rov-server/recommend.py

import json
import time
import multiprocessing
import traceback
import hashlib

from datetime import datetime

import config
from log import Log
from config import set_config
from video_recall import PoolRecall
from video_rank import video_rank, bottom_strategy, video_rank_by_w_h_rate, video_rank_with_old_video
from db_helper import RedisHelper
import gevent
from utils import FilterVideos
import ast

log_ = Log()
config_ = set_config()


def relevant_video_top_recommend(app_type, mid, uid, head_vid, videos, size):
    """
    相关推荐强插 运营给定置顶相关性视频
    :param app_type: 产品标识 type-int
    :param mid: mid
    :param uid: uid
    :param head_vid: 相关推荐头部视频id type-int
    :param videos: 当前相关推荐结果 type-list
    :param size: 返回视频个数 type-int
    :return: rank_result
    """
    # 获取头部视频对应的相关性视频
    key_name = '{}{}'.format(config_.RELEVANT_VIDEOS_WITH_OP_KEY_NAME, head_vid)
    redis_helper = RedisHelper()
    relevant_videos = redis_helper.get_data_from_redis(key_name=key_name)
    if relevant_videos is None:
        # 该视频没有指定的相关性视频
        return videos
    relevant_videos = json.loads(relevant_videos)
    # 按照指定顺序排序
    relevant_videos_sorted = sorted(relevant_videos, key=lambda x: x['order'], reverse=False)

    # 过滤
    relevant_video_ids = [int(item['recommend_vid']) for item in relevant_videos_sorted]
    filter_helper = FilterVideos(app_type=app_type, video_ids=relevant_video_ids, mid=mid, uid=uid)
    filtered_ids = filter_helper.filter_videos()
    if filtered_ids is None:
        return videos

    # 获取生效中的视频
    now = int(time.time())
    relevant_videos_in_effect = [
        {'videoId': int(item['recommend_vid']), 'pushFrom': config_.PUSH_FROM['relevant_video_op'],
         'abCode': config_.AB_CODE['relevant_video_op']}
        for item in relevant_videos_sorted
        if item['start_time'] < now < item['finish_time'] and int(item['recommend_vid']) in filtered_ids
    ]

    if len(relevant_videos_in_effect) == 0:
        return videos

    # 与现有排序结果 进行合并重排
    # 获取现有排序结果中流量池视频 及其位置
    relevant_ids = [item['videoId'] for item in relevant_videos_in_effect]
    flow_pool_videos = []
    other_videos = []
    for i, item in enumerate(videos):
        if item.get('pushFrom', None) == config_.PUSH_FROM['flow_recall'] and item.get('videoId') not in relevant_ids:
            flow_pool_videos.append((i, item))
        elif item.get('videoId') not in relevant_ids:
            other_videos.append(item)
        else:
            continue
    # 重排，保持流量池视频位置不变
    rank_result = relevant_videos_in_effect + other_videos
    for i, item in flow_pool_videos:
        rank_result.insert(i, item)

    return rank_result[:size]


def video_position_recommend(mid, uid, app_type, videos):
    # videos = video_recommend(mid=mid, uid=uid, size=size, app_type=app_type,
    #                          algo_type=algo_type, client_info=client_info)
    redis_helper = RedisHelper()
    pos1_vids = redis_helper.get_data_from_redis(config.BaseConfig.RECALL_POSITION1_KEY_NAME)
    pos2_vids = redis_helper.get_data_from_redis(config.BaseConfig.RECALL_POSITION2_KEY_NAME)
    if pos1_vids is not None:
        pos1_vids = ast.literal_eval(pos1_vids)
    if pos2_vids is not None:
        pos2_vids = ast.literal_eval(pos2_vids)

    pos1_vids = [] if pos1_vids is None else pos1_vids
    pos2_vids = [] if pos2_vids is None else pos2_vids

    pos1_vids = [int(i) for i in pos1_vids]
    pos2_vids = [int(i) for i in pos2_vids]

    filter_1 = FilterVideos(app_type=app_type, video_ids=pos1_vids, mid=mid, uid=uid)
    filter_2 = FilterVideos(app_type=app_type, video_ids=pos2_vids, mid=mid, uid=uid)
    t = [gevent.spawn(filter_1.filter_videos), gevent.spawn(filter_2.filter_videos)]
    gevent.joinall(t)
    filted_list = [i.get() for i in t]

