rov-offline/alg_recsys_recall_1h_region.py

# -*- coding: utf-8 -*-
import multiprocessing
import traceback
import gevent
import datetime
import pandas as pd
import math
from functools import reduce
from odps import ODPS
from threading import Timer
from utils import MysqlHelper, RedisHelper, get_data_from_odps, filter_video_status, filter_shield_video, \
    check_table_partition_exits, filter_video_status_app, send_msg_to_feishu, filter_political_videos
from config import set_config
from log import Log
from check_video_limit_distribute import update_limit_video_score

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

region_code = config_.REGION_CODE


RULE_PARAMS = {
    'rule_params': {
        'rule66': {
            'view_type': 'video-show-region', 'platform_return_rate': 0.001,
            'region_24h_rule_key': 'rule66', '24h_rule_key': 'rule66'
        },
        'rule67': {
          'view_type': 'video-show-region', 'platform_return_rate': 0.001,
        'region_24h_rule_key': 'rule66', '24h_rule_key': 'rule66', 'h_rule_key': 'rule66'
         },
         'rule68': {
             'view_type': 'video-show-region', 'platform_return_rate': 0.001,
             'region_24h_rule_key': 'rule66', '24h_rule_key': 'rule66',
             'score_func': 'back_rate_exponential_weighting1'
         },

    },
    'data_params': config_.DATA_PARAMS,
    'params_list': [
        # 532
        # {'data': 'data66', 'rule': 'rule66'},  # 523-> 523 & 518
        {'data': 'data66', 'rule': 'rule67'},  # 523->510
        # {'data': 'data66', 'rule': 'rule68'},  # 523->514
        # {'data': 'data66', 'rule': 'rule69'},  # 523->518
    ],
}

features = [
    'apptype',
    'code',
    '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
    'lastonehour_show_region',  # 地域分组
    '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 get_region_code(region):
    """获取省份对应的code"""
    mysql_helper = MysqlHelper(mysql_info=config_.MYSQL_INFO)
    sql = f"SELECT ad_code FROM region_adcode WHERE parent_id = 0 AND region LIKE '{region}%';"
    ad_code = mysql_helper.get_data(sql=sql)
    return ad_code[0][0]


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')
        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 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_day_30day_videos(now_date, data_key, rule_key):
    """获取天级更新相对30天的视频id"""
    redis_helper = RedisHelper()
    day_30day_recall_key_prefix = config_.RECALL_KEY_NAME_PREFIX_30DAY
    now_dt = datetime.datetime.strftime(now_date, '%Y%m%d')
    day_30day_recall_key_name = f"{day_30day_recall_key_prefix}{data_key}:{rule_key}:{now_dt}"
    if not redis_helper.key_exists(key_name=day_30day_recall_key_name):
        redis_dt = datetime.datetime.strftime((now_date - datetime.timedelta(days=1)), '%Y%m%d')
        day_30day_recall_key_name = f"{day_30day_recall_key_prefix}{data_key}:{rule_key}:{redis_dt}"
    data = redis_helper.get_all_data_from_zset(key_name=day_30day_recall_key_name, with_scores=True)
    if data is None:
        return None
    video_ids = [int(video_id) for video_id, _ in data]
    return video_ids


def get_feature_data(project, table, 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_initial(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_preview+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) == 'video-show':
        df['ctr'] = df['lastonehour_play'] / (df['lastonehour_show'] + 1000)
    elif param.get('view_type', None) == 'video-show-region':
        df['ctr'] = df['lastonehour_play'] / (df['lastonehour_show_region'] + 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['score1'] = df['share_rate'] * df['back_rate'] * df['log_back'] * df['K2']

    click_score_rate = param.get('click_score_rate', None)
    back_score_rate = param.get('click_score_rate', None)
    if click_score_rate is not None:
        df['score'] = (1 - click_score_rate) * df['score1'] + click_score_rate * df['K2']
    elif back_score_rate is not None:
        df['score'] = (1 - back_score_rate) * df['score1'] + back_score_rate * df['back_rate']
    else:
        df['score'] = df['score1']

    df = df.sort_values(by=['score'], ascending=False)
    return df


def cal_score_add_return(df, param):
    # score计算公式: sharerate*(backrate*logback + backrate2*logback_now2 + backrate3*logback_now3)*ctr
    # sharerate = lastonehour_share/(lastonehour_play+1000)
    # backrate = lastonehour_return/(lastonehour_share+10)
    # backrate2 = lasttwohour_return_now/(lasttwohour_share+10)
    # backrate3 = lastthreehour_return_now/(lastthreehour_share+10)
    # ctr = lastonehour_play/(lastonehour_preview+1000), 对ctr限最大值：K2 = 0.6 if ctr > 0.6 else ctr
    # score = k2 * sharerate * (backrate * LOG(lastonehour_return+1) + backrate_2 * LOG(lasttwohour_return_now+1) + backrate_3 * LOG(lastthreehour_return_now+1))

    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)
    df['back_rate2'] = df['lasttwohour_return_now'] / (df['lasttwohour_share'] + 10)
    df['log_back2'] = (df['lasttwohour_return_now'] + 1).apply(math.log)
    df['back_rate3'] = df['lastthreehour_return_now'] / (df['lastthreehour_share'] + 10)
    df['log_back3'] = (df['lastthreehour_return_now'] + 1).apply(math.log)

    if param.get('view_type', None) == 'video-show':
        df['ctr'] = df['lastonehour_play'] / (df['lastonehour_show'] + 1000)
    elif param.get('view_type', None) == 'video-show-region':
        df['ctr'] = df['lastonehour_play'] / (df['lastonehour_show_region'] + 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['K2'] * df['share_rate'] * (
            df['back_rate'] * df['log_back'] +
            df['back_rate2'] * df['log_back2'] +
            df['back_rate3'] * df['log_back3']
    )

    df = df.sort_values(by=['score'], ascending=False)
    return df


def cal_score_multiply_return_retention(df, param):
    # score计算公式: k2 * sharerate * backrate * LOG(lastonehour_return+1) * 前两小时回流留存
    # sharerate = lastonehour_share/(lastonehour_play+1000)
    # backrate = lastonehour_return/(lastonehour_share+10)
    # ctr = lastonehour_play/(lastonehour_preview+1000), 对ctr限最大值：K2 = 0.6 if ctr > 0.6 else ctr
    # 前两小时回流留存 return_retention_initial = (lasttwohour_return_now + lastthreehour_return_now)/(lasttwohour_return + lastthreehour_return + 1)
    # return_retention = 0.5 if return_retention_initial == 0 else return_retention_initial
    # score = k2 * sharerate * backrate * LOG(lastonehour_return+1) * return_retention

