import pandas as pd import math import traceback from functools import reduce from odps import ODPS from threading import Timer from datetime import datetime, timedelta from get_data import get_data_from_odps from db_helper import RedisHelper from my_utils import filter_video_status, check_table_partition_exits, filter_video_status_app, send_msg_to_feishu from my_config import set_config from log import Log config_, _ = set_config() log_ = Log() features = [ 'apptype', 'videoid', 'lastonehour_preview', # 过去1小时预曝光人数 - 区分地域 'lastonehour_view', # 过去1小时曝光人数 - 区分地域 'lastonehour_play', # 过去1小时播放人数 - 区分地域 'lastonehour_share', # 过去1小时分享人数 - 区分地域 'lastonehour_return', # 过去1小时分享，过去1小时回流人数 - 区分地域 'lastonehour_preview_total', # 过去1小时预曝光次数 - 区分地域 'lastonehour_view_total', # 过去1小时曝光次数 - 区分地域 'lastonehour_play_total', # 过去1小时播放次数 - 区分地域 'lastonehour_share_total', # 过去1小时分享次数 - 区分地域 'platform_return', 'lastonehour_show', # 不区分地域 'lasttwohour_share', # h-2小时分享人数 'lasttwohour_return_now', # h-2分享，过去1小时回流人数 'lasttwohour_return', # h-2分享，h-2回流人数 'lastthreehour_share', # h-3小时分享人数 'lastthreehour_return_now', # h-3分享，过去1小时回流人数 'lastthreehour_return', # h-3分享，h-3回流人数 'lastonehour_return_new', # 过去1小时分享，过去1小时回流人数（回流统计为对应地域分享带回的回流，分享限制地域，回流不限制地域） 'lasttwohour_return_now_new', # h-2分享，过去1小时回流人数（回流统计为对应地域分享带回的回流，分享限制地域，回流不限制地域） 'lasttwohour_return_new', # h-2分享，h-2回流人数（回流统计为对应地域分享带回的回流，分享限制地域，回流不限制地域） 'lastthreehour_return_now_new', # h-3分享，过去1小时回流人数（回流统计为对应地域分享带回的回流，分享限制地域，回流不限制地域） 'lastthreehour_return_new', # h-3分享，h-3回流人数（回流统计为对应地域分享带回的回流，分享限制地域，回流不限制地域） 'platform_return_new', # 平台分发回流（回流统计为对应地域分享带回的回流，分享限制地域，回流不限制地域） ] def h_data_check(project, table, now_date): """检查数据是否准备好""" odps = ODPS( access_id=config_.ODPS_CONFIG['ACCESSID'], secret_access_key=config_.ODPS_CONFIG['ACCESSKEY'], project=project, endpoint=config_.ODPS_CONFIG['ENDPOINT'], connect_timeout=3000, read_timeout=500000, pool_maxsize=1000, pool_connections=1000 ) try: dt = datetime.strftime(now_date, '%Y%m%d%H') check_res = check_table_partition_exits(date=dt, project=project, table=table) if check_res: sql = f'select * from {project}.{table} where dt = {dt}' with odps.execute_sql(sql=sql).open_reader() as reader: data_count = reader.count else: data_count = 0 except Exception as e: data_count = 0 return data_count def h_rank_bottom(now_date, now_h, rule_params): """未按时更新数据，用上一小时结果作为当前小时的数据""" redis_helper = RedisHelper() if now_h == 0: redis_dt = datetime.strftime(now_date - timedelta(days=1), '%Y%m%d') redis_h = 23 else: redis_dt = datetime.strftime(now_date, '%Y%m%d') redis_h = now_h - 1 key_prefix = config_.RECALL_KEY_NAME_PREFIX_BY_H_H for param in rule_params.get('params_list'): data_key = param.get('data') rule_key = param.get('rule') log_.info(f"data_key = {data_key}, rule_key = {rule_key}") key_name = f"{key_prefix}{data_key}:{rule_key}:{redis_dt}:{redis_h}" initial_data = redis_helper.get_all_data_from_zset(key_name=key_name, with_scores=True) if initial_data is None: initial_data = [] final_data = dict() for video_id, score in initial_data: final_data[video_id] = score # 存入对应的redis final_key_name = \ f"{key_prefix}{data_key}:{rule_key}:{datetime.strftime(now_date, '%Y%m%d')}:{now_h}" if len(final_data) > 0: redis_helper.add_data_with_zset(key_name=final_key_name, data=final_data, expire_time=2 * 3600) def get_feature_data(project, table, now_date): """获取特征数据""" dt = datetime.strftime(now_date, '%Y%m%d%H') records = get_data_from_odps(date=dt, project=project, table=table) feature_data = [] for record in records: item = {} for feature_name