    pos1_vids = filted_list[0]
    pos2_vids = filted_list[1]

    videos = positon_duplicate(pos1_vids, pos2_vids, videos)    

    if pos1_vids is not None and len(pos1_vids) >0 :
        videos.insert(0, {'videoId': int(pos1_vids[0]), 'rovScore': 100,
                          'pushFrom': config_.PUSH_FROM['position_insert'], 'abCode': config_.AB_CODE['position_insert']})
    if pos2_vids is not None and len(pos2_vids) >0 :
        videos.insert(1, {'videoId': int(pos2_vids[0]), 'rovScore': 100,
                          'pushFrom': config_.PUSH_FROM['position_insert'], 'abCode': config_.AB_CODE['position_insert']})

    return videos[:10]


def positon_duplicate(pos1_vids, pos2_vids, videos):
    s = set()
    if pos1_vids is not None and len(pos1_vids) >0:
        s.add(int(pos1_vids[0]))
    if pos2_vids is not None and len(pos2_vids) >0:
        s.add(int(pos2_vids[0]))

    l = []
    for item in videos:
        if item['videoId'] in s:
            continue
        else:
            l.append(item)
    return l


def video_recommend(mid, uid, size, top_K, flow_pool_P, app_type, algo_type, client_info, expire_time=24*3600,
                    ab_code=config_.AB_CODE['initial'], rule_key='', no_op_flag=False, old_video_index=-1):
    """
    首页线上推荐逻辑
    :param mid: mid type-string
    :param uid: uid type-string
    :param size: 请求视频数量 type-int
    :param top_K: 保证topK为召回池视频 type-int
    :param flow_pool_P: size-top_K视频为流量池视频的概率 type-float
    :param app_type: 产品标识  type-int
    :param algo_type: 算法类型  type-string
    :param client_info: 用户位置信息 {"country": "国家",  "province": "省份",  "city": "城市"}
    :param expire_time: 末位视频记录redis过期时间
    :param ab_code: AB实验code
    :return:
    """
    # ####### 多进程召回
    start_recall = time.time()
    # log_.info('====== recall')
    '''     
    cores = multiprocessing.cpu_count()
    pool = multiprocessing.Pool(processes=cores)
    pool_recall = PoolRecall(app_type=app_type, mid=mid, uid=uid, ab_code=ab_code)
    _, last_rov_recall_key, _ = pool_recall.get_video_last_idx()
    pool_list = [
        # rov召回池
        pool.apply_async(pool_recall.rov_pool_recall, (size,)),
        # 流量池
        pool.apply_async(pool_recall.flow_pool_recall, (size,))
    ]
    recall_result_list = [p.get() for p in pool_list]
    pool.close()
    pool.join()
    '''
    recall_result_list = []
    pool_recall = PoolRecall(app_type=app_type, mid=mid, uid=uid, ab_code=ab_code,
                             client_info=client_info, rule_key=rule_key, no_op_flag=no_op_flag)
    _, last_rov_recall_key, _ = pool_recall.get_video_last_idx()
    # 小时级实验
    if ab_code in [code for _, code in config_.AB_CODE['rank_by_h'].items()] + \
            [code for _, code in config_.AB_CODE['rank_by_24h'].items()]:
        t = [gevent.spawn(pool_recall.rov_pool_recall_by_h, size, expire_time),
             gevent.spawn(pool_recall.flow_pool_recall, size)]
    # 地域分组实验
    elif ab_code in [code for _, code in config_.AB_CODE['region_rank_by_h'].items()]:
        t = [gevent.spawn(pool_recall.rov_pool_recall_with_region, size, expire_time),
             gevent.spawn(pool_recall.flow_pool_recall, size)]
    # 最惊奇/老好看实验
    elif app_type in [config_.APP_TYPE['LAO_HAO_KAN_VIDEO'], config_.APP_TYPE['ZUI_JING_QI']]:
        t = [gevent.spawn(pool_recall.rov_pool_recall, size, expire_time),
             gevent.spawn(pool_recall.flow_pool_recall_18_19, size)]
    # 天级实验
    elif ab_code in [code for _, code in config_.AB_CODE['rank_by_day'].items()]:
        t = [gevent.spawn(pool_recall.rov_pool_recall_by_day, size, expire_time),
             gevent.spawn(pool_recall.flow_pool_recall, size)]
    # 老视频实验
    # elif ab_code in [config_.AB_CODE['old_video']]:
    #     t = [gevent.spawn(pool_recall.rov_pool_recall, size, expire_time),
    #          gevent.spawn(pool_recall.flow_pool_recall, size),
    #          gevent.spawn(pool_recall.old_videos_recall, size)]
    else:
        t = [gevent.spawn(pool_recall.rov_pool_recall, size, expire_time),
             gevent.spawn(pool_recall.flow_pool_recall, size)]
    gevent.joinall(t)
    recall_result_list = [i.get() for i in t]