    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)
    elif param.get('view_type', None) == 'video-show-region':
        df['ctr'] = df['lastonehour_play'] / (df['lastonehour_show_region'] + 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['return_retention_initial'] = (df['lasttwohour_return_now'] + df['lastthreehour_return_now']) / \
                                     (df['lasttwohour_return'] + df['lastthreehour_return'] + 1)
    df['return_retention'] = df['return_retention_initial'].apply(lambda x: 0.5 if x == 0 else x)

    df['platform_return_rate'] = df['platform_return'] / df['lastonehour_return']

    df['score'] = df['K2'] * df['share_rate'] * df['back_rate'] * df['log_back'] * df['return_retention']

    df = df.sort_values(by=['score'], ascending=False)
    return df


def cal_score_update_backrate(df, param):
    # score计算公式: k2 * sharerate * (backrate + backrate * backrate_2 * backrate_3) * LOG(lastonehour_return+1)
    # sharerate = lastonehour_share/(lastonehour_play+1000)
    # backrate = lastonehour_return/(lastonehour_share+10)
    # backrate2 = lasttwohour_return_now/(lasttwohour_share+10)
    # backrate3 = lastthreehour_return_now/(lastthreehour_share+10)
    # ctr = lastonehour_play/(lastonehour_preview+1000), 对ctr限最大值：K2 = 0.6 if ctr > 0.6 else ctr
    # backrate1_3_initial = backrate * backrate_2 * backrate_3
    # backrate1_3 = 0.02 if backrate1_3_initial == 0 else backrate1_3_initial
    # score = k2 * sharerate * (backrate + backrate1_3) * LOG(lastonehour_return+1)

    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['back_rate2'] = df['lasttwohour_return_now'] / (df['lasttwohour_share'] + 10)
    df['back_rate3'] = df['lastthreehour_return_now'] / (df['lastthreehour_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)
    elif param.get('view_type', None) == 'video-show-region':
        df['ctr'] = df['lastonehour_play'] / (df['lastonehour_show_region'] + 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['backrate1_3_initial'] = df['back_rate'] * df['back_rate2'] * df['back_rate3']
    df['backrate1_3'] = df['backrate1_3_initial'].apply(lambda x: 0.02 if x == 0 else x)
    df['platform_return_rate'] = df['platform_return'] / df['lastonehour_return']

    df['score'] = df['K2'] * df['share_rate'] * (df['back_rate'] + df['backrate1_3']) * df['log_back']

    df = df.sort_values(by=['score'], ascending=False)
    return df


def cal_score_with_new_return(df, param):
    # 回流数据使用 分享限制地域，回流不限制地域 统计数据
    # score计算公式: sharerate*backrate*logback*ctr
    # sharerate = lastonehour_share/(lastonehour_play+1000)
    # backrate = lastonehour_return_new/(lastonehour_share+10)
    # ctr = lastonehour_play/(lastonehour_preview+1000), 对ctr限最大值：K2 = 0.6 if ctr > 0.6 else ctr
    # score = sharerate * backrate * LOG(lastonehour_return_new+1) * K2

    df = df.fillna(0)
    df['share_rate'] = df['lastonehour_share'] / (df['lastonehour_play'] + 1000)
    df['back_rate'] = df['lastonehour_return_new'] / (df['lastonehour_share'] + 10)
    df['log_back'] = (df['lastonehour_return_new'] + 1).apply(math.log)
    if param.get('view_type', None) == 'video-show':
        df['ctr'] = df['lastonehour_play'] / (df['lastonehour_show'] + 1000)
    elif param.get('view_type', None) == 'video-show-region':
        df['ctr'] = df['lastonehour_play'] / (df['lastonehour_show_region'] + 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_new'] / df['lastonehour_return_new']

    df['score'] = df['share_rate'] * df['back_rate'] * df['log_back'] * df['K2']

    df = df.sort_values(by=['score'], ascending=False)
    return df


def cal_score_multiply_return_retention_with_new_return(df, param):
    # 回流数据使用 分享限制地域，回流不限制地域 统计数据
    # score计算公式: k2 * sharerate * backrate * LOG(lastonehour_return_new+1) * 前两小时回流留存
    # sharerate = lastonehour_share/(lastonehour_play+1000)
    # backrate = lastonehour_return_new/(lastonehour_share+10)
    # ctr = lastonehour_play/(lastonehour_preview+1000), 对ctr限最大值：K2 = 0.6 if ctr > 0.6 else ctr
    # 前两小时回流留存 return_retention_initial = (lasttwohour_return_now_new + lastthreehour_return_now_new)/(lasttwohour_return_new + lastthreehour_return_new + 1)
    # return_retention = 0.5 if return_retention_initial == 0 else return_retention_initial
    # score = k2 * sharerate * backrate * LOG(lastonehour_return_new+1) * return_retention

    df = df.fillna(0)
    df['share_rate'] = df['lastonehour_share'] / (df['lastonehour_play'] + 1000)
    df['back_rate'] = df['lastonehour_return_new'] / (df['lastonehour_share'] + 10)
    df['log_back'] = (df['lastonehour_return_new'] + 1).apply(math.log)

