import json import traceback import datetime from utils import RedisHelper from config import set_config from log import Log log_ = Log() config_ = set_config() redis_helper = RedisHelper() def get_params(ab_exp_info, ab_test_code): """ 根据实验分组给定对应的参数 :param ab_exp_info: AB实验组参数 :param ab_test_code: 用户对应的ab组 :return: """ abtest_id, abtest_config_tag = None, None ad_abtest_id_list = [key.split('-')[0] for key in config_.AD_ABTEST_CONFIG] # 获取广告实验配置 config_value_dict = {} if ab_exp_info: ab_exp_list = ab_exp_info.get('ab_test002', None) if ab_exp_list: for ab_item in ab_exp_list: ab_exp_code = ab_item.get('abExpCode', None) if not ab_exp_code: continue if ab_exp_code in ad_abtest_id_list: config_value = ab_item.get('configValue', None) if config_value: config_value_dict[str(ab_exp_code)] = eval(str(config_value)) if len(config_value_dict) > 0: for ab_exp_code, config_value in config_value_dict.items(): for tag, value in config_value.items(): if ab_test_code in value: abtest_id = ab_exp_code abtest_config_tag = tag break return abtest_id, abtest_config_tag def get_threshold(abtest_id, abtest_config_tag, ab_test_code, mid_group, care_model_status, abtest_param): """获取对应的阈值""" # 判断是否是关怀模式实验 care_model_status_param = abtest_param.get('care_model_status_param', None) care_model_ab_mid_group = abtest_param.get('care_model_ab_mid_group', []) if care_model_status_param is None: # 无关怀模式实验 threshold_key_name_prefix = config_.KEY_NAME_PREFIX_AD_THRESHOLD else: # 关怀模式实验 if care_model_status is None or len(care_model_ab_mid_group) == 0 or care_model_status == 'null': # 参数缺失，走默认 threshold_key_name_prefix = config_.KEY_NAME_PREFIX_AD_THRESHOLD elif int(care_model_status) == int(care_model_status_param) and mid_group in care_model_ab_mid_group: # 实验匹配，获取对应的阈值 threshold_key_name_prefix = config_.KEY_NAME_PREFIX_AD_THRESHOLD_CARE_MODEL else: threshold_key_name_prefix = config_.KEY_NAME_PREFIX_AD_THRESHOLD threshold_key_name = f"{threshold_key_name_prefix}{abtest_id}:{abtest_config_tag}:{ab_test_code}:{mid_group}" threshold = redis_helper.get_data_from_redis(key_name=threshold_key_name) if threshold is None: threshold = 0 else: threshold = float(threshold) return threshold def predict_with_rate_process(now_date, video_id, abtest_param, abtest_id, abtest_config_tag, ab_test_code, care_model_status, mid_group): now_dt = datetime.datetime.strftime(now_date, '%Y%m%d') user_data_key = abtest_param['user'].get('data') user_rule_key = abtest_param['user'].get('rule') video_data_key = abtest_param['video'].get('data') # 获取用户组分享率 group_share_rate_key = f"{config_.KEY_NAME_PREFIX_AD_GROUP}{user_data_key}:{user_rule_key}:{now_dt}" if not redis_helper.key_exists(group_share_rate_key): redis_dt = datetime.datetime.strftime(now_date - datetime.timedelta(days=1), '%Y%m%d') group_share_rate_key = f"{config_.KEY_NAME_PREFIX_AD_GROUP}{user_data_key}:{user_rule_key}:{redis_dt}" group_share_rate = redis_helper.get_score_with_value(key_name=group_share_rate_key, value=mid_group) # 获取视频分享率 video_share_rate_key = f"{config_.KEY_NAME_PREFIX_AD_VIDEO}{video_data_key}:{now_dt}" if not redis_helper.key_exists(video_share_rate_key): redis_dt = datetime.datetime.strftime(now_date - datetime.timedelta(days=1), '%Y%m%d') video_share_rate_key = f"{config_.KEY_NAME_PREFIX_AD_VIDEO}{video_data_key}:{redis_dt}" video_share_rate = redis_helper.get_score_with_value(key_name=video_share_rate_key, value=int(video_id)) if video_share_rate is None: video_share_rate = redis_helper.get_score_with_value(key_name=video_share_rate_key, value=-1) # 计算 mid-video 分享率 if group_share_rate is None or video_share_rate is None: return None mid_video_predict_res = float(group_share_rate) * float(video_share_rate) # 获取对应的阈值 threshold = get_threshold( abtest_id=abtest_id, abtest_config_tag=abtest_config_tag, ab_test_code=ab_test_code, mid_group=mid_group, care_model_status=care_model_status, abtest_param=abtest_param ) # 阈值判断 if mid_video_predict_res > threshold: # 大于阈值，出广告 ad_predict = 2 else: # 否则，不出广告 ad_predict = 1 result = { 'mid_group': mid_group, 'group_share_rate': group_share_rate, 'video_share_rate': video_share_rate, 'mid_video_predict_res': mid_video_predict_res, 'threshold': threshold, 'ad_predict': ad_predict } return result def predict_mid_video_res(now_date, mid, video_id, abtest_param, abtest_id, abtest_config_tag, ab_test_code, care_model_status, app_type): # now_dt = datetime.datetime.strftime(now_date, '%Y%m%d') # user_data_key = abtest_param['user'].get('data') # user_rule_key = abtest_param['user'].get('rule') # video_data_key = abtest_param['video'].get('data') group_class_key = abtest_param.get('group_class_key') no_ad_mid_group_list = abtest_param.get('no_ad_mid_group_list', []) no_ad_group_with_video_mapping = abtest_param.get('no_ad_group_with_video_mapping', {}) # 判断mid所属分组 mid_group_key_name = f"{config_.KEY_NAME_PREFIX_MID_GROUP}{group_class_key}:{mid}" mid_group = redis_helper.get_data_from_redis(key_name=mid_group_key_name) if mid_group is None: mid_group = 'mean_group' # 判断用户是否在免广告用户组列表中 if mid_group in no_ad_mid_group_list: # 在免广告用户组列表中，则不出广告 ad_predict = 1 result = { 'mid_group': mid_group, 'ad_predict': ad_predict, 'no_ad_strategy': 'no_ad_mid_group' } elif mid_group in no_ad_group_with_video_mapping: # 用户组在特定内容不出广告设置中 # 获取对应的特定内容 video_mapping_key_list = no_ad_group_with_video_mapping.get(mid_group, []) no_ad_videos = redis_helper.get_data_from_redis(key_name=f"{config_.KEY_NAME_PREFIX_NO_AD_VIDEOS}{app_type}") if no_ad_videos is not None: no_ad_videos = json.loads(no_ad_videos) else: no_ad_videos = {} no_ad_video_list = [] for video_mapping_key in video_mapping_key_list: no_ad_video_list.extend(no_ad_videos.get(video_mapping_key, [])) # 判断此次请求视频是否在免广告视频列表中 if video_id in no_ad_video_list: # 在，则不出广告 ad_predict = 1 result = { 'mid_group': mid_group, 'ad_predict': ad_predict, 'no_ad_strategy': 'no_ad_mid_group_with_video' } else: result = predict_with_rate_process( now_date=now_date, video_id=video_id, abtest_param=abtest_param, abtest_id=abtest_id, abtest_config_tag=abtest_config_tag, ab_test_code=ab_test_code, care_model_status=care_model_status, mid_group=mid_group) else: result = predict_with_rate_process( now_date=now_date, video_id=video_id, abtest_param=abtest_param, abtest_id=abtest_id, abtest_config_tag=abtest_config_tag, ab_test_code=ab_test_code, care_model_status=care_model_status, mid_group=mid_group) return result def predict_mid_video_res_with_add(now_date, mid, video_id, abtest_param, abtest_id, abtest_config_tag, ab_test_code, care_model_status): now_dt = datetime.datetime.strftime(now_date, '%Y%m%d') # 判断mid所属分组 group_class_key = abtest_param.get('group_class_key') mid_group_key_name = f"{config_.KEY_NAME_PREFIX_MID_GROUP}{group_class_key}:{mid}" mid_group = redis_helper.get_data_from_redis(key_name=mid_group_key_name) if mid_group is None: mid_group = 'mean_group' # 判断用户是否在免广告用户组列表中 no_ad_mid_group_list = abtest_param.get('no_ad_mid_group_list', []) if mid_group in no_ad_mid_group_list: # 在免广告用户组列表中，则不出广告 ad_predict = 1 result = { 'mid_group': mid_group, 'ad_predict': ad_predict } else: # 获取用户组出广告后分享的概率 share_user_data_key = abtest_param['share']['user'].get('data') share_user_rule_key = abtest_param['share']['user'].get('rule') group_share_rate_key = \ f"{config_.KEY_NAME_PREFIX_AD_GROUP}{share_user_data_key}:{share_user_rule_key}:{now_dt}" if not redis_helper.key_exists(group_share_rate_key): redis_dt = datetime.datetime.strftime(now_date - datetime.timedelta(days=1), '%Y%m%d') group_share_rate_key = \ f"{config_.KEY_NAME_PREFIX_AD_GROUP}{share_user_data_key}:{share_user_rule_key}:{redis_dt}" group_share_rate = redis_helper.get_score_with_value(key_name=group_share_rate_key, value=mid_group) # 获取视频出广告后分享的概率 share_video_data_key = abtest_param['share']['video'].get('data') video_share_rate_key = f"{config_.KEY_NAME_PREFIX_AD_VIDEO}{share_video_data_key}:{now_dt}" if not redis_helper.key_exists(video_share_rate_key): redis_dt = datetime.datetime.strftime(now_date - datetime.timedelta(days=1), '%Y%m%d') video_share_rate_key = f"{config_.KEY_NAME_PREFIX_AD_VIDEO}{share_video_data_key}:{redis_dt}" video_share_rate = redis_helper.get_score_with_value(key_name=video_share_rate_key, value=int(video_id)) if video_share_rate is None: video_share_rate = redis_helper.get_score_with_value(key_name=video_share_rate_key, value=-1) # 获取用户组出广告后不直接跳出的概率 out_user_data_key = abtest_param['out']['user'].get('data') out_user_rule_key = abtest_param['out']['user'].get('rule') group_out_rate_key = \ f"{config_.KEY_NAME_PREFIX_AD_GROUP}{out_user_data_key}:{out_user_rule_key}:{now_dt}" if not redis_helper.key_exists(group_out_rate_key): redis_dt = datetime.datetime.strftime(now_date - datetime.timedelta(days=1), '%Y%m%d') group_out_rate_key = \ f"{config_.KEY_NAME_PREFIX_AD_GROUP}{out_user_data_key}:{out_user_rule_key}:{redis_dt}" group_out_rate = redis_helper.get_score_with_value(key_name=group_out_rate_key, value=mid_group) # 获取视频出广告后不直接跳出的概率 out_video_data_key = abtest_param['out']['video'].get('data') video_out_rate_key = f"{config_.KEY_NAME_PREFIX_AD_VIDEO}{out_video_data_key}:{now_dt}" if not redis_helper.key_exists(video_out_rate_key): redis_dt = datetime.datetime.strftime(now_date - datetime.timedelta(days=1), '%Y%m%d') video_out_rate_key = f"{config_.KEY_NAME_PREFIX_AD_VIDEO}{out_video_data_key}:{redis_dt}" video_out_rate = redis_helper.get_score_with_value(key_name=video_out_rate_key, value=int(video_id)) if video_out_rate is None: video_out_rate = redis_helper.get_score_with_value(key_name=video_out_rate_key, value=-1) # 计算 mid-video 预测值 if group_share_rate is None or video_share_rate is None or group_out_rate is None or video_out_rate is None: return None # 加权融合 share_weight = abtest_param['mix_param']['share_weight'] out_weight = abtest_param['mix_param']['out_weight'] group_rate = share_weight * float(group_share_rate) + out_weight * float(group_out_rate) video_rate = share_weight * float(video_share_rate) + out_weight * float(video_out_rate) mid_video_predict_res = group_rate * video_rate # 获取对应的阈值 threshold = get_threshold( abtest_id=abtest_id, abtest_config_tag=abtest_config_tag, ab_test_code=ab_test_code, mid_group=mid_group, care_model_status=care_model_status, abtest_param=abtest_param ) # 阈值判断 if mid_video_predict_res > threshold: # 大于阈值，出广告 ad_predict = 2 else: # 否则，不出广告 ad_predict = 1 result = { 'mid_group': mid_group, 'group_share_rate': group_share_rate, 'video_share_rate': video_share_rate, 'group_out_rate': group_out_rate, 'video_out_rate': video_out_rate, 'group_rate': group_rate, 'video_rate': video_rate, 'mid_video_predict_res': mid_video_predict_res, 'threshold': threshold, 'ad_predict': ad_predict} return result def predict_mid_video_res_with_multiply(now_date, mid, video_id, abtest_param, abtest_id, abtest_config_tag, ab_test_code, care_model_status): now_dt = datetime.datetime.strftime(now_date, '%Y%m%d') # 判断mid所属分组 group_class_key = abtest_param.get('group_class_key') mid_group_key_name = f"{config_.KEY_NAME_PREFIX_MID_GROUP}{group_class_key}:{mid}" mid_group = redis_helper.get_data_from_redis(key_name=mid_group_key_name) if mid_group is None: mid_group = 'mean_group' # 判断用户是否在免广告用户组列表中 no_ad_mid_group_list = abtest_param.get('no_ad_mid_group_list', []) if mid_group in no_ad_mid_group_list: # 在免广告用户组列表中，则不出广告 ad_predict = 1 result = { 'mid_group': mid_group, 'ad_predict': ad_predict } else: # 获取用户组出广告后分享的概率 share_user_data_key = abtest_param['share']['user'].get('data') share_user_rule_key = abtest_param['share']['user'].get('rule') group_share_rate_key = \ f"{config_.KEY_NAME_PREFIX_AD_GROUP}{share_user_data_key}:{share_user_rule_key}:{now_dt}" if not redis_helper.key_exists(group_share_rate_key): redis_dt = datetime.datetime.strftime(now_date - datetime.timedelta(days=1), '%Y%m%d') group_share_rate_key = \ f"{config_.KEY_NAME_PREFIX_AD_GROUP}{share_user_data_key}:{share_user_rule_key}:{redis_dt}" group_share_rate = redis_helper.get_score_with_value(key_name=group_share_rate_key, value=mid_group) # 获取视频出广告后分享的概率 share_video_data_key = abtest_param['share']['video'].get('data') video_share_rate_key = f"{config_.KEY_NAME_PREFIX_AD_VIDEO}{share_video_data_key}:{now_dt}" if not redis_helper.key_exists(video_share_rate_key): redis_dt = datetime.datetime.strftime(now_date - datetime.timedelta(days=1), '%Y%m%d') video_share_rate_key = f"{config_.KEY_NAME_PREFIX_AD_VIDEO}{share_video_data_key}:{redis_dt}" video_share_rate = redis_helper.get_score_with_value(key_name=video_share_rate_key, value=int(video_id)) if video_share_rate is None: video_share_rate = redis_helper.get_score_with_value(key_name=video_share_rate_key, value=-1) # 获取用户组出广告后不直接跳出的概率 out_user_data_key = abtest_param['out']['user'].get('data') out_user_rule_key = abtest_param['out']['user'].get('rule') group_out_rate_key = \ f"{config_.KEY_NAME_PREFIX_AD_GROUP}{out_user_data_key}:{out_user_rule_key}:{now_dt}" if not redis_helper.key_exists(group_out_rate_key): redis_dt = datetime.datetime.strftime(now_date - datetime.timedelta(days=1), '%Y%m%d') group_out_rate_key = \ f"{config_.KEY_NAME_PREFIX_AD_GROUP}{out_user_data_key}:{out_user_rule_key}:{redis_dt}" group_out_rate = redis_helper.get_score_with_value(key_name=group_out_rate_key, value=mid_group) # 获取视频出广告后不直接跳出的概率 out_video_data_key = abtest_param['out']['video'].get('data') video_out_rate_key = f"{config_.KEY_NAME_PREFIX_AD_VIDEO}{out_video_data_key}:{now_dt}" if not