    end_recall = time.time()
    log_.info('mid: {}, uid: {}, recall: {}, execute time = {}ms'.format(
        mid, uid, recall_result_list, (end_recall - start_recall) * 1000))

    # ####### 排序
    start_rank = time.time()
    # log_.info('====== rank')
    if app_type in [config_.APP_TYPE['LAO_HAO_KAN_VIDEO'], config_.APP_TYPE['ZUI_JING_QI']]:
        if ab_code in [config_.AB_CODE['rov_rank_appType_18_19'], config_.AB_CODE['rov_rank_appType_19']]:
            data = {
                'rov_pool_recall': recall_result_list[0],
                'flow_pool_recall': recall_result_list[1]
            }
        else:
            data = {
                'rov_pool_recall': recall_result_list[0],
                'flow_pool_recall': []
            }
    else:
        data = {
            'rov_pool_recall': recall_result_list[0],
            'flow_pool_recall': recall_result_list[1]
        }
    rank_result = video_rank(data=data, size=size, top_K=top_K, flow_pool_P=flow_pool_P)

    # 老视频实验
    # if ab_code in [config_.AB_CODE['old_video']]:
    #     rank_result = video_rank_with_old_video(rank_result=rank_result, old_video_recall=recall_result_list[2],
    #                                             size=size, top_K=top_K, old_video_index=old_video_index)

    end_rank = time.time()
    log_.info('mid: {}, uid: {}, rank_result: {}, execute time = {}ms'.format(
        mid, uid, rank_result, (end_rank - start_rank) * 1000))

    if not rank_result:
        # 兜底策略
        # log_.info('====== bottom strategy')
        start_bottom = time.time()
        rank_result = bottom_strategy(size=size, app_type=app_type, ab_code=ab_code)
        end_bottom = time.time()
        log_.info('mid: {}, uid: {}, bottom strategy result: {}, execute time = {}ms'.format(
            mid, uid, rank_result, (end_bottom - start_bottom) * 1000))

    return rank_result, last_rov_recall_key


def ab_test_op(rank_result, ab_code_list, app_type, mid, uid, **kwargs):
    """
    对排序后的结果 按照AB实验进行对应的分组操作
    :param rank_result: 排序后的结果
    :param ab_code_list: 此次请求参与的 ab实验组
    :param app_type: 产品标识
    :param mid: mid
    :param uid: uid
    :param kwargs: 其他参数
    :return:
    """
    # ####### 视频宽高比AB实验
    # 对内容精选进行 视频宽高比分发实验
    # if config_.AB_CODE['w_h_rate'] in ab_code_list and app_type in config_.AB_TEST.get('w_h_rate', []):
    #     rank_result = video_rank_by_w_h_rate(videos=rank_result)
    #     log_.info('app_type: {}, mid: {}, uid: {}, rank_by_w_h_rate_result: {}'.format(
    #         app_type, mid, uid, rank_result))

    # 按position位置排序
    if config_.AB_CODE['position_insert'] in ab_code_list and app_type in config_.AB_TEST.get('position_insert', []):
        rank_result = video_position_recommend(mid, uid, app_type, rank_result)
        print('===========================')
        print(rank_result)
        log_.info('app_type: {}, mid: {}, uid: {}, rank_by_position_insert_result: {}'.format(
            app_type, mid, uid, rank_result))

