periods = time_diff / days_unit
      weight = math.pow(decay_factor, periods)
      return weight
  
  

  def swing_algorithm(df, alpha=0.5, time_decay=True, decay_factor=0.95, use_daily_session=True, logger=None, debug=False):

      """
      Swing算法实现
      
      Args:
          df: DataFrame with columns: user_id, item_id, weight, create_time
          alpha: Swing算法的alpha参数
          time_decay: 是否使用时间衰减
          decay_factor: 时间衰减因子

          use_daily_session: 是否同时使用uid+日期作为session维度

          logger: 日志记录器
          debug: 是否开启debug模式

      
      Returns:
          Dict[item_id, List[Tuple(similar_item_id, score)]]
      """

      start_time = datetime.now()
      if logger:

          logger.debug(f"开始Swing算法计算，参数: alpha={alpha}, time_decay={time_decay}, use_daily_session={use_daily_session}")

      # 如果使用时间衰减，计算时间权重
      reference_time = datetime.now()
      if time_decay and 'create_time' in df.columns:

          if logger:
              logger.debug("应用时间衰减...")

          df['time_weight'] = df['create_time'].apply(
              lambda x: calculate_time_weight(x, reference_time, decay_factor)
          )
          df['weight'] = df['weight'] * df['time_weight']

          if logger and debug:
              logger.debug(f"时间权重统计: min={df['time_weight'].min():.4f}, max={df['time_weight'].max():.4f}, avg={df['time_weight'].mean():.4f}")

      # 如果启用daily_session，duplicate数据：添加uid+date作为新的uid
      if use_daily_session and 'create_time' in df.columns:
          if logger:
              logger.info("启用日期维度：duplicate数据，添加uid+日期作为新的session")
          
          # 创建原始数据副本
          df_original = df.copy()
          
          # 创建uid+date版本
          df_daily = df.copy()
          df_daily['date'] = pd.to_datetime(df_daily['create_time']).dt.strftime('%Y%m%d')
          df_daily['user_id'] = df_daily['user_id'].astype(str) + '_' + df_daily['date']
          
          # 合并两份数据
          df = pd.concat([df_original, df_daily], ignore_index=True)
          
          if logger:
              logger.info(f"原始数据: {len(df_original)} 条")
              logger.info(f"日期维度数据: {len(df_daily)} 条")
              logger.info(f"合并后总数据: {len(df)} 条")
      

      # 构建用户-物品倒排索引

      if logger:
          log_processing_step(logger, "步骤1: 构建用户-物品倒排索引")
      

      user_items = defaultdict(set)
      item_users = defaultdict(set)
      item_freq = defaultdict(float)
      
      for _, row in df.iterrows():
          user_id = row['user_id']
          item_id = row['item_id']
          weight = row['weight']
          
          user_items[user_id].add(item_id)
          item_users[item_id].add(user_id)
          item_freq[item_id] += weight
      

      if logger:
          logger.info(f"总用户数: {len(user_items)}, 总商品数: {len(item_users)}")
          if debug:
              log_dict_stats(logger, dict(list(user_items.items())[:1000]), "用户-商品倒排索引（采样）", top_n=5)
              log_dict_stats(logger, dict(list(item_users.items())[:1000]), "商品-用户倒排索引（采样）", top_n=5)

      
      # 计算物品相似度

      if logger:
          log_processing_step(logger, "步骤2: 计算Swing物品相似度")
      

      item_sim_dict = defaultdict(lambda: defaultdict(float))
      
      # 遍历每个物品对

      processed_pairs = 0
      total_items = len(item_users)
      
      for idx_i, item_i in enumerate(item_users):

          users_i = item_users[item_i]
          
          # 找到所有与item_i共现的物品
          for item_j in item_users:
              if item_i >= item_j:  # 避免重复计算
                  continue
              
              users_j = item_users[item_j]
              common_users = users_i & users_j
              
              if len(common_users) < 2:
                  continue
              
              # 计算Swing相似度
              sim_score = 0.0
              common_users_list = list(common_users)
              
              for idx_u in range(len(common_users_list)):
                  user_u = common_users_list[idx_u]
                  items_u = user_items[user_u]
                  
                  for idx_v in range(idx_u + 1, len(common_users_list)):
                      user_v = common_users_list[idx_v]
                      items_v = user_items[user_v]
                      