redis_helper.key_exists(video_out_rate_key): redis_dt = datetime.datetime.strftime(now_date - datetime.timedelta(days=1), '%Y%m%d') video_out_rate_key = f"{config_.KEY_NAME_PREFIX_AD_VIDEO}{out_video_data_key}:{redis_dt}" video_out_rate = redis_helper.get_score_with_value(key_name=video_out_rate_key, value=int(video_id)) if video_out_rate is None: video_out_rate = redis_helper.get_score_with_value(key_name=video_out_rate_key, value=-1) # 计算 mid-video 预测值 if group_share_rate is None or video_share_rate is None or group_out_rate is None or video_out_rate is None: return None # 乘积融合 group_rate = float(group_share_rate) * float(group_out_rate) video_rate = float(video_share_rate) * float(video_out_rate) mid_video_predict_res = group_rate * video_rate # 获取对应的阈值 threshold = get_threshold( abtest_id=abtest_id, abtest_config_tag=abtest_config_tag, ab_test_code=ab_test_code, mid_group=mid_group, care_model_status=care_model_status, abtest_param=abtest_param ) # 阈值判断 if mid_video_predict_res > threshold: # 大于阈值，出广告 ad_predict = 2 else: # 否则，不出广告 ad_predict = 1 result = { 'mid_group': mid_group, 'group_share_rate': group_share_rate, 'video_share_rate': video_share_rate, 'group_out_rate': group_out_rate, 'video_out_rate': video_out_rate, 'group_rate': group_rate, 'video_rate': video_rate, 'mid_video_predict_res': mid_video_predict_res, 'threshold': threshold, 'ad_predict': ad_predict} return result def ad_recommend_predict(app_type, mid, video_id, ab_exp_info, ab_test_code, care_model_status): """ 广告推荐预测 :param app_type: app_type :param mid: mid :param video_id: video_id :param ab_exp_info: AB实验组参数 :param ab_test_code: 用户对应的ab组 :param care_model_status: 用户关怀模式状态 1-未开启，2-开启 :return: ad_predict, type-int, 1-不发放广告,2-发放广告 """ try: now_date = datetime.datetime.today() # now_dt = datetime.datetime.strftime(now_date, '%Y%m%d') now_h = datetime.datetime.now().hour if 0 <= now_h < 8: # 00:00 - 08:00 不出广告 ad_predict = 1 result = { 'now_h': now_h, 'ad_predict': ad_predict } return result # 获取实验参数 abtest_id, abtest_config_tag = get_params(ab_exp_info=ab_exp_info, ab_test_code=ab_test_code) if abtest_id is None or abtest_config_tag is None: return None abtest_param = config_.AD_ABTEST_CONFIG.get(f'{abtest_id}-{abtest_config_tag}') if abtest_param is None: return None threshold_mix_func = abtest_param.get('threshold_mix_func', None) if threshold_mix_func == 'add': result = predict_mid_video_res_with_add( now_date=now_date, mid=mid, video_id=video_id, abtest_param=abtest_param, abtest_id=abtest_id, abtest_config_tag=abtest_config_tag, ab_test_code=ab_test_code, care_model_status=care_model_status ) elif threshold_mix_func == 'multiply': result = predict_mid_video_res_with_multiply( now_date=now_date, mid=mid, video_id=video_id, abtest_param=abtest_param, abtest_id=abtest_id, abtest_config_tag=abtest_config_tag, ab_test_code=ab_test_code, care_model_status=care_model_status ) else: result = predict_mid_video_res( now_date=now_date, mid=mid, video_id=video_id, abtest_param=abtest_param, abtest_id=abtest_id, abtest_config_tag=abtest_config_tag, ab_test_code=ab_test_code, care_model_status=care_model_status, app_type=app_type ) # user_data_key = abtest_param['user'].get('data') # user_rule_key = abtest_param['user'].get('rule') # video_data_key = abtest_param['video'].get('data') # group_class_key = abtest_param.get('group_class_key') # no_ad_mid_group_list = abtest_param.get('no_ad_mid_group_list', []) # # # 判断mid所属分组 # mid_group_key_name = f"{config_.KEY_NAME_PREFIX_MID_GROUP}{group_class_key}:{mid}" # mid_group = redis_helper.get_data_from_redis(key_name=mid_group_key_name) # if mid_group is None: # mid_group = 'mean_group' # # # 判断用户是否在免广告用户组列表中 # if mid_group in no_ad_mid_group_list: # # 在免广告用户组列表中，则不出广告 # ad_predict = 1 # result = { # 'mid_group': mid_group, # 'ad_predict': ad_predict # } # else: # # 获取用户组分享率 # group_share_rate_key = f"{config_.KEY_NAME_PREFIX_AD_GROUP}{user_data_key}:{user_rule_key}:{now_dt}" # if not redis_helper.key_exists(group_share_rate_key): # redis_dt = datetime.datetime.strftime(now_date - datetime.timedelta(days=1), '%Y%m%d') # group_share_rate_key = f"{config_.KEY_NAME_PREFIX_AD_GROUP}{user_data_key}:{user_rule_key}:{redis_dt}" # group_share_rate = redis_helper.get_score_with_value(key_name=group_share_rate_key, value=mid_group) # # 获取视频分享率 # video_share_rate_key = f"{config_.KEY_NAME_PREFIX_AD_VIDEO}{video_data_key}:{now_dt}" # if not redis_helper.key_exists(video_share_rate_key): # redis_dt = datetime.datetime.strftime(now_date - datetime.timedelta(days=1), '%Y%m%d') # video_share_rate_key = f"{config_.KEY_NAME_PREFIX_AD_VIDEO}{video_data_key}:{redis_dt}" # video_share_rate = redis_helper.get_score_with_value(key_name=video_share_rate_key, value=int(video_id)) # if video_share_rate is None: # video_share_rate = redis_helper.get_score_with_value(key_name=video_share_rate_key, value=-1) # # # 计算 mid-video 分享率 # if group_share_rate is None or video_share_rate is None: # return None # mid_video_share_rate = float(group_share_rate) * float(video_share_rate) # # # 获取对应的阈值 # threshold = get_threshold( # abtest_id=abtest_id, # abtest_config_tag=abtest_config_tag, # ab_test_code=ab_test_code, # mid_group=mid_group, # care_model_status=care_model_status, # abtest_param=abtest_param # ) # # 阈值判断 # if mid_video_share_rate > threshold: # # 大于阈值，出广告 # ad_predict = 2 # else: # # 否则，不出广告 # ad_predict = 1 # result = { # 'mid_group': mid_group, # 'group_share_rate': group_share_rate, # 'video_share_rate': video_share_rate, # 'mid_video_share_rate': mid_video_share_rate, # 'threshold': threshold, # 'ad_predict': ad_predict} return result except Exception as e: log_.error(traceback.format_exc()) return None def ad_recommend_predict_with_roi(app_type, mid, video_id, ads, arpu, roi_param): """ 广告推荐预测 :param app_type: app_type :param mid: mid :param video_id: video_id :param ads: 需要发放广告列表 list :param arpu: 上一周期arpu值 :param roi_param: 计算roi使用参数 :return: ad_predict, type-int, 1-不发放广告,2-发放广告 """ try: now_date = datetime.datetime.today() now_dt = datetime.datetime.strftime(now_date, '%Y%m%d') ad_info = ads[0] ad_id = ad_info['adId'] ad_type = ad_info['adType'] ecpm = float(ad_info['ecpm']) # 获取参数 params = config_.PARAMS_NEW_STRATEGY[int(app_type)] # 判断mid所属分组 group_class_key = params.get('group_class_key') mid_group_key_name = f"{config_.KEY_NAME_PREFIX_MID_GROUP}{group_class_key}:{mid}" mid_group = redis_helper.get_data_from_redis(key_name=mid_group_key_name) if mid_group is None: mid_group = 'mean_group' # 获取用户组出广告后分享的概率 share_user_data_key = params['user'].get('data') share_user_rule_key = params['user'].get('rule') group_share_rate_key_with_ad = \ f"{config_.KEY_NAME_PREFIX_GROUP_WITH_AD}{share_user_data_key}:{share_user_rule_key}:{now_dt}" if