    # 相关推荐强插
    # if config_.AB_CODE['relevant_video_op'] in ab_code_list \
    #         and app_type in config_.AB_TEST.get('relevant_video_op', []):
    #     head_vid = kwargs['head_vid']
    #     size = kwargs['size']
    #     rank_result = relevant_video_top_recommend(
    #         app_type=app_type, mid=mid, uid=uid, head_vid=head_vid, videos=rank_result, size=size
    #     )
    #     log_.info('app_type: {}, mid: {}, uid: {}, head_vid: {}, rank_by_relevant_video_op_result: {}'.format(
    #         app_type, mid, uid, head_vid, rank_result))

    return rank_result


def update_redis_data(result, app_type, mid, last_rov_recall_key, top_K, expire_time=24*3600):
    """
    根据最终的排序结果更新相关redis数据
    :param result: 排序结果
    :param app_type: 产品标识
    :param mid: mid
    :param last_rov_recall_key: 用户上一次在rov召回池对应的位置 redis key
    :param top_K: 保证topK为召回池视频 type-int
    :param expire_time: 末位视频记录redis过期时间
    :return: None
    """
    # ####### redis数据刷新
    try:
        # log_.info('====== update redis')
        if mid:
            # mid为空时，不做预曝光和定位数据更新
            # 预曝光数据同步刷新到Redis, 过期时间为0.5h
            redis_helper = RedisHelper()
            preview_key_name = config_.PREVIEW_KEY_PREFIX + '{}.{}'.format(app_type, mid)
            preview_video_ids = [int(item['videoId']) for item in result]
            if preview_video_ids:
                # log_.error('key_name = {} \n values = {}'.format(preview_key_name, tuple(preview_video_ids)))
                redis_helper.add_data_with_set(key_name=preview_key_name, values=tuple(preview_video_ids), expire_time=30 * 60)
                log_.info('preview redis update success!')

            # 将此次获取的ROV召回池top_K末位视频id同步刷新到Redis中，方便下次快速定位到召回位置，过期时间为1天
            rov_recall_video = [item['videoId'] for item in result[:top_K]
                                if item['pushFrom'] == config_.PUSH_FROM['rov_recall']]
            if len(rov_recall_video) > 0:
                if app_type == config_.APP_TYPE['APP']:
                    key_name = config_.UPDATE_ROV_KEY_NAME_APP
                else:
                    key_name = config_.UPDATE_ROV_KEY_NAME
                if not redis_helper.get_score_with_value(key_name=key_name, value=rov_recall_video[-1]):
                    redis_helper.set_data_to_redis(key_name=last_rov_recall_key, value=rov_recall_video[-1],
                                                   expire_time=expire_time)
                log_.info('last video redis update success!')

        # 将此次分发的流量池视频，对 本地分发数-1 进行记录
        if app_type not in [config_.APP_TYPE['LAO_HAO_KAN_VIDEO'], config_.APP_TYPE['ZUI_JING_QI']]:
            flow_recall_video = [item for item in result if item['pushFrom'] == config_.PUSH_FROM['flow_recall']]
            if flow_recall_video:
                update_local_distribute_count(flow_recall_video)
                log_.info('update local distribute count success!')

    except Exception as e:
        log_.error("update redis data fail!")
        log_.error(traceback.format_exc())


def update_local_distribute_count(videos):
    """
    更新本地分发数
    :param videos: 视频列表 type-list [{'videoId':'', 'flowPool':'', 'distributeCount': '',
                                    'rovScore': '', 'pushFrom': 'flow_pool', 'abCode': self.ab_code}, ....]
    :return:
    """
    try:
        redis_helper = RedisHelper()
        for item in videos:
            key_name = '{}{}.{}'.format(config_.LOCAL_DISTRIBUTE_COUNT_PREFIX, item['videoId'], item['flowPool'])
            # 本地记录的分发数 - 1
            redis_helper.decr_key(key_name=key_name, amount=1, expire_time=5 * 60)

            # if redis_helper.key_exists(key_name=key_name):
            #     # 该视频本地有记录，本地记录的分发数 - 1
            #     redis_helper.decr_key(key_name=key_name, amount=1, expire_time=5 * 60)
            # else:
            #     # 该视频本地无记录，接口获取的分发数 - 1
            #     redis_helper.incr_key(key_name=key_name, amount=int(item['distributeCount']) - 1, expire_time=5 * 60)

    except Exception as e:
        log_.error('update_local_distribute_count error...')
        log_.error(traceback.format_exc())