    if param.get('view_type', None) == 'video-show':
        df['ctr'] = df['lastonehour_play'] / (df['lastonehour_show'] + 1000)
    elif param.get('view_type', None) == 'video-show-region':
        df['ctr'] = df['lastonehour_play'] / (df['lastonehour_show_region'] + 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['return_retention_initial'] = (df['lasttwohour_return_now_new'] + df['lastthreehour_return_now_new']) / \
                                     (df['lasttwohour_return_new'] + df['lastthreehour_return_new'] + 1)
    df['return_retention'] = df['return_retention_initial'].apply(lambda x: 0.5 if x == 0 else x)

    df['platform_return_rate'] = df['platform_return_new'] / df['lastonehour_return_new']

    df['score'] = df['K2'] * df['share_rate'] * df['back_rate'] * df['log_back'] * df['return_retention']

    df = df.sort_values(by=['score'], ascending=False)
    return df


def cal_score_with_back_view0(df, param):
    # score = sharerate*backrate*log(return+1)*CTR,
    # sharerate=(lastonehour_share+1)/(lastonehour_play+1000)
    # backrate=(lastonehour_return+1)/(lastonehour_share+10)
    # CTR=(lastonehour_play+1)/(lastonehour_view+100), ctr不进行校正
    df = df.fillna(0)
    df['share_rate'] = (df['lastonehour_share'] + 1) / (df['lastonehour_play'] + 1000)
    df['back_rate'] = (df['lastonehour_return'] + 1) / (df['lastonehour_share'] + 10)
    df['log_back'] = (df['lastonehour_return'] + 1).apply(math.log)
    df['ctr'] = (df['lastonehour_play'] + 1) / (df['lastonehour_view'] + 100)
    df['platform_return_rate'] = df['platform_return'] / df['lastonehour_return']
    df['score'] = df['share_rate'] * df['back_rate'] * df['log_back'] * df['ctr']
    df = df.sort_values(by=['score'], ascending=False)
    return df


def cal_score_with_back_view1(df, param):
    # score = back_play_rate*log(return+1)*CTR,
    # back_play_rate=(lastonehour_return+1)/(lastonehour_play+1000)
    # CTR=(lastonehour_play+1)/(lastonehour_view+100), ctr不进行校正
    df = df.fillna(0)
    df['back_play_rate'] = (df['lastonehour_return'] + 1) / (df['lastonehour_play'] + 1000)
    df['log_back'] = (df['lastonehour_return'] + 1).apply(math.log)
    df['ctr'] = (df['lastonehour_play'] + 1) / (df['lastonehour_view'] + 100)
    df['platform_return_rate'] = df['platform_return'] / df['lastonehour_return']
    df['score'] = df['back_play_rate'] * df['log_back'] * df['ctr']
    df = df.sort_values(by=['score'], ascending=False)
    return df


def cal_score_with_back_rate_exponential_weighting1(df, param):
    """
    计算score
    :param df: 特征数据
    :param param: 规则参数
    :return:
    """
    # score计算公式: score = sharerate * backrate ^ 2 * 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)
    elif param.get('view_type', None) == 'video-show-region':
        df['ctr'] = df['lastonehour_play'] / (df['lastonehour_show_region'] + 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'] ** 2 * df['log_back'] * df['K2']

    df = df.sort_values(by=['score'], ascending=False)
    return df


def cal_score_with_back_rate_exponential_weighting2(df, param):
    """
    计算score
    :param df: 特征数据
    :param param: 规则参数
    :return:
    """
    # score计算公式: score = sharerate ^ 0.5 * backrate ^ 2 * LOG(lastonehour_return + 1) * K2 ^ 0.5
    # 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)
    elif param.get('view_type', None) == 'video-show-region':
        df['ctr'] = df['lastonehour_play'] / (df['lastonehour_show_region'] + 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'] ** 0.5 * df['back_rate'] ** 2 * df['log_back'] * df['K2'] ** 0.5

    df = df.sort_values(by=['score'], ascending=False)
    return df

def cal_score_with_back_rate_by_rank_weighting(df, param):
    """
    add by sunmingze 20231123
    计算score
    :param df: 特征数据
    :param param: 规则参数
    :return:
    """
    # score计算公式: score =  1 / sharerate(rank)^0.5 + 5 / backrate(rank)^0.5 + 10 / LOG(lastonehour_return +1)(rank) ^0.5
    #   +  1 / K2(rank)^0.5
    # 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)
    elif param.get('view_type', None) == 'video-show-region':
        df['ctr'] = df['lastonehour_play'] / (df['lastonehour_show_region'] + 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']

    # 分别的得到sharerate、backrate、K值、return人数的序关系
    df['rank_by_sharerate'] = df['share_rate'].rank(ascending=0, method='dense')
    df['rank_by_backrate'] = df['back_rate'].rank(ascending=0, method='dense')
    df['rank_by_K2'] = df['K2'].rank(ascending=0, method='dense')
    df['rank_by_logback'] = df['log_back'].rank(ascending=0, method='dense')

    # 计算基于序的加法关系函数
    df['score'] = 1/(df['rank_by_sharerate'] + 10) + 5/(df['rank_by_backrate'] + 10)
    df['score'] = df['score'] + 5/(df['rank_by_logback'] + 10) + 1/(df['rank_by_K2'] + 10)