in features: item[feature_name] = record[feature_name] feature_data.append(item) feature_df = pd.DataFrame(feature_data) return feature_df def cal_score(df, param): # score = sharerate * backrate * LOG(lastonehour_return + 1) * K2 # sharerate = lastonehour_share / (lastonehour_play + 1000) # backrate = lastonehour_return / (lastonehour_share + 10) # ctr = lastonehour_play / (lastonehour_show + 1000), 对ctr限最大值：K2 = 0.6 if ctr > 0.6 else ctr df = df.fillna(0) df['share_rate'] = df['lastonehour_share'] / (df['lastonehour_play'] + 1000) df['back_rate'] = df['lastonehour_return'] / (df['lastonehour_share'] + 10) df['log_back'] = (df['lastonehour_return'] + 1).apply(math.log) if param.get('view_type', None) == 'video-show': df['ctr'] = df['lastonehour_play'] / (df['lastonehour_show'] + 1000) else: df['ctr'] = df['lastonehour_play'] / (df['lastonehour_preview'] + 1000) df['K2'] = df['ctr'].apply(lambda x: 0.6 if x > 0.6 else x) df['platform_return_rate'] = df['platform_return'] / df['lastonehour_return'] df['score'] = df['share_rate'] * df['back_rate'] * df['log_back'] * df['K2'] df = df.sort_values(by=['score'], ascending=False) return df def merge_df(df_left, df_right): """ df按照videoid 合并，对应特征求和 :param df_left: :param df_right: :return: """ df_merged = pd.merge(df_left, df_right, on=['videoid'], how='outer', suffixes=['_x', '_y']) df_merged.fillna(0, inplace=True) feature_list = ['videoid'] for feature in features: if feature in ['apptype', 'videoid']: continue df_merged[feature] = df_merged[f'{feature}_x'] + df_merged[f'{feature}_y'] feature_list.append(feature) return df_merged[feature_list] def merge_df_with_score(df_left, df_right): """ df 按照videoid合并，平台回流人数、回流人数、分数 分别求和 :param df_left: :param df_right: :return: """ df_merged = pd.merge(df_left, df_right, on=['videoid'], how='outer', suffixes=['_x', '_y']) df_merged.fillna(0, inplace=True) feature_list = ['videoid', 'lastonehour_return', 'platform_return', 'score'] for feature in feature_list[1:]: df_merged[feature] = df_merged[f'{feature}_x'] + df_merged[f'{feature}_y'] return df_merged[feature_list] def video_rank_h(df, now_date, now_h, rule_key, param, data_key): """ 获取符合进入召回源条件的视频 """ redis_helper = RedisHelper() log_.info(f"videos_count = {len(df)}") # videoid重复时，保留分值高 df = df.sort_values(by=['score'], ascending=False) df = df.drop_duplicates(subset=['videoid'], keep='first') df['videoid'] = df['videoid'].astype(int) # 获取符合进入召回源条件的视频 platform_return_rate = param.get('platform_return_rate', 0) h_recall_df = df[df['platform_return_rate'] > platform_return_rate] h_recall_videos = h_recall_df['videoid'].to_list() log_.info(f'h_recall videos count = {len(h_recall_videos)}') # 视频状态过滤 if data_key in ['data7', ]: filtered_videos = filter_video_status_app(h_recall_videos) else: filtered_videos = filter_video_status(h_recall_videos) log_.info('filtered_videos count = {}'.format(len(filtered_videos))) # 写入对应的redis now_dt = datetime.strftime(now_date, '%Y%m%d') h_video_ids = [] h_recall_result = {} for video_id in filtered_videos: score = h_recall_df[h_recall_df['videoid'] == video_id]['score'] h_recall_result[int(video_id)] = float(score) h_video_ids.append(int(video_id)) h_recall_key_name = \ f"{config_.RECALL_KEY_NAME_PREFIX_BY_H_H}{data_key}:{rule_key}:{now_dt}:{now_h}" if len(h_recall_result) > 0: log_.info(f"count = {len(h_recall_result)}, key = {h_recall_key_name}") redis_helper.add_data_with_zset(key_name=h_recall_key_name, data=h_recall_result, expire_time=2 * 3600) def rank_by_h(now_date, now_h, rule_params, project, table): # 