def get_recommend_params(ab_exp_info):
    """根据实验分组给定对应的推荐参数"""
    top_K = config_.K
    flow_pool_P = config_.P
    # 不获取人工干预数据标记
    no_op_flag = False
    if not ab_exp_info:
        ab_code = config_.AB_CODE['initial']
        expire_time = 24 * 3600
        rule_key = config_.RULE_KEY['initial']
        old_video_index = -1
    else:
        ab_exp_code_list = []
        config_value_dict = {}
        for _, item in ab_exp_info.items():
            if not item:
                continue
            for ab_item in item:
                ab_exp_code = ab_item.get('abExpCode', None)
                if not ab_exp_code:
                    continue
                ab_exp_code_list.append(str(ab_exp_code))
                config_value_dict[str(ab_exp_code)] = ab_item.get('configValue', None)
        # 推荐条数 10->4 实验
        # if config_.AB_EXP_CODE['rec_size_home'] in ab_exp_code_list:
        #     config_value = config_value_dict.get(config_.AB_EXP_CODE['rec_size_home'], None)
        #     if config_value:
        #         config_value = eval(str(config_value))
        #     else:
        #         config_value = {}
        #     log_.info(f'config_value: {config_value}, type: {type(config_value)}')
        #     size = int(config_value.get('size', 4))
        #     top_K = int(config_value.get('K', 3))
        #     flow_pool_P = float(config_value.get('P', 0.3))
        # else:
        #     size = size
        #     top_K = config_.K
        #     flow_pool_P = config_.P

        # 算法实验相对对照组
        if config_.AB_EXP_CODE['ab_initial'] in ab_exp_code_list:
            ab_code = config_.AB_CODE['ab_initial']
            expire_time = 24 * 3600
            rule_key = config_.RULE_KEY['initial']
            no_op_flag = True

        # 小时级更新-规则1 实验
        elif config_.AB_EXP_CODE['rule_rank1'] in ab_exp_code_list:
            ab_code = config_.AB_CODE['rank_by_h'].get('rule_rank1')
            expire_time = 3600
            rule_key = config_.RULE_KEY['rule_rank1']
            no_op_flag = True

        # elif config_.AB_EXP_CODE['rule_rank2'] in ab_exp_code_list:
        #     ab_code = config_.AB_CODE['rank_by_h'].get('rule_rank2')
        #     expire_time = 3600
        #     rule_key = config_.RULE_KEY['rule_rank2']

        elif config_.AB_EXP_CODE['rule_rank3'] in ab_exp_code_list:
            ab_code = config_.AB_CODE['rank_by_h'].get('rule_rank3')
            expire_time = 3600
            rule_key = config_.RULE_KEY['rule_rank3']
            no_op_flag = True

        # elif config_.AB_EXP_CODE['rule_rank4'] in ab_exp_code_list:
        #     ab_code = config_.AB_CODE['rank_by_h'].get('rule_rank4')
        #     expire_time = 3600
        #     rule_key = config_.RULE_KEY['rule_rank4']

        # elif config_.AB_EXP_CODE['rule_rank5'] in ab_exp_code_list:
        #     ab_code = config_.AB_CODE['rank_by_h'].get('rule_rank5')
        #     expire_time = 3600
        #     rule_key = config_.RULE_KEY['rule_rank5']

        # elif config_.AB_EXP_CODE['day_rule_rank1'] in ab_exp_code_list:
        #     ab_code = config_.AB_CODE['rank_by_day'].get('day_rule_rank1')
        #     expire_time = 24 * 3600
        #     rule_key = config_.RULE_KEY_DAY['day_rule_rank1']
        #     no_op_flag = True

        elif config_.AB_EXP_CODE['rule_rank6'] in ab_exp_code_list:
            ab_code = config_.AB_CODE['rank_by_h'].get('rule_rank6')
            expire_time = 3600
            rule_key = config_.RULE_KEY['rule_rank6']
            no_op_flag = True

        elif config_.AB_EXP_CODE['day_rule_rank2'] in ab_exp_code_list:
            ab_code = config_.AB_CODE['rank_by_day'].get('day_rule_rank2')
            expire_time = 24 * 3600
            rule_key = config_.RULE_KEY_DAY['day_rule_rank2']
            no_op_flag = True

        elif config_.AB_EXP_CODE['region_rule_rank1'] in ab_exp_code_list:
            ab_code = config_.AB_CODE['region_rank_by_h'].get('region_rule_rank1')
            expire_time = 3600
            rule_key = config_.RULE_KEY_REGION['region_rule_rank1']
            no_op_flag = True