                      # 计算用户u和用户v的共同物品数
                      common_items = items_u & items_v
                      
                      # Swing公式
                      sim_score += 1.0 / (alpha + len(common_items))
              
              item_sim_dict[item_i][item_j] = sim_score
              item_sim_dict[item_j][item_i] = sim_score

              processed_pairs += 1
          
          # Debug: 显示处理进度
          if logger and debug and (idx_i + 1) % 50 == 0:
              logger.debug(f"已处理 {idx_i + 1}/{total_items} 个商品 ({(idx_i+1)/total_items*100:.1f}%)")
      
      if logger:
          logger.info(f"计算了 {processed_pairs} 对商品相似度")

      
      # 对相似度进行归一化并排序

      if logger:
          log_processing_step(logger, "步骤3: 整理和排序结果")
      

      result = {}
      for item_i in item_sim_dict:
          sims = item_sim_dict[item_i]
          

          # 按相似度排序
          sorted_sims = sorted(sims.items(), key=lambda x: -x[1])
          result[item_i] = sorted_sims
      

      if logger:
          total_time = (datetime.now() - start_time).total_seconds()
          logger.info(f"Swing算法完成: {len(result)} 个商品有相似推荐")
          logger.info(f"总耗时: {total_time:.2f}秒")
          
          # 统计每个商品的相似商品数
          sim_counts = [len(sims) for sims in result.values()]
          if sim_counts:
              logger.info(f"相似商品数统计: min={min(sim_counts)}, max={max(sim_counts)}, avg={sum(sim_counts)/len(sim_counts):.2f}")
          
          # 采样展示结果
          if debug:
              sample_results = list(result.items())[:3]
              for item_i, sims in sample_results:
                  logger.debug(f"  商品 {item_i} 的Top5相似商品: {sims[:5]}")
      

      return result
  
  
  def main():
      parser = argparse.ArgumentParser(description='Run Swing algorithm for i2i similarity')
      parser.add_argument('--alpha', type=float, default=I2I_CONFIG['swing']['alpha'],
                         help='Alpha parameter for Swing algorithm')
      parser.add_argument('--top_n', type=int, default=DEFAULT_I2I_TOP_N,
                         help=f'Top N similar items to output (default: {DEFAULT_I2I_TOP_N})')
      parser.add_argument('--lookback_days', type=int, default=DEFAULT_LOOKBACK_DAYS,
                         help=f'Number of days to look back for user behavior (default: {DEFAULT_LOOKBACK_DAYS})')

      parser.add_argument('--time_decay', action='store_true', default=False,
                         help='Use time decay for behavior weights (default: False for B2B low-frequency scenarios)')

      parser.add_argument('--decay_factor', type=float, default=0.95,
                         help='Time decay factor')

      parser.add_argument('--use_daily_session', action='store_true', default=True,
                         help='Use uid+date as additional session dimension (default: True)')
      parser.add_argument('--no_daily_session', action='store_false', dest='use_daily_session',
                         help='Disable uid+date session dimension')

      parser.add_argument('--output', type=str, default=None,
                         help='Output file path')

      parser.add_argument('--debug', action='store_true',
                         help='Enable debug mode with detailed logging and readable output')

      
      args = parser.parse_args()
      

      # 设置日志
      logger = setup_debug_logger('i2i_swing', debug=args.debug)
      
      # 记录参数
      log_algorithm_params(logger, {
          'alpha': args.alpha,
          'top_n': args.top_n,
          'lookback_days': args.lookback_days,
          'time_decay': args.time_decay,
          'decay_factor': args.decay_factor,

          'use_daily_session': args.use_daily_session,

          'debug': args.debug
      })
      

      # 创建数据库连接

      logger.info("连接数据库...")

      engine = create_db_connection(
          DB_CONFIG['host'],
          DB_CONFIG['port'],
          DB_CONFIG['database'],
          DB_CONFIG['username'],
          DB_CONFIG['password']
      )
      
      # 获取时间范围
      start_date, end_date = get_time_range(args.lookback_days)

      logger.info(f"获取数据: {start_date} 到 {end_date}")

