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 my_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 my_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',
            # 'score_func': '20240223',
            # 'lastonehour_allreturn': "1",
            '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）

    'lastonehour_allreturn',
    'lastonehour_allreturn_sharecnt'
]


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')
        # 测试 张博
        check_res = check_table_partition_exits(date=dt, project=project, table=table)
        if check_res:
            sql = f'select * from {project}.{table} where dt = "{dt}"'
            print("zhangbo-sql-是否有数据")
            print(sql)
            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')
    # 张博 测试
    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_20240223(df, param):
    """
    计算score
    :param df: 特征数据
    :param param: 规则参数
    :return:
    """
    log_.info("进入了cal_score_initial_20240223")
    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_rate_new'] = (df['lastonehour_return'] + 1) / (df['lastonehour_share'] + 10)
    df['back_rate_all'] = df['lastonehour_allreturn'] / (df['lastonehour_allreturn_sharecnt'] + 10)
    df['log_back'] = (df['lastonehour_return'] + 1).apply(math.log)
    df['log_back_all'] = (df['lastonehour_allreturn'] + 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_new'] + 0.01 * df['back_rate_all']
    ) * (
            df['log_back'] + 0.01 * df['log_back_all']
    ) * df['K2']
    df = df.sort_values(by=['score'], ascending=False)
    return 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)
        elif param.get('score_func', None) == '20240223':
            df = cal_score_initial_20240223(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)]
    h_recall_df = df[
        (df['lastonehour_return'] >= return_count) &
        (df['score'] >= score_value) &
        (df['platform_return_rate'] >= platform_return_rate)
        ]
    if "lastonehour_allreturn" in param.keys():
        log_.info("采用 lastonehour_allreturn 过滤")
        h_recall_df = df[
            (df['lastonehour_allreturn'] > 0)
        ]
    # try:
    #     if "return_countv2" in param.keys() and "platform_return_ratev2" in param.keys():
    #         return_countv2 = param["return_countv2"]
    #         platform_return_ratev2 = param["platform_return_ratev2"]
    #         h_recall_df = h_recall_df[
    #             df['platform_return_rate'] >= platform_return_ratev2 |
    #             (df['platform_return_rate'] < platform_return_ratev2 & df['lastonehour_return'] > return_countv2)
    #             ]
    # except Exception as e:
    #     log_.error("return_countv2 is wrong with{}".format(e))

    # 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()