        elif config_.AB_EXP_CODE['24h_rule_rank1'] in ab_exp_code_list:
            ab_code = config_.AB_CODE['rank_by_24h'].get('24h_rule_rank1')
            expire_time = 3600
            rule_key = config_.RULE_KEY_24H['24h_rule_rank1']
            no_op_flag = True

        elif config_.AB_EXP_CODE['region_rule_rank2'] in ab_exp_code_list:
            ab_code = config_.AB_CODE['region_rank_by_h'].get('region_rule_rank2')
            expire_time = 3600
            rule_key = config_.RULE_KEY_REGION['region_rule_rank2']
            no_op_flag = True

        else:
            ab_code = config_.AB_CODE['initial']
            expire_time = 24 * 3600
            rule_key = config_.RULE_KEY['initial']

        # 老好看视频 / 票圈最惊奇 首页/相关推荐逻辑更新实验
        if config_.AB_EXP_CODE['rov_rank_appType_18_19'] in ab_exp_code_list:
            ab_code = config_.AB_CODE['rov_rank_appType_18_19']
            expire_time = 3600
            flow_pool_P = config_.P_18_19
            no_op_flag = True

        elif config_.AB_EXP_CODE['rov_rank_appType_19'] in ab_exp_code_list:
            ab_code = config_.AB_CODE['rov_rank_appType_19']
            expire_time = 3600
            top_K = 1
            flow_pool_P = config_.P_18_19
            no_op_flag = True

        # 老视频实验
        if config_.AB_EXP_CODE['old_video'] in ab_exp_code_list:
            ab_code = config_.AB_CODE['old_video']
            no_op_flag = True
            old_video_index = 2
        else:
            old_video_index = -1

    return top_K, flow_pool_P, ab_code, rule_key, expire_time, no_op_flag, old_video_index


def video_homepage_recommend(mid, uid, size, app_type, algo_type, client_info, ab_exp_info):
    """
    首页线上推荐逻辑
    :param mid: mid type-string
    :param uid: uid type-string
    :param size: 请求视频数量 type-int
    :param app_type: 产品标识  type-int
    :param algo_type: 算法类型  type-string
    :param client_info: 用户位置信息 {"country": "国家",  "province": "省份",  "city": "城市"}
    :param ab_exp_info: ab实验分组参数 [{"expItemId":1, "configValue":{"size":4, "K":3, ...}}, ...]
    :return:
    """

    # 对 vlog 切换10%的流量做实验
    # 对mid进行哈希
    # hash_mid = hashlib.md5(mid.encode('utf-8')).hexdigest()
    # if app_type in config_.AB_TEST['rank_by_h'] and hash_mid[-1:] in ['8', '0', 'a', 'b']:
    #     # 简单召回 - 排序 - 兜底
    #     rank_result, last_rov_recall_key = video_recommend(mid=mid, uid=uid, size=size, app_type=app_type,
    #                                                        algo_type=algo_type, client_info=client_info,
    #                                                        expire_time=3600,
    #                                                        ab_code=config_.AB_CODE['rank_by_h'])
    #     # ab-test
    #     result = ab_test_op(rank_result=rank_result,
    #                         ab_code_list=[config_.AB_CODE['position_insert']],
    #                         app_type=app_type, mid=mid, uid=uid)
    #     # redis数据刷新
    #     update_redis_data(result=result, app_type=app_type, mid=mid, last_rov_recall_key=last_rov_recall_key,
    #                       expire_time=3600)

    if app_type == config_.APP_TYPE['APP']:
        # 票圈视频APP
        top_K = config_.K
        flow_pool_P = config_.P
        # 简单召回 - 排序 - 兜底
        rank_result, last_rov_recall_key = video_recommend(mid=mid, uid=uid, app_type=app_type,
                                                           size=size, top_K=top_K, flow_pool_P=flow_pool_P,
                                                           algo_type=algo_type, client_info=client_info,
                                                           expire_time=12 * 3600)
        # ab-test
        # result = ab_test_op(rank_result=rank_result,
        #                     ab_code_list=[config_.AB_CODE['position_insert']],
        #                     app_type=app_type, mid=mid, uid=uid)
        # redis数据刷新
        update_redis_data(result=rank_result, app_type=app_type, mid=mid, last_rov_recall_key=last_rov_recall_key,
                          top_K=top_K, expire_time=12 * 3600)

    else:
        top_K, flow_pool_P, ab_code, rule_key, expire_time, no_op_flag, old_video_index = \
            get_recommend_params(ab_exp_info=ab_exp_info)