    df = df.sort_values(by=['score'], ascending=False)
    return df




def cal_score(df, param):
    if param.get('return_data', None) == 'share_region_return':
        if param.get('score_func', None) == 'multiply_return_retention':
            df = cal_score_multiply_return_retention_with_new_return(df=df, param=param)
        else:
            df = cal_score_with_new_return(df=df, param=param)
    else:
        if param.get('score_func', None) == 'add_backrate*log(return+1)':
            df = cal_score_add_return(df=df, param=param)
        elif param.get('score_func', None) == 'multiply_return_retention':
            df = cal_score_multiply_return_retention(df=df, param=param)
        elif param.get('score_func', None) == 'update_backrate':
            df = cal_score_update_backrate(df=df, param=param)
        elif param.get('score_func', None) == 'back_view0':
            df = cal_score_with_back_view0(df=df, param=param)
        elif param.get('score_func', None) == 'back_view1':
            df = cal_score_with_back_view1(df=df, param=param)
        elif param.get('score_func', None) == 'back_rate_exponential_weighting1':
            df = cal_score_with_back_rate_exponential_weighting1(df=df, param=param)
        elif param.get('score_func', None) == 'back_rate_exponential_weighting2':
            df = cal_score_with_back_rate_exponential_weighting2(df=df, param=param)
        elif param.get('score_func', None) == 'back_rate_rank_weighting':
            df = cal_score_with_back_rate_by_rank_weighting(df=df, param=param)
        else:
            df = cal_score_initial(df=df, param=param)
    return df


def add_func1(initial_df, pre_h_df):
    """当前小时级数据与前几个小时数据合并"""
    score_list = initial_df['score'].to_list()
    if len(score_list) > 0:
        min_score = min(score_list)
    else:
        min_score = 0
    pre_h_df = pre_h_df[pre_h_df['score'] > min_score]
    df = pd.concat([initial_df, pre_h_df], ignore_index=True)
    # videoid去重，保留分值高
    df['videoid'] = df['videoid'].astype(int)
    df = df.sort_values(by=['score'], ascending=False)
    df = df.drop_duplicates(subset=['videoid'], keep="first")
    return df


def add_func2(initial_df, pre_h_df):
    """当前小时级数据与前几个小时数据合并: 当前小时存在的视频以当前小时为准，否则以高分为主"""
    score_list = initial_df['score'].to_list()
    if len(score_list) > 0:
        min_score = min(score_list)
    else:
        min_score = 0
    initial_video_id_list = initial_df['videoid'].to_list()
    pre_h_df = pre_h_df[pre_h_df['score'] > min_score]
    pre_h_df = pre_h_df[~pre_h_df['videoid'].isin(initial_video_id_list)]

    df = pd.concat([initial_df, pre_h_df], ignore_index=True)
    # videoid去重，保留分值高
    df['videoid'] = df['videoid'].astype(int)
    df = df.sort_values(by=['score'], ascending=False)
    df = df.drop_duplicates(subset=['videoid'], keep="first")
    return df


def add_videos(initial_df, now_date, rule_key, region, data_key, hour_count, top, add_func):
    """
    地域小时级数据列表中增加前6h优质视频
    :param initial_df: 地域小时级筛选结果
    :param now_date:
    :param data_key:
    :param region:
    :param rule_key:
    :param hour_count: 前几个小时, type-int
    :param top: type-int
    :return: df
    """
    redis_helper = RedisHelper()
    pre_h_data = []
    for i in range(1, hour_count+1):
        pre_date = now_date - datetime.timedelta(hours=i)
        pre_h = pre_date.hour
        pre_h_recall_key_name = f"{config_.RECALL_KEY_NAME_PREFIX_REGION_BY_H}{region}:{data_key}:{rule_key}:" \
                                f"{datetime.datetime.strftime(pre_date, '%Y%m%d')}:{pre_h}"
        pre_h_top_data = redis_helper.get_data_zset_with_index(key_name=pre_h_recall_key_name,
                                                               start=0, end=top-1,
                                                               desc=True, with_scores=True)
        if pre_h_top_data is None:
            continue
        pre_h_data.extend(pre_h_top_data)
    pre_h_df = pd.DataFrame(data=pre_h_data, columns=['videoid', 'score'])
    if add_func == 'func2':
        df = add_func2(initial_df=initial_df, pre_h_df=pre_h_df)
    else:
        df = add_func1(initial_df=initial_df, pre_h_df=pre_h_df)
    return df


def video_rank(df, now_date, now_h, rule_key, param, region, data_key, rule_rank_h_flag,
               add_videos_with_pre_h=False, hour_count=0):
    """
    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
    :param region: 所属地域
    :return:
    """
    redis_helper = RedisHelper()

    # 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', 1)
    score_value = param.get('score_rule', 0)
    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)]

    # videoid重复时，保留分值高
    h_recall_df = h_recall_df.sort_values(by=['score'], ascending=False)
    h_recall_df = h_recall_df.drop_duplicates(subset=['videoid'], keep='first')
    h_recall_df['videoid'] = h_recall_df['videoid'].astype(int)

    log_.info(f"各种规则过滤后，一共有多少个视频 = {len(h_recall_df)}")
    # 增加打捞的优质视频
    if add_videos_with_pre_h is True:
        add_func = param.get('add_func', None)
        h_recall_df = add_videos(initial_df=h_recall_df, now_date=now_date, rule_key=rule_key,
                                 region=region, data_key=data_key, hour_count=hour_count, top=10, add_func=add_func)
        log_.info(f"打捞优质视频完成")
    h_recall_videos = h_recall_df['videoid'].to_list()
    log_.info(f"各种规则增加后，一共有多少个视频 = {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)

    # 屏蔽视频过滤
    shield_config = param.get('shield_config', config_.SHIELD_CONFIG)
    shield_key_name_list = shield_config.get(region, None)
    if shield_key_name_list is not None:
        filtered_videos = filter_shield_video(video_ids=filtered_videos, shield_key_name_list=shield_key_name_list)

    # 涉政视频过滤
    political_filter = param.get('political_filter', None)
    if political_filter is True:
        filtered_videos = filter_political_videos(video_ids=filtered_videos)
    log_.info(f"视频状态-涉政等-过滤后，一共有多少个视频 = {len(filtered_videos)}")


    h_video_ids = []
    by_30day_rule_key = param.get('30day_rule_key', None)
    if by_30day_rule_key is not None:
        # 与相对30天列表去重
        h_video_ids = get_day_30day_videos(now_date=now_date, data_key=data_key, rule_key=by_30day_rule_key)
        if h_video_ids is not None:
            filtered_videos = [video_id for video_id in filtered_videos if int(video_id) not in h_video_ids]

