import random import numpy from log import Log from config import set_config from video_recall import PoolRecall from db_helper import RedisHelper from utils import FilterVideos, send_msg_to_feishu log_ = Log() config_ = set_config() def video_rank(data, size): """ 视频分发排序 :param data: 各路召回的视频 type-dict {'rov_pool_recall': [], 'flow_pool_recall': []} :param size: 请求数 :return: rank_result """ if not data['rov_pool_recall'] and not data['flow_pool_recall']: return None # 将各路召回的视频按照score从大到小排序 # ROV召回池 rov_recall_rank = sorted(data['rov_pool_recall'], key=lambda k: (k.get('rovScore'), 0), reverse=True) # 流量池 flow_recall_rank = sorted(data['flow_pool_recall'], key=lambda k: (k.get('rovScore'), 0), reverse=True) # 对各路召回的视频进行去重 rov_recall_rank, flow_recall_rank = remove_duplicate(rov_recall=rov_recall_rank, flow_recall=flow_recall_rank) log_.info('remove_duplicate finished! rov_recall_rank = {}, flow_recall_rank = {}'.format( rov_recall_rank, flow_recall_rank)) # 从ROV召回池中获取top k if len(rov_recall_rank) > 0: rank_result = rov_recall_rank[:config_.K] rov_recall_rank = rov_recall_rank[config_.K:] else: rank_result = flow_recall_rank[:config_.K] flow_recall_rank = flow_recall_rank[config_.K:] # 按概率 p 及score排序获取 size - k 个视频 i = 0 while i < size - config_.K: # 随机生成[0, 1)浮点数 rand = random.random() log_.info('rand: {}'.format(rand)) if rand < config_.P: if flow_recall_rank: rank_result.append(flow_recall_rank[0]) flow_recall_rank.remove(flow_recall_rank[0]) else: rank_result.extend(rov_recall_rank[:size - config_.K - i]) return rank_result else: if rov_recall_rank: rank_result.append(rov_recall_rank[0]) rov_recall_rank.remove(rov_recall_rank[0]) else: rank_result.extend(flow_recall_rank[:size - config_.K - i]) return rank_result i += 1 return rank_result def remove_duplicate(rov_recall, flow_recall): """ 对多路召回的视频去重 去重原则： 如果视频在ROV召回池topK，则保留ROV召回池，否则保留流量池 :param rov_recall: ROV召回池-已排序 :param flow_recall: 流量池-已排序 :return: """ flow_recall_result = [] rov_recall_remove = [] flow_recall_video_ids = [item['videoId'] for item in flow_recall] # rov_recall topK for item in rov_recall[:config_.K]: if item['videoId'] in flow_recall_video_ids: flow_recall_video_ids.remove(item['videoId']) # other for item in rov_recall[config_.K:]: if item['videoId'] in flow_recall_video_ids: rov_recall_remove.append(item) # rov recall remove for item in rov_recall_remove: rov_recall.remove(item) # flow recall remove for item in flow_recall: if item['videoId'] in flow_recall_video_ids: flow_recall_result.append(item) return rov_recall, flow_recall_result def bottom_strategy(size, app_type, ab_code): """ 兜底策略: 从ROV召回池中获取top1000，进行状态过滤后的视频 :param size: 需要获取的视频数 :param app_type: 产品标识 type-int :param ab_code: abCode :return: """ pool_recall = PoolRecall(app_type=app_type, ab_code=ab_code) key_name, _ = pool_recall.get_pool_redis_key(pool_type='rov') redis_helper = RedisHelper() data = redis_helper.get_data_zset_with_index(key_name=key_name, start=0, end=1000) if not data: send_msg_to_feishu('生产环境 —— ROV推荐进入了二次兜底，请查看是否有数据更新失败问题。') # 二次兜底 bottom_data = bottom_strategy_last(size=size, app_type=app_type, ab_code=ab_code) return bottom_data # 视频状态过滤采用离线定时过滤方案 # 状态过滤 # filter_videos = FilterVideos(app_type=app_type, video_ids=data) # filtered_data = filter_videos.filter_video_status(video_ids=data) if len(data) > size: random_data = numpy.random.choice(data, size, False) else: random_data = data bottom_data = [{'videoId': int(item), 'pushFrom': 'bottom_strategy', 'abCode': ab_code} for item in random_data] return bottom_data def bottom_strategy_last(size, app_type, ab_code): """ 兜底策略: 从兜底视频中随机获取视频，进行状态过滤后的视频 :param size: 需要获取的视频数 :param app_type: 产品标识 type-int :param ab_code: abCode :return: """ redis_helper = RedisHelper() bottom_data = redis_helper.get_data_zset_with_index(key_name=config_.BOTTOM_KEY_NAME, start=0, end=-1) random_data = numpy.random.choice(bottom_data, size * 30, False) # 视频状态过滤采用离线定时过滤方案 # 状态过滤 # filter_videos = FilterVideos(app_type=app_type, video_ids=random_data) # filtered_data = filter_videos.filter_video_status(video_ids=random_data) bottom_data = [{'videoId': int(video_id), 'pushFrom': 'bottom_strategy_last', 'abCode': ab_code} for video_id in random_data[:size]] return bottom_data if __name__ == '__main__': d_test = [[{'videoId': 3674236, 'rovScore': 99.24105262298141, 'pushFrom': 'recall_pool', 'abCode': 10000}, {'videoId': 1915009, 'rovScore': 99.248872388032, 'pushFrom': 'recall_pool', 'abCode': 10000}, {'videoId': 9033859, 'rovScore': 99.21956695197761, 'pushFrom': 'recall_pool', 'abCode': 10000}, {'videoId': 4258137, 'rovScore': 99.24737622823497, 'pushFrom': 'recall_pool', 'abCode': 10000}, {'videoId': 9034962, 'rovScore': 99.18993382219318, 'pushFrom': 'recall_pool', 'abCode': 10000}, {'videoId': 1922051, 'rovScore': 99.2351969813565, 'pushFrom': 'recall_pool', 'abCode': 10000}, {'videoId': 7829308, 'rovScore': 99.25465474490638, 'pushFrom': 'recall_pool', 'abCode': 10000}, {'videoId': 3247671, 'rovScore': 99.24601245746983, 'pushFrom': 'recall_pool', 'abCode': 10000}, {'videoId': 5831941, 'rovScore': 99.16776814766304, 'pushFrom': 'recall_pool', 'abCode': 10000}, {'videoId': 7837973, 'rovScore': 99.253749334822, 'pushFrom': 'recall_pool', 'abCode': 10000}], [{'videoId': 9035245, 'flowPool': '1#1#1#1636085384424', 'rovScore': 1.0, 'pushFrom': 'flow_pool', 'abCode': 10000}, {'videoId': 9034828, 'flowPool': '1#1#1#1636090368461', 'rovScore': 1.0, 'pushFrom': 'flow_pool', 'abCode': 10000}, {'videoId': 9035244, 'flowPool': '1#1#1#1636085467105', 'rovScore': 1.0, 'pushFrom': 'flow_pool', 'abCode': 10000}, {'videoId': 9035237, 'flowPool': '1#1#1#1636086478074', 'rovScore': 1.0, 'pushFrom': 'flow_pool', 'abCode': 10000}]] data = { 'rov_pool_recall': d_test[0], 'flow_pool_recall': d_test[1] } res = video_rank(data, size=10) for item in res: print(item)