rov-offline/region_rule_rank_h_by24h.py

# -*- coding: utf-8 -*-
# @ModuleName: region_rule_rank_h
# @Author: Liqian
# @Time: 2022/5/5 15:54
# @Software: PyCharm

import time
import multiprocessing
import os
import gevent
import datetime
import pandas as pd
import math
from functools import reduce
from odps import ODPS
from threading import Timer, Thread
from utils import RedisHelper, get_data_from_odps, filter_video_status, check_table_partition_exits
from config import set_config
from log import Log

# os.environ['NUMEXPR_MAX_THREADS'] = '16'

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

region_code = config_.REGION_CODE

features = [
    'apptype',
    'code',  # 省份编码
    'videoid',
    'lastday_preview',  # 昨日预曝光人数
    'lastday_view',  # 昨日曝光人数
    'lastday_play',  # 昨日播放人数
    'lastday_share',  # 昨日分享人数
    'lastday_return',  # 昨日回流人数
    'lastday_preview_total',  # 昨日预曝光次数
    'lastday_view_total',  # 昨日曝光次数
    'lastday_play_total',  # 昨日播放次数
    'lastday_share_total',  # 昨日分享次数
    'platform_return',
    'platform_preview',
    'platform_preview_total',
    'platform_show',
    'platform_show_total',
    'platform_view',
    'platform_view_total',
]


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 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_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(df, param):
    """
    计算score
    :param df: 特征数据
    :param param:
    :return:
    """
    # score计算公式: sharerate*backrate*logback*ctr
    # sharerate = lastday_share/(lastday_play+1000)
    # backrate = lastday_return/(lastday_share+10)
    # ctr = lastday_play/(lastday_preview+1000), 对ctr限最大值：K2 = 0.6 if ctr > 0.6 else ctr
    # score = sharerate * backrate * LOG(lastday_return+1) * K2

    df = df.fillna(0)
    df['share_rate'] = df['lastday_share'] / (df['lastday_play'] + 1000)
    df['back_rate'] = df['lastday_return'] / (df['lastday_share'] + 10)
    df['log_back'] = (df['lastday_return'] + 1).apply(math.log)
    if param.get('view_type', None) == 'video-show':
        df['ctr'] = df['lastday_play'] / (df['platform_show'] + 1000)
    else:
        df['ctr'] = df['lastday_play'] / (df['lastday_preview'] + 1000)
    df['K2'] = df['ctr'].apply(lambda x: 0.6 if x > 0.6 else x)
    df['score'] = df['share_rate'] * df['back_rate'] * df['log_back'] * df['K2']
    df['platform_return_rate'] = df['platform_return'] / df['lastday_return']
    df = df.sort_values(by=['score'], ascending=False)
    return df


def video_rank(df, now_date, now_h, rule_key, param, region, data_key):
    """
    获取符合进入召回源条件的视频
    :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
    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['lastday_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)
    h_recall_videos = h_recall_df['videoid'].to_list()
    # log_.info(f'day_recall videos count = {len(h_recall_videos)}')

    # 视频状态过滤
    filtered_videos = filter_video_status(h_recall_videos)
    # log_.info('filtered_videos count = {}'.format(len(filtered_videos)))

    # 写入对应的redis
    h_video_ids = []
    day_recall_result = {}
    for video_id in filtered_videos:
        score = h_recall_df[h_recall_df['videoid'] == video_id]['score']
        # print(score)
        day_recall_result[int(video_id)] = float(score)
        h_video_ids.append(int(video_id))

    day_recall_key_name = \
        f"{config_.RECALL_KEY_NAME_PREFIX_REGION_BY_24H}{region}:{data_key}:{rule_key}:" \
        f"{datetime.datetime.strftime(now_date, '%Y%m%d')}:{now_h}"
    if len(day_recall_result) > 0:
        redis_helper.add_data_with_zset(key_name=day_recall_key_name, data=day_recall_result, expire_time=2 * 3600)
        # 清空线上过滤应用列表
        # redis_helper.del_keys(key_name=f"{config_.REGION_H_VIDEO_FILER_24H}{region}.{app_type}.{data_key}.{rule_key}")

    # 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, 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', 'lastday_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):
    log_.info(f"region = {region} start...")
    # 计算score
    region_df = df_merged[df_merged['code'] == region]
    log_.info(f'region = {region}, region_df count = {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, region=region,
               rule_key=rule_key, param=rule_param, data_key=data_key)
    log_.info(f"region = {region} end!")