    # 写入对应的redis
    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_REGION_BY_H}{region}:{data_key}:{rule_key}:" \
        f"{datetime.datetime.strftime(now_date, '%Y%m%d')}:{now_h}"
    log_.info("打印地域1小时的某个地域{},redis key:{}".format(region, h_recall_key_name))
    if len(h_recall_result) > 0:
        log_.info(f"开始写入头部数据：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 * 24 * 3600)
        # 限流视频score调整
        tmp = update_limit_video_score(initial_videos=h_recall_result, key_name=h_recall_key_name)
        if tmp:
            log_.info(f"走了限流逻辑后：count = {len(h_recall_result)}, key = {h_recall_key_name}")
        else:
            log_.info("走了限流逻辑，但没更改redis，未生效。")
        # 清空线上过滤应用列表
        # redis_helper.del_keys(key_name=f"{config_.REGION_H_VIDEO_FILER}{region}.{app_type}.{data_key}.{rule_key}")
    else:
        log_.info(f"无数据，不写入。")

    # h_rule_key = param.get('h_rule_key', None)
    # region_24h_rule_key = param.get('region_24h_rule_key', 'rule1')
    # by_24h_rule_key = param.get('24h_rule_key', None)
    # by_48h_rule_key = param.get('48h_rule_key', None)
    # dup_remove = param.get('dup_remove', True)
    # # 与其他召回视频池去重，存入对应的redis
    # dup_to_redis(h_video_ids=h_video_ids, now_date=now_date, now_h=now_h, rule_key=rule_key, h_rule_key=h_rule_key,
    #              region_24h_rule_key=region_24h_rule_key, by_24h_rule_key=by_24h_rule_key,
    #              by_48h_rule_key=by_48h_rule_key, region=region, data_key=data_key,
    #              rule_rank_h_flag=rule_rank_h_flag, political_filter=political_filter,
    #              shield_config=shield_config, dup_remove=dup_remove)


def dup_data(h_video_ids, initial_key_name, dup_key_name, region, political_filter, shield_config, dup_remove):
    redis_helper = RedisHelper()
    if redis_helper.key_exists(key_name=initial_key_name):
        initial_data = redis_helper.get_all_data_from_zset(key_name=initial_key_name, with_scores=True)
        # 屏蔽视频过滤
        initial_video_ids = [int(video_id) for video_id, _ in initial_data]
        shield_key_name_list = shield_config.get(region, None)
        if shield_key_name_list is not None:
            initial_video_ids = filter_shield_video(video_ids=initial_video_ids,
                                                    shield_key_name_list=shield_key_name_list)
        # 涉政视频过滤
        if political_filter is True:
            initial_video_ids = filter_political_videos(video_ids=initial_video_ids)

        dup_data = {}
        # 视频去重逻辑
        if dup_remove is True:
            for video_id, score in initial_data:
                if int(video_id) not in h_video_ids and int(video_id) in initial_video_ids:
                    dup_data[int(video_id)] = score
                    h_video_ids.append(int(video_id))
        else:
            for video_id, score in initial_data:
                if int(video_id) in initial_video_ids:
                    dup_data[int(video_id)] = score

        if len(dup_data) > 0:
            redis_helper.add_data_with_zset(key_name=dup_key_name, data=dup_data, expire_time=2 * 24 * 3600)
            # 限流视频score调整
            update_limit_video_score(initial_videos=dup_data, key_name=dup_key_name)
    return h_video_ids


def dup_to_redis(h_video_ids, now_date, now_h, rule_key, h_rule_key, region_24h_rule_key, by_24h_rule_key, by_48h_rule_key,
                 region, data_key, rule_rank_h_flag, political_filter, shield_config, dup_remove):
    """将地域分组小时级数据与其他召回视频池去重，存入对应的redis"""
    # ##### 去重更新不区分地域小时级列表，并另存为redis中
    if h_rule_key is not None:
        h_key_name = \
            f"{config_.RECALL_KEY_NAME_PREFIX_BY_H_H}{data_key}:{h_rule_key}:" \
            f"{datetime.datetime.strftime(now_date, '%Y%m%d')}:{now_h}"
        h_dup_key_name = \
            f"{config_.RECALL_KEY_NAME_PREFIX_DUP_H_H}{region}:{data_key}:{rule_key}:" \
            f"{datetime.datetime.strftime(now_date, '%Y%m%d')}:{now_h}"
        h_video_ids = dup_data(h_video_ids=h_video_ids, initial_key_name=h_key_name,
                               dup_key_name=h_dup_key_name, region=region, political_filter=political_filter,
                               shield_config=shield_config, dup_remove=dup_remove)

    # ##### 去重更新地域分组小时级24h列表，并另存为redis中
    region_24h_key_name = \
        f"{config_.RECALL_KEY_NAME_PREFIX_REGION_BY_24H}{region}:{data_key}:{region_24h_rule_key}:" \
        f"{datetime.datetime.strftime(now_date, '%Y%m%d')}:{now_h}"
    region_24h_dup_key_name = \
        f"{config_.RECALL_KEY_NAME_PREFIX_DUP1_REGION_24H_H}{region}:{data_key}:{rule_key}:" \
        f"{datetime.datetime.strftime(now_date, '%Y%m%d')}:{now_h}"
    h_video_ids = dup_data(h_video_ids=h_video_ids, initial_key_name=region_24h_key_name,
                           dup_key_name=region_24h_dup_key_name, region=region, political_filter=political_filter,
                           shield_config=shield_config, dup_remove=dup_remove)

    if rule_rank_h_flag == '48h':