      
      # SQL查询 - 获取用户行为数据
      sql_query = f"""
      SELECT 
          se.anonymous_id AS user_id,
          se.item_id,
          se.event AS event_type,
          se.create_time,
          pgs.name AS item_name
      FROM 
          sensors_events se
      LEFT JOIN prd_goods_sku pgs ON se.item_id = pgs.id
      WHERE 
          se.event IN ('contactFactory', 'addToPool', 'addToCart', 'purchase')
          AND se.create_time >= '{start_date}'
          AND se.create_time <= '{end_date}'
          AND se.item_id IS NOT NULL
          AND se.anonymous_id IS NOT NULL
      ORDER BY 
          se.create_time
      """
      

      try:
          logger.info("执行SQL查询...")
          df = pd.read_sql(sql_query, engine)
          logger.info(f"获取到 {len(df)} 条记录")
          
          # Debug: 显示数据详情
          if args.debug:
              log_dataframe_info(logger, df, "用户行为数据", sample_size=10)
      except Exception as e:
          logger.error(f"获取数据失败: {e}")
          return
      
      if len(df) == 0:
          logger.warning("没有找到数据")
          return

      
      # 转换create_time为datetime
      df['create_time'] = pd.to_datetime(df['create_time'])
      
      # 定义行为权重
      behavior_weights = {
          'contactFactory': 5.0,
          'addToPool': 2.0,
          'addToCart': 3.0,
          'purchase': 10.0
      }
      
      # 添加权重列
      df['weight'] = df['event_type'].map(behavior_weights).fillna(1.0)
      

      if logger and args.debug:
          logger.debug(f"行为类型分布:")
          event_counts = df['event_type'].value_counts()
          for event, count in event_counts.items():
              logger.debug(f"  {event}: {count} ({count/len(df)*100:.2f}%)")
      

      # 运行Swing算法

      logger.info("运行Swing算法...")

      result = swing_algorithm(
          df,
          alpha=args.alpha,
          time_decay=args.time_decay,

          decay_factor=args.decay_factor,

          use_daily_session=args.use_daily_session,

          logger=logger,
          debug=args.debug

      )
      

      # 创建item_id到name的映射（key转为字符串，与name_mappings一致）
      item_name_map = dict(zip(df['item_id'].unique().astype(str), df.groupby('item_id')['item_name'].first()))

      
      # 输出结果
      output_file = args.output or os.path.join(OUTPUT_DIR, f'i2i_swing_{datetime.now().strftime("%Y%m%d")}.txt')
      

      logger.info(f"保存结果到: {output_file}")
      output_count = 0

      with open(output_file, 'w', encoding='utf-8') as f:
          for item_id, sims in result.items():

              # item_name_map的key是字符串，需要转换
              item_name = item_name_map.get(str(item_id), 'Unknown')

              
              # 只取前N个最相似的商品
              top_sims = sims[:args.top_n]
              
              if not top_sims:
                  continue
              
              # 格式：item_id \t item_name \t similar_item_id1:score1,similar_item_id2:score2,...
              sim_str = ','.join([f'{sim_id}:{score:.4f}' for sim_id, score in top_sims])
              f.write(f'{item_id}\t{item_name}\t{sim_str}\n')

              output_count += 1
      
      logger.info(f"输出了 {output_count} 个商品的推荐")

      # Debug模式：生成明文文件
      if args.debug:
          logger.info("Debug模式：生成明文索引文件...")
          try:

              # 从本地文件加载名称映射
              logger.debug("加载ID到名称的映射...")
              name_mappings = load_name_mappings_from_file(debug=True)

              # 准备索引数据（合并已有的item_name_map）
              # item_name_map的key已经是str类型，可以直接更新

              name_mappings['item'].update(item_name_map)
              

              if args.debug:

                  logger.debug(f"name_mappings['item']共有 {len(name_mappings['item'])} 个商品名称")
              

              index_data = {}
              for item_id, sims in result.items():
                  top_sims = sims[:args.top_n]
                  if top_sims:
                      index_data[f"i2i:swing:{item_id}"] = top_sims
              
              # 保存明文文件
              readable_file = save_readable_index(
                  output_file,
                  index_data,
                  name_mappings,
                  description=f"Swing算法 i2i相似度推荐 (alpha={args.alpha}, lookback_days={args.lookback_days})"
              )
              logger.info(f"明文索引文件: {readable_file}")
          except Exception as e:
              logger.error(f"生成明文文件失败: {e}", exc_info=True)
      
      logger.info("完成！")

  
  
  if __name__ == '__main__':
      main()