获取特征数据 feature_df = get_feature_data(now_date=now_date, project=project, table=table) feature_df['apptype'] = feature_df['apptype'].astype(int) # rank data_params_item = rule_params.get('data_params') rule_params_item = rule_params.get('rule_params') for param in rule_params.get('params_list'): score_df_list = [] data_key = param.get('data') data_param = data_params_item.get(data_key) log_.info(f"data_key = {data_key}, data_param = {data_param}") rule_key = param.get('rule') rule_param = rule_params_item.get(rule_key) log_.info(f"rule_key = {rule_key}, rule_param = {rule_param}") merge_func = rule_param.get('merge_func', 1) if merge_func == 2: for apptype, weight in data_param.items(): df = feature_df[feature_df['apptype'] == apptype] # 计算score score_df = cal_score(df=df, param=rule_param) score_df['score'] = score_df['score'] * weight score_df_list.append(score_df) # 分数合并 df_merged = reduce(merge_df_with_score, score_df_list) # 更新平台回流比 df_merged['platform_return_rate'] = df_merged['platform_return'] / df_merged['lastonehour_return'] video_rank_h(df=df_merged, now_date=now_date, now_h=now_h, rule_key=rule_key, param=rule_param, data_key=data_key) else: df_list = [feature_df[feature_df['apptype'] == apptype] for apptype, _ in data_param.items()] df_merged = reduce(merge_df, df_list) score_df = cal_score(df=df_merged, param=rule_param) video_rank_h(df=score_df, now_date=now_date, now_h=now_h, rule_key=rule_key, param=rule_param, data_key=data_key) def h_timer_check(): try: project = config_.PROJECT_H_APP_TYPE table = config_.TABLE_H_APP_TYPE rule_params = config_.RULE_PARAMS_H_APP_TYPE now_date = datetime.today() log_.info(f"now_date: {datetime.strftime(now_date, '%Y%m%d%H')}") now_min = datetime.now().minute now_h = datetime.now().hour redis_helper = RedisHelper() if now_h == 0: log_.info(f'now_h = {now_h} use bottom data!') h_rank_bottom(now_date=now_date, now_h=now_h, rule_params=rule_params) log_.info(f"h_data end!") redis_helper.set_data_to_redis( key_name=f"{config_.RULE_H_DATA_STATUS}:{datetime.strftime(now_date, '%Y%m%d%H')}", value='1', expire_time=2 * 3600 ) log_.info(f"rule_h_data status update to '1' finished!") return # 查看当前小时级更新的数据是否已准备好 h_data_count = h_data_check(project=project, table=table, now_date=now_date) if h_data_count > 0: log_.info(f'h_data_count = {h_data_count}') # 数据准备好，进行更新 rank_by_h(now_date=now_date, now_h=now_h, rule_params=rule_params, project=project, table=table) log_.info(f"h_data end!") redis_helper.set_data_to_redis( key_name=f"{config_.RULE_H_DATA_STATUS}:{datetime.strftime(now_date, '%Y%m%d%H')}", value='1', expire_time=2 * 3600 ) log_.info(f"rule_h_data status update to '1' finished!") elif now_min > 40: log_.info('h_recall data is None, use bottom data!') h_rank_bottom(now_date=now_date, now_h=now_h, rule_params=rule_params) log_.info(f"h_data end!") redis_helper.set_data_to_redis( key_name=f"{config_.RULE_H_DATA_STATUS}:{datetime.strftime(now_date, '%Y%m%d%H')}", value='1', expire_time=2 * 3600 ) log_.info(f"rule_h_data status update to '1' finished!") else: # 数据没准备好，1分钟后重新检查 Timer(60, h_timer_check).start() except Exception as e: log_.error(f"不区分地域小时级数据更新失败, exception: {e}, traceback: {traceback.format_exc()}") send_msg_to_feishu( webhook=config_.FEISHU_ROBOT['server_robot'].get('webhook'), key_word=config_.FEISHU_ROBOT['server_robot'].get('key_word'), msg_text=f"rov-offline{config_.ENV_TEXT} - 不区分地域小时级数据更新失败\n" f"exception: {e}\n" f"traceback: {traceback.format_exc()}" ) if __name__ == '__main__': log_.info(f"h_data start...") h_timer_check()