        # 简单召回 - 排序 - 兜底
        rank_result, last_rov_recall_key = video_recommend(mid=mid, uid=uid, app_type=app_type,
                                                           size=size, top_K=top_K, flow_pool_P=flow_pool_P,
                                                           algo_type=algo_type, client_info=client_info,
                                                           ab_code=ab_code, expire_time=expire_time,
                                                           rule_key=rule_key, no_op_flag=no_op_flag,
                                                           old_video_index=old_video_index)
        # ab-test
        # result = ab_test_op(rank_result=rank_result,
        #                     ab_code_list=[config_.AB_CODE['position_insert']],
        #                     app_type=app_type, mid=mid, uid=uid)
        # redis数据刷新
        update_redis_data(result=rank_result, app_type=app_type, mid=mid, last_rov_recall_key=last_rov_recall_key,
                          top_K=top_K)

    return rank_result


def video_relevant_recommend(video_id, mid, uid, size, app_type, ab_exp_info, client_info):
    """
    相关推荐逻辑
    :param video_id: 相关推荐的头部视频id
    :param mid: mid type-string
    :param uid: uid type-string
    :param size: 请求视频数量 type-int
    :param app_type: 产品标识  type-int
    :param ab_exp_info: ab实验分组参数 [{"expItemId":1, "configValue":{"size":4, "K":3, ...}}, ...]
    :return: videos type-list
    """
    top_K, flow_pool_P, ab_code, rule_key, expire_time, no_op_flag, old_video_index = \
        get_recommend_params(ab_exp_info=ab_exp_info)

    # 简单召回 - 排序 - 兜底
    rank_result, last_rov_recall_key = video_recommend(mid=mid, uid=uid, app_type=app_type,
                                                       size=size, top_K=top_K, flow_pool_P=flow_pool_P,
                                                       algo_type='', client_info=client_info,
                                                       ab_code=ab_code, expire_time=expire_time,
                                                       rule_key=rule_key, no_op_flag=no_op_flag,
                                                       old_video_index=old_video_index)
    # ab-test
    # result = ab_test_op(rank_result=rank_result,
    #                     ab_code_list=[config_.AB_CODE['position_insert'], config_.AB_CODE['relevant_video_op']],
    #                     app_type=app_type, mid=mid, uid=uid, head_vid=video_id, size=size)
    # redis数据刷新
    update_redis_data(result=rank_result, app_type=app_type, mid=mid, last_rov_recall_key=last_rov_recall_key,
                      top_K=top_K)

    return rank_result


if __name__ == '__main__':
    videos = [
        {"videoId": 10136461, "rovScore": 99.971, "pushFrom": "recall_pool", "abCode": 10000},
        {"videoId": 10239014, "rovScore": 99.97, "pushFrom": "recall_pool", "abCode": 10000},
        {"videoId": 9851154, "rovScore": 99.969, "pushFrom": "recall_pool", "abCode": 10000},
        {"videoId": 10104347, "rovScore": 99.968, "pushFrom": "recall_pool", "abCode": 10000},
        {"videoId": 10141507, "rovScore": 99.967, "pushFrom": "recall_pool", "abCode": 10000},
        {"videoId": 10292817, "flowPool": "2#6#2#1641780979606", "rovScore": 53.926690610816486,
         "pushFrom": "flow_pool", "abCode": 10000},
        {"videoId": 10224932, "flowPool": "2#5#1#1641800279644", "rovScore": 53.47890460059617, "pushFrom": "flow_pool",
         "abCode": 10000},
        {"videoId": 9943255, "rovScore": 99.966, "pushFrom": "recall_pool", "abCode": 10000},
        {"videoId": 10282970, "flowPool": "2#5#1#1641784814103", "rovScore": 52.682815076325575,
         "pushFrom": "flow_pool", "abCode": 10000},
        {"videoId": 10282205, "rovScore": 99.965, "pushFrom": "recall_pool", "abCode": 10000}
    ]
    res = relevant_video_top_recommend(app_type=4, mid='', uid=1111, head_vid=123, videos=videos, size=10)
    print(res)