rov-offline/rule_rank_h.py

import datetime
import pandas as pd
import math
from odps import ODPS
from threading import Timer
from get_data import get_data_from_odps
from utils import filter_video_status
from db_helper import RedisHelper
from config import set_config
from log import Log

config_, _ = set_config()
log_ = Log()

project = 'loghubods'
table = 'video_each_hour_update'
features = [
    'videoid',
    'lastonehour_preview',  # 过去1小时预曝光人数
    'lastonehour_view',  # 过去1小时曝光人数
    'lastonehour_play',  # 过去1小时播放人数
    'lastonehour_share',  # 过去1小时分享人数
    'lastonehour_return',  # 过去1小时分享，过去1小时回流人数
    'lastonehour_preview_total_final',  # 过去1小时预曝光次数
    'lastonehour_view_total_final',  # 过去1小时曝光次数
    'lastonehour_play_total_final',  # 过去1小时播放次数
    'lastonehour_share_total_final',  # 过去1小时分享次数
    'lastonehour_show',  # 过去1小时video_show人数
    'lastonehour_show_total_final',  # 过去1小时video_show次数
    'platform_return',
]


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.datetime.strftime(now_date, '%Y%m%d%H')
        sql = f'select * from {project}.{table} where dt = {dt}'
        with odps.execute_sql(sql=sql).open_reader() as reader:
            data_count = reader.count
    except Exception as e:
        data_count = 0
    return data_count


def get_rov_redis_key(now_date):
    # 获取rov模型结果存放key
    redis_helper = RedisHelper()
    now_dt = datetime.datetime.strftime(now_date, '%Y%m%d')
    key_name = f'{config_.RECALL_KEY_NAME_PREFIX}{now_dt}'
    if not redis_helper.key_exists(key_name=key_name):
        pre_dt = datetime.datetime.strftime(now_date - datetime.timedelta(days=1), '%Y%m%d')
        key_name = f'{config_.RECALL_KEY_NAME_PREFIX}{pre_dt}'
    return key_name


def get_feature_data(now_date):
    """获取特征数据"""
    dt = datetime.datetime.strftime(now_date, '%Y%m%d%H')
    # dt = '2022041310'
    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):
    """
    计算score
    :param df: 特征数据
    :param param: 规则参数
    :return:
    """
    # score计算公式: sharerate*backrate*logback*ctr
    # sharerate = lastonehour_share/(lastonehour_play+1000)
    # backrate = lastonehour_return/(lastonehour_share+10)
    # ctr = lastonehour_play/(lastonehour_view+1000), 对ctr限最大值：K2 = 0.6 if ctr > 0.6 else ctr
    # score = sharerate * backrate * LOG(lastonehour_return+1) * K2

    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) == 'pre-view':
        df['ctr'] = df['lastonehour_play'] / (df['lastonehour_preview'] + 1000)
    elif param.get('view_type', None) == 'video-show':
        df['ctr'] = df['lastonehour_play'] / (df['lastonehour_show'] + 1000)
    else:
        df['ctr'] = df['lastonehour_play'] / (df['lastonehour_view'] + 1000)
    df['K2'] = df['ctr'].apply(lambda x: 0.6 if x > 0.6 else x)
    df['score'] = df['share_rate'] * df['back_rate'] * df['log_back'] * df['K2']
    df['platform_return_rate'] = df['platform_return'] / df['lastonehour_return']
    df = df.sort_values(by=['score'], ascending=False)
    return df


def cal_score2(df):
    # score2计算公式: score = lastonehour_return/(lastonehour_view+1000)
    df = df.fillna(0)
    df['score'] = df['lastonehour_return'] / (df['lastonehour_view'] + 1000)
    df['platform_return_rate'] = df['platform_return'] / df['lastonehour_return']
    df = df.sort_values(by=['score'], ascending=False)
    return df


def cal_score3(df):
    # score3计算公式:
    # score = lastonehour_share_total_final/(lastonehour_view+1000)
    # + 0.03 * lastonehour_return/(lastonehour_share_total_final+1)
    df = df.fillna(0)
    df['share_rate'] = df['lastonehour_share_total_final'] / (df['lastonehour_view'] + 1000)
    df['back_rate'] = df['lastonehour_return'] / (df['lastonehour_share_total_final'] + 1)
    df['score'] = df['share_rate'] + 0.03 * df['back_rate']
    df['platform_return_rate'] = df['platform_return'] / df['lastonehour_return']
    df = df.sort_values(by=['score'], ascending=False)
    return df