        # ##### 去重小程序相对48h更新结果，并另存为redis中
        h_48h_key_name = f"{config_.RECALL_KEY_NAME_PREFIX_BY_48H}{data_key}:{by_48h_rule_key}:" \
                         f"{datetime.datetime.strftime(now_date, '%Y%m%d')}:{now_h}"
        h_48h_dup_key_name = \
            f"{config_.RECALL_KEY_NAME_PREFIX_DUP2_REGION_48H_H}{region}:{data_key}:{rule_key}:" \
            f"{datetime.datetime.strftime(now_date, '%Y%m%d')}:{now_h}"
        h_video_ids = dup_data(h_video_ids=h_video_ids, initial_key_name=h_48h_key_name,
                               dup_key_name=h_48h_dup_key_name, region=region, political_filter=political_filter,
                               shield_config=shield_config, dup_remove=dup_remove)

        # ##### 去重小程序相对48h 筛选后剩余数据 更新结果，并另存为redis中
        if by_48h_rule_key == 'rule1':
            other_h_48h_key_name = f"{config_.RECALL_KEY_NAME_PREFIX_BY_48H_OTHER}{data_key}:" \
                                   f"{by_48h_rule_key}:{datetime.datetime.strftime(now_date, '%Y%m%d')}:{now_h}"
            other_h_48h_dup_key_name = \
                f"{config_.RECALL_KEY_NAME_PREFIX_DUP3_REGION_48H_H}{region}:{data_key}:{rule_key}:" \
                f"{datetime.datetime.strftime(now_date, '%Y%m%d')}:{now_h}"
            h_video_ids = dup_data(h_video_ids=h_video_ids, initial_key_name=other_h_48h_key_name,
                                   dup_key_name=other_h_48h_dup_key_name, region=region,
                                   political_filter=political_filter, shield_config=shield_config,
                                   dup_remove=dup_remove)

    else:
        # ##### 去重小程序相对24h更新结果，并另存为redis中
        h_24h_key_name = f"{config_.RECALL_KEY_NAME_PREFIX_BY_24H}{data_key}:{by_24h_rule_key}:" \
                         f"{datetime.datetime.strftime(now_date, '%Y%m%d')}:{now_h}"
        h_24h_dup_key_name = \
            f"{config_.RECALL_KEY_NAME_PREFIX_DUP2_REGION_24H_H}{region}:{data_key}:{rule_key}:" \
            f"{datetime.datetime.strftime(now_date, '%Y%m%d')}:{now_h}"
        h_video_ids = dup_data(h_video_ids=h_video_ids, initial_key_name=h_24h_key_name,
                               dup_key_name=h_24h_dup_key_name, region=region, political_filter=political_filter,
                               shield_config=shield_config, dup_remove=dup_remove)

        # ##### 去重小程序相对24h 筛选后剩余数据 更新结果，并另存为redis中
        # if by_24h_rule_key in ['rule3', 'rule4']:
        other_h_24h_key_name = f"{config_.RECALL_KEY_NAME_PREFIX_BY_24H_OTHER}{data_key}:" \
                               f"{by_24h_rule_key}:{datetime.datetime.strftime(now_date, '%Y%m%d')}:{now_h}"
        other_h_24h_dup_key_name = \
            f"{config_.RECALL_KEY_NAME_PREFIX_DUP3_REGION_24H_H}{region}:{data_key}:{rule_key}:" \
            f"{datetime.datetime.strftime(now_date, '%Y%m%d')}:{now_h}"
        h_video_ids = dup_data(h_video_ids=h_video_ids, initial_key_name=other_h_24h_key_name,
                               dup_key_name=other_h_24h_dup_key_name, region=region, political_filter=political_filter,
                               shield_config=shield_config, dup_remove=dup_remove)

    # ##### 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_dup_key_name = \
    #     f"{config_.RECALL_KEY_NAME_PREFIX_DUP_REGION_H}{region}:{data_key}:{rule_key}:" \
    #     f"{datetime.datetime.strftime(now_date, '%Y%m%d')}:{now_h}"
    # h_video_ids = dup_data(h_video_ids=h_video_ids, initial_key_name=model_key_name,
    #                        dup_key_name=model_data_dup_key_name, region=region)


def merge_df(df_left, df_right):
    """
    df按照videoid, code 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', 'code'], how='outer', suffixes=['_x', '_y'])
    df_merged.fillna(0, inplace=True)
    feature_list = ['videoid', 'code']
    for feature in features:
        if feature in ['apptype', 'videoid', 'code']:
            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, code]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', 'code'], how='outer', suffixes=['_x', '_y'])
    df_merged.fillna(0, inplace=True)
    feature_list = ['videoid', 'code', 'lastonehour_return', 'platform_return', 'score']
    for feature in feature_list[2:]:
        df_merged[feature] = df_merged[f'{feature}_x'] + df_merged[f'{feature}_y']
    return df_merged[feature_list]


def process_with_region(region, df_merged, data_key, rule_key, rule_param, now_date, now_h,
                        rule_rank_h_flag, add_videos_with_pre_h, hour_count):
    log_.info(f"多协程的region = {region} 开始执行")
    region_df = df_merged[df_merged['code'] == region]
    log_.info(f'该区域region = {region}, 下有多少数据量 = {len(region_df)}')
    score_df = cal_score(df=region_df, param=rule_param)
    video_rank(df=score_df, now_date=now_date, now_h=now_h, rule_key=rule_key, param=rule_param,
               region=region, data_key=data_key, rule_rank_h_flag=rule_rank_h_flag,
               add_videos_with_pre_h=add_videos_with_pre_h, hour_count=hour_count)
    log_.info(f"多协程的region = {region} 完成执行")


def process_with_region2(region, df_merged, data_key, rule_key, rule_param, now_date, now_h,
                         rule_rank_h_flag, add_videos_with_pre_h, hour_count):
    log_.info(f"region = {region} start...")
    region_score_df = df_merged[df_merged['code'] == region]
    log_.info(f'region = {region}, region_score_df count = {len(region_score_df)}')
    video_rank(df=region_score_df, now_date=now_date, now_h=now_h, region=region,
               rule_key=rule_key, param=rule_param, data_key=data_key, rule_rank_h_flag=rule_rank_h_flag,
               add_videos_with_pre_h=add_videos_with_pre_h, hour_count=hour_count)
    log_.info(f"region = {region} end!")