not redis_helper.key_exists(group_share_rate_key_with_ad): redis_dt = datetime.datetime.strftime(now_date - datetime.timedelta(days=1), '%Y%m%d') group_share_rate_key_with_ad = \ f"{config_.KEY_NAME_PREFIX_GROUP_WITH_AD}{share_user_data_key}:{share_user_rule_key}:{redis_dt}" group_share_rate_with_ad = redis_helper.get_score_with_value(key_name=group_share_rate_key_with_ad, value=mid_group) # 获取视频出广告后分享的概率 share_video_data_key = params['video'].get('data') video_share_rate_key_with_ad = f"{config_.KEY_NAME_PREFIX_VIDEO_WITH_AD}{share_video_data_key}:{now_dt}" if not redis_helper.key_exists(video_share_rate_key_with_ad): redis_dt = datetime.datetime.strftime(now_date - datetime.timedelta(days=1), '%Y%m%d') video_share_rate_key_with_ad = f"{config_.KEY_NAME_PREFIX_VIDEO_WITH_AD}{share_video_data_key}:{redis_dt}" video_share_rate_with_ad = redis_helper.get_score_with_value(key_name=video_share_rate_key_with_ad, value=int(video_id)) if video_share_rate_with_ad is None: video_share_rate_with_ad = redis_helper.get_score_with_value(key_name=video_share_rate_key_with_ad, value=-1) # 获取用户组不出广告后分享的概率 group_share_rate_key_no_ad = \ f"{config_.KEY_NAME_PREFIX_GROUP_NO_AD}{share_user_data_key}:{share_user_rule_key}:{now_dt}" if not redis_helper.key_exists(group_share_rate_key_no_ad): redis_dt = datetime.datetime.strftime(now_date - datetime.timedelta(days=1), '%Y%m%d') group_share_rate_key_no_ad = \ f"{config_.KEY_NAME_PREFIX_GROUP_NO_AD}{share_user_data_key}:{share_user_rule_key}:{redis_dt}" group_share_rate_no_ad = redis_helper.get_score_with_value(key_name=group_share_rate_key_no_ad, value=mid_group) # 获取视频不出广告后分享的概率 video_share_rate_key_no_ad = f"{config_.KEY_NAME_PREFIX_VIDEO_NO_AD}{share_video_data_key}:{now_dt}" if not redis_helper.key_exists(video_share_rate_key_no_ad): redis_dt = datetime.datetime.strftime(now_date - datetime.timedelta(days=1), '%Y%m%d') video_share_rate_key_no_ad = f"{config_.KEY_NAME_PREFIX_VIDEO_NO_AD}{share_video_data_key}:{redis_dt}" video_share_rate_no_ad = redis_helper.get_score_with_value(key_name=video_share_rate_key_no_ad, value=int(video_id)) if video_share_rate_no_ad is None: video_share_rate_no_ad = redis_helper.get_score_with_value(key_name=video_share_rate_key_no_ad, value=-1) if group_share_rate_with_ad is None or video_share_rate_with_ad is None \ or group_share_rate_no_ad is None or video_share_rate_no_ad is None: return None # 计算此次请求出广告后分享的概率 share_rate_with_ad = float(group_share_rate_with_ad) * float(video_share_rate_with_ad) # 计算此次请求不出广告分享的概率 share_rate_no_ad = float(group_share_rate_no_ad) * float(video_share_rate_no_ad) # 计算此次请求出广告的收入增益 roi_ad = ecpm / 1000 - float(roi_param) * float(arpu) * (share_rate_no_ad - share_rate_with_ad) # 收入增益判断 if roi_ad > 0: # 大于0，出广告 ad_predict = 2 else: # 否则，不出广告 ad_predict = 1 result = { 'arpu': arpu, 'roi_param': roi_param, 'ad_id': ad_id, 'ad_type': ad_type, 'mid_group': mid_group, 'group_share_rate_with_ad': group_share_rate_with_ad, 'video_share_rate_with_ad': video_share_rate_with_ad, 'group_share_rate_no_ad': group_share_rate_no_ad, 'video_share_rate_no_ad': video_share_rate_no_ad, 'share_rate_with_ad': share_rate_with_ad, 'share_rate_no_ad': share_rate_no_ad, 'roi_ad': roi_ad, 'ad_predict': ad_predict } return result except Exception as e: log_.error(traceback.format_exc()) return None