def video_rank(df, now_date, now_h, rule_key, param):
    """
    获取符合进入召回源条件的视频，与每日更新的rov模型结果视频列表进行合并
    :param df:
    :param now_date:
    :param now_h:
    :param rule_key: 小时级数据进入条件
    :param param: 小时级数据进入条件参数
    :return:
    """
    # 获取rov模型结果
    redis_helper = RedisHelper()
    key_name = get_rov_redis_key(now_date=now_date)
    initial_data = redis_helper.get_all_data_from_zset(key_name=key_name, with_scores=True)
    log_.info(f'initial data count = {len(initial_data)}')

    # 获取符合进入召回源条件的视频，进入条件：小时级回流>=20 && score>=0.005
    return_count = param.get('return_count')
    score_value = param.get('score_rule')
    platform_return_rate = param.get('platform_return_rate', 0)
    h_recall_df = df[(df['lastonehour_return'] >= return_count) & (df['score'] >= score_value)
                     & (df['platform_return_rate'] >= platform_return_rate)]
    h_recall_df['videoid'] = h_recall_df['videoid'].astype(int)
    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
    filtered_videos = filter_video_status(h_recall_videos)
    log_.info('filtered_videos count = {}'.format(len(filtered_videos)))
    # 写入对应的redis
    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}{rule_key}.{datetime.datetime.strftime(now_date, '%Y%m%d')}.{now_h}"
    if len(h_recall_result) > 0:
        redis_helper.add_data_with_zset(key_name=h_recall_key_name, data=h_recall_result, expire_time=23 * 3600)
        # 清空线上过滤应用列表
        redis_helper.del_keys(key_name=f"{config_.H_VIDEO_FILER}{rule_key}")

    dup_to_redis(h_video_ids, now_date, now_h, rule_key)

    # 去重更新rov模型结果，并另存为redis中
    # initial_data_dup = {}
    # for video_id, score in initial_data:
    #     if int(video_id) not in h_video_ids:
    #         initial_data_dup[int(video_id)] = score
    # log_.info(f"initial data dup count = {len(initial_data_dup)}")
    # initial_key_name = \
    #     f"{config_.RECALL_KEY_NAME_PREFIX_DUP_H}{rule_key}.{datetime.datetime.strftime(now_date, '%Y%m%d')}.{now_h}"
    # if len(initial_data_dup) > 0:
    #     redis_helper.add_data_with_zset(key_name=initial_key_name, data=initial_data_dup, expire_time=23 * 3600)


def dup_to_redis(h_video_ids, now_date, now_h, rule_key):
    """将小时级数据与其他召回视频池去重，存入对应的redis"""
    redis_helper = RedisHelper()

    # ##### 去重小程序相对24h数据更新结果，并另存为redis中
    rule_24h_key_name = f"{config_.RECALL_KEY_NAME_PREFIX_BY_24H}rule2.{datetime.datetime.strftime(now_date, '%Y%m%d')}.{now_h}"
    if redis_helper.key_exists(key_name=rule_24h_key_name):
        rule_24h_data = redis_helper.get_all_data_from_zset(key_name=rule_24h_key_name, with_scores=True)
        log_.info(f'rule_24h data count = {len(rule_24h_data)}')
        rule_24h_dup = {}
        for video_id, score in rule_24h_data:
            if int(video_id) not in h_video_ids:
                rule_24h_dup[int(video_id)] = score
                h_video_ids.append(int(video_id))
        log_.info(f"rule_24h data dup count = {len(rule_24h_dup)}")
        rule_24h_dup_key_name = \
            f"{config_.RECALL_KEY_NAME_PREFIX_DUP_24H_H}{rule_key}.{datetime.datetime.strftime(now_date, '%Y%m%d')}.{now_h}"
        if len(rule_24h_dup) > 0:
            redis_helper.add_data_with_zset(key_name=rule_24h_dup_key_name, data=rule_24h_dup, expire_time=23 * 3600)