def process_with_app_type(app_type, params, region_code_list, feature_df, now_date, now_h, rule_rank_h_flag):
    log_.info(f"app_type = {app_type} start...")
    data_params_item = params.get('data_params')
    rule_params_item = params.get('rule_params')
    task_list = []
    for param in params.get('params_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}")
        df_list = [feature_df[feature_df['apptype'] == apptype] for apptype in data_param]
        df_merged = reduce(merge_df, df_list)

        rule_key = param.get('rule')
        rule_param = rule_params_item.get(rule_key)
        log_.info(f"rule_key = {rule_key}, rule_param = {rule_param}")
        task_list.extend(
            [
                gevent.spawn(process_with_region, region, df_merged, app_type, data_key, rule_key, rule_param,
                             now_date, now_h, rule_rank_h_flag)
                for region in region_code_list
            ]
        )
    gevent.joinall(task_list)
    log_.info(f"app_type = {app_type} end!")


    # log_.info(f"app_type = {app_type}")
    # task_list = []
    # for data_key, data_param in params['data_params'].items():
    #     log_.info(f"data_key = {data_key}, data_param = {data_param}")
    #     df_list = [feature_df[feature_df['apptype'] == apptype] for apptype in data_param]
    #     df_merged = reduce(merge_df, df_list)
    #     for rule_key, rule_param in params['rule_params'].items():
    #         log_.info(f"rule_key = {rule_key}, rule_param = {rule_param}")
    #         task_list.extend(
    #             [
    #                 gevent.spawn(process_with_region, region, df_merged, app_type, data_key, rule_key, rule_param,
    #                              now_date, now_h)
    #                 for region in region_code_list
    #             ]
    #         )
    # gevent.joinall(task_list)


def copy_data_for_city(region, city_code, data_key, rule_key, now_date, now_h, shield_config):
    """copy 对应数据到城市对应redis，并做相应屏蔽视频过滤"""
    log_.info(f"city_code = {city_code} start ...")
    redis_helper = RedisHelper()
    key_prefix_list = [
        config_.RECALL_KEY_NAME_PREFIX_REGION_BY_H,  # 地域小时级
        config_.RECALL_KEY_NAME_PREFIX_DUP1_REGION_24H_H,  # 地域相对24h
        config_.RECALL_KEY_NAME_PREFIX_DUP2_REGION_24H_H,  # 不区分地域相对24h
        config_.RECALL_KEY_NAME_PREFIX_DUP3_REGION_24H_H,  # 不区分地域相对24h筛选后
        config_.RECALL_KEY_NAME_PREFIX_DUP_REGION_H,  # rov大列表
    ]
    for key_prefix in key_prefix_list:
        region_key = f"{key_prefix}{region}:{data_key}:{rule_key}:{datetime.datetime.strftime(now_date, '%Y%m%d')}:{now_h}"
        city_key = f"{key_prefix}{city_code}:{data_key}:{rule_key}:{datetime.datetime.strftime(now_date, '%Y%m%d')}:{now_h}"
        if not redis_helper.key_exists(key_name=region_key):
            continue
        region_data = redis_helper.get_all_data_from_zset(key_name=region_key, with_scores=True)
        if not region_data:
            continue
        # 屏蔽视频过滤
        region_video_ids = [int(video_id) for video_id, _ in region_data]
        shield_key_name_list = shield_config.get(city_code, None)
        # shield_key_name_list = config_.SHIELD_CONFIG.get(city_code, None)
        if shield_key_name_list is not None:
            filtered_video_ids = filter_shield_video(video_ids=region_video_ids,
                                                     shield_key_name_list=shield_key_name_list)
        else:
            filtered_video_ids = region_video_ids
        city_data = {}
        for video_id, score in region_data:
            if int(video_id) in filtered_video_ids:
                city_data[int(video_id)] = score

        if len(city_data) > 0:
            redis_helper.add_data_with_zset(key_name=city_key, data=city_data, expire_time=2 * 24 * 3600)

    log_.info(f"city_code = {city_code} end!")


def process_with_param(param, data_params_item, rule_params_item, region_code_list, feature_df, now_date, now_h, rule_rank_h_flag):
    data_key = param.get('data')
    data_param = data_params_item.get(data_key)
    rule_key = param.get('rule')
    rule_param = rule_params_item.get(rule_key)
    merge_func = rule_param.get('merge_func', None)
    log_.info("数据采用:{},统计采用{}.".format(data_key, rule_key))
    log_.info("具体的规则是:{}.".format(rule_param))
    # 是否在地域小时级数据中增加打捞的优质视频
    add_videos_with_pre_h = rule_param.get('add_videos_with_pre_h', False)
    hour_count = rule_param.get('hour_count', 0)

    if merge_func == 2:
        score_df_list = []
        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']
        task_list = [
            gevent.spawn(process_with_region2,
                         region, df_merged, data_key, rule_key, rule_param, now_date, now_h, rule_rank_h_flag,
                         add_videos_with_pre_h, hour_count)
            for region in region_code_list
        ]
    else:
        df_list = [feature_df[feature_df['apptype'] == apptype] for apptype in data_param]
        df_merged = reduce(merge_df, df_list)
        task_list = [
            gevent.spawn(process_with_region,
                         region, df_merged, data_key, rule_key, rule_param, now_date, now_h, rule_rank_h_flag,
                         add_videos_with_pre_h, hour_count)
            for region in region_code_list
        ]

    gevent.joinall(task_list)

    # 特殊城市视频数据准备
    # 屏蔽视频过滤
    # shield_config = rule_param.get('shield_config', config_.SHIELD_CONFIG)
    # for region, city_list in config_.REGION_CITY_MAPPING.items():
    #     t = [
    #         gevent.spawn(
    #             copy_data_for_city,
    #             region, city_code, data_key, rule_key, now_date, now_h, shield_config
    #         )
    #         for city_code in city_list
    #     ]
    #     gevent.joinall(t)
    log_.info(f"多进程的 param = {param} 完成执行!")


def rank_by_h(project, table, now_date, now_h, rule_params, region_code_list, rule_rank_h_flag):
    # 获取特征数据
    feature_df = get_feature_data(project=project, table=table, now_date=now_date)
    feature_df['apptype'] = feature_df['apptype'].astype(int)
    data_params_item = rule_params.get('data_params')
    rule_params_item = rule_params.get('rule_params')
    params_list = rule_params.get('params_list')
    pool = multiprocessing.Pool(processes=len(params_list))
    for param in params_list:
        pool.apply_async(
            func=process_with_param,
            args=(param, data_params_item, rule_params_item, region_code_list, feature_df, now_date, now_h, rule_rank_h_flag)
        )
    pool.close()
    pool.join()



def h_bottom_process(param, rule_params_item, region_code_list, key_prefix, redis_dt, redis_h,
                     now_date, now_h, rule_rank_h_flag):
    redis_helper = RedisHelper()
    data_key = param.get('data')
    rule_key = param.get('rule')
    rule_param = rule_params_item.get(rule_key)
    log_.info(f"data_key = {data_key}, rule_key = {rule_key}, rule_param = {rule_param}")
    h_rule_key = rule_param.get('h_rule_key', None)
    region_24h_rule_key = rule_param.get('region_24h_rule_key', 'rule1')
    by_24h_rule_key = rule_param.get('24h_rule_key', None)
    by_48h_rule_key = rule_param.get('48h_rule_key', None)
    # 涉政视频过滤
    political_filter = param.get('political_filter', None)
    # 屏蔽视频过滤
    shield_config = param.get('shield_config', config_.SHIELD_CONFIG)
    dup_remove = param.get('dup_remove', True)
    for region in region_code_list:
        log_.info(f"region = {region}")
        key_name = f"{key_prefix}{region}:{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()
        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}{region}:{data_key}:{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=2 * 24 * 3600)
        # 与其他召回视频池去重，存入对应的redis
        dup_to_redis(h_video_ids=h_video_ids, now_date=now_date, now_h=now_h, rule_key=rule_key, h_rule_key=h_rule_key,
                     region_24h_rule_key=region_24h_rule_key, region=region,
                     data_key=data_key, by_24h_rule_key=by_24h_rule_key,
                     by_48h_rule_key=by_48h_rule_key, rule_rank_h_flag=rule_rank_h_flag,
                     political_filter=political_filter, shield_config=shield_config, dup_remove=dup_remove)
    # 特殊城市视频数据准备
    for region, city_list in config_.REGION_CITY_MAPPING.items():
        t = [
            gevent.spawn(
                copy_data_for_city,
                region, city_code, data_key, rule_key, now_date, now_h, shield_config
            )
            for city_code in city_list
        ]
        gevent.joinall(t)


def h_rank_bottom(now_date, now_h, rule_params, region_code_list, rule_rank_h_flag):
    """未按时更新数据，用上一小时结果作为当前小时的数据"""
    # 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

    # 以上一小时的地域分组数据作为当前小时的数据
    key_prefix = config_.RECALL_KEY_NAME_PREFIX_REGION_BY_H
    rule_params_item = rule_params.get('rule_params')
    params_list = rule_params.get('params_list')
    pool = multiprocessing.Pool(processes=len(params_list))
    for param in params_list:
        pool.apply_async(
            func=h_bottom_process,
            args=(param, rule_params_item, region_code_list, key_prefix, redis_dt, redis_h, now_date, now_h, rule_rank_h_flag)
        )
    pool.close()
    pool.join()


def h_timer_check():
    try:
        rule_rank_h_flag = "24h"
        rule_params = RULE_PARAMS
        project = config_.PROJECT_REGION_APP_TYPE
        table = config_.TABLE_REGION_APP_TYPE
        region_code_list = [code for region, code in region_code.items()]
        now_date = datetime.datetime.today()
        log_.info(f"开始执行: {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:
            log_.info("当前时间{}小时，使用bottom的data，开始。".format(now_h))
            h_rank_bottom(now_date=now_date, now_h=now_h, rule_params=rule_params, region_code_list=region_code_list,
                          rule_rank_h_flag=rule_rank_h_flag)
            log_.info("----------当前时间{}小时，使用bottom的data，完成----------".format(now_h))
            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('上游数据表查询数据条数 h_data_count = {}，开始计算。'.format(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, region_code_list=region_code_list, rule_rank_h_flag=rule_rank_h_flag)
            log_.info("数据1----------正常完成----------")
        elif now_min > 40:
            log_.info('当前分钟超过40，预计执行无法完成，使用 bottom data!')
            h_rank_bottom(now_date=now_date, now_h=now_h, rule_params=rule_params, region_code_list=region_code_list,
                          rule_rank_h_flag=rule_rank_h_flag)
            log_.info('----------当前分钟超过40，使用bottom的data，完成----------')
        else:
            # 数据没准备好，1分钟后重新检查
            log_.info("上游数据未就绪，等待...")
            Timer(60, h_timer_check).start()
        # 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} - 推荐视频数据更新完成\n"
        #              f"now_date: {datetime.datetime.strftime(now_date, '%Y%m%d')}\n"
        #              f"now_h: {now_h}\n"
        #              f"finished time: {datetime.datetime.strftime(datetime.datetime.now(), '%Y%m%d %H:%M:%S')}"
        # )
    except Exception as e:
        log_.error(f"地域分组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} - 地域分组小时级数据更新失败\n"
                     f"exception: {e}\n"
                     f"traceback: {traceback.format_exc()}"
        )


if __name__ == '__main__':
    log_.info("文件alg_recsys_recall_1h_region.py:「1小时地域」 开始执行")
    h_timer_check()