    # ##### 去重小程序模型更新结果，并另存为redis中
    model_key_name = get_rov_redis_key(now_date=now_date)
    model_data = redis_helper.get_all_data_from_zset(key_name=model_key_name, with_scores=True)
    log_.info(f'model data count = {len(model_data)}')
    model_data_dup = {}
    for video_id, score in model_data:
        if int(video_id) not in h_video_ids:
            model_data_dup[int(video_id)] = score
            h_video_ids.append(int(video_id))
    log_.info(f"model data dup count = {len(model_data_dup)}")
    model_data_dup_key_name = \
        f"{config_.RECALL_KEY_NAME_PREFIX_DUP_H}{rule_key}.{datetime.datetime.strftime(now_date, '%Y%m%d')}.{now_h}"
    if len(model_data_dup) > 0:
        redis_helper.add_data_with_zset(key_name=model_data_dup_key_name, data=model_data_dup, expire_time=23 * 3600)


def rank_by_h(now_date, now_h, rule_params):
    # 获取特征数据
    feature_df = get_feature_data(now_date=now_date)
    # rank
    for key, value in rule_params.items():
        log_.info(f"rule = {key}, param = {value}")
        # 计算score
        cal_score_func = value.get('cal_score_func', 0)
        if cal_score_func == 2:
            score_df = cal_score2(df=feature_df)
        elif cal_score_func == 3:
            score_df = cal_score3(df=feature_df)
        else:
            score_df = cal_score(df=feature_df, param=value)
        video_rank(df=score_df, now_date=now_date, now_h=now_h, rule_key=key, param=value)
        # to-csv
        score_filename = f"score_{key}_{datetime.datetime.strftime(now_date, '%Y%m%d%H')}.csv"
        score_df.to_csv(f'./data/{score_filename}')
        # to-logs
        log_.info({"date": datetime.datetime.strftime(now_date, '%Y%m%d%H'),
                   "redis_key_prefix": config_.RECALL_KEY_NAME_PREFIX_BY_H,
                   "rule_key": key,
                   "score_df": score_df[['videoid', 'score']]})


def h_rank_bottom(now_date, now_h, rule_key):
    """未按时更新数据，用上一小时结果作为当前小时的数据"""
    log_.info(f"rule_key = {rule_key}")
    # 获取rov模型结果
    redis_helper = RedisHelper()
    if now_h == 0:
        redis_dt = datetime.datetime.strftime(now_date - datetime.timedelta(days=1), '%Y%m%d')
        redis_h = 23
    else:
        redis_dt = datetime.datetime.strftime(now_date, '%Y%m%d')
        redis_h = now_h - 1
    key_prefix_list = [config_.RECALL_KEY_NAME_PREFIX_BY_H]
    for key_prefix in key_prefix_list:
        key_name = f"{key_prefix}{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()
        h_video_ids = []
        for video_id, score in initial_data:
            final_data[video_id] = score
            h_video_ids.append(int(video_id))
        # 存入对应的redis
        final_key_name = \
            f"{key_prefix}{rule_key}.{datetime.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=23 * 3600)
        # 清空线上过滤应用列表
        redis_helper.del_keys(key_name=f"{config_.H_VIDEO_FILER}{rule_key}")
        dup_to_redis(h_video_ids, now_date, now_h, rule_key)


def h_timer_check():
    rule_params = config_.RULE_PARAMS
    # return_count_list = [20, 10]
    now_date = datetime.datetime.today()
    log_.info(f"now_date: {datetime.datetime.strftime(now_date, '%Y%m%d%H')}")
    now_h = datetime.datetime.now().hour
    now_min = datetime.datetime.now().minute
    # if now_h == 0:
    #     for key, _ in rule_params.items():
    #         h_rank_bottom(now_date=now_date, now_h=now_h, rule_key=key)
    #     return
    # 查看当前小时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)
    elif now_min > 50:
        log_.info('h_recall data is None, use bottom data!')
        for key, _ in rule_params.items():
            h_rank_bottom(now_date=now_date, now_h=now_h, rule_key=key)
    else:
        # 数据没准备好，1分钟后重新检查
        Timer(60, h_timer_check).start()


if __name__ == '__main__':
    # df1 = get_feature_data()
    # res = cal_score(df=df1)
    # video_rank(df=res, now_date=datetime.datetime.today())
    # rank_by_h()
    h_timer_check()