def process_with_region2(region, df_merged, data_key, rule_key, rule_param, now_date, now_h):
    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)
    log_.info(f"region = {region} end!")


def process_with_app_type(app_type, params, region_code_list, feature_df, now_date, now_h):
    log_.info(f"app_type = {app_type} start...")
    data_params_item = params.get('data_params')
    rule_params_item = params.get('rule_params')
    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 = [
            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)
    log_.info(f"app_type = {app_type} end!")


def process_with_param(param, data_params_item, rule_params_item, region_code_list, feature_df, now_date, now_h):
    log_.info(f"param = {param} start...")
    data_key = param.get('data')
    data_param = data_params_item.get(data_key)
    log_.info(f"data_key = {data_key}, data_param = {data_param}")
    rule_key = param.get('rule')
    rule_param = rule_params_item.get(rule_key)
    log_.info(f"rule_key = {rule_key}, rule_param = {rule_param}")
    merge_func = rule_param.get('merge_func', None)
    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['lastday_return']
        task_list = [
            gevent.spawn(process_with_region2, region, df_merged, data_key, rule_key, rule_param, now_date, now_h)
            for region in region_code_list
        ]
    else:
        df_list = [feature_df[feature_df['apptype'] == apptype] for apptype, _ in data_param.items()]
        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)
            for region in region_code_list
        ]

    gevent.joinall(task_list)
    log_.info(f"param = {param} end!")


def rank_by_24h(project, table, now_date, now_h, rule_params, region_code_list):
    # 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)
    # rank
    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)
        )
    pool.close()
    pool.join()

    """
    pool = multiprocessing.Pool(processes=len(config_.APP_TYPE))
    for app_type, params in rule_params.items():
        pool.apply_async(func=process_with_app_type,
                         args=(app_type, params, region_code_list, feature_df, now_date, now_h))
    pool.close()
    pool.join()
    """

    # for app_type, params in rule_params.items():
    #     log_.info(f"app_type = {app_type}")
    #     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 = [
    #                 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)


    # for key, value in rule_params.items():
    #     log_.info(f"rule = {key}, param = {value}")
    #     for region in region_code_list:
    #         log_.info(f"region = {region}")
    #         # 计算score
    #         region_df = feature_df[feature_df['code'] == region]
    #         log_.info(f'region_df count = {len(region_df)}')
    #         score_df = cal_score(df=region_df, param=value)
    #         video_rank(df=score_df, now_date=now_date, now_h=now_h, rule_key=key, param=value, region=region)
    #         # to-csv
    #         score_filename = f"score_24h_{region}_{key}_{datetime.datetime.strftime(now_date, '%Y%m%d%H')}.csv"
    #         score_df.to_csv(f'./data/{score_filename}')
    #         # to-logs
    #         log_.info({"date": datetime.datetime.strftime(now_date, '%Y%m%d%H'),
    #                    "region_code": region,
    #                    "redis_key_prefix": config_.RECALL_KEY_NAME_PREFIX_REGION_BY_24H,
    #                    "rule_key": key,
    #                    # "score_df": score_df[['videoid', 'score']]
    #                    })


def dup_to_redis(h_video_ids, now_date, now_h, rule_key, region):
    """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"""
    redis_helper = RedisHelper()

    # ##### 去重小程序天级更新结果，并另存为redis中
    day_key_name = f"{config_.RECALL_KEY_NAME_PREFIX_BY_DAY}rule2.{datetime.datetime.strftime(now_date, '%Y%m%d')}"
    if redis_helper.key_exists(key_name=day_key_name):
        day_data = redis_helper.get_all_data_from_zset(key_name=day_key_name, with_scores=True)
        log_.info(f'day data count = {len(day_data)}')
        day_dup = {}
        for video_id, score in day_data:
            if int(video_id) not in h_video_ids:
                day_dup[int(video_id)] = score
                h_video_ids.append(int(video_id))
        log_.info(f"day data dup count = {len(day_dup)}")
        day_dup_key_name = \
            f"{config_.RECALL_KEY_NAME_PREFIX_DUP_REGION_DAY_24H}{region}.{rule_key}." \
            f"{datetime.datetime.strftime(now_date, '%Y%m%d')}.{now_h}"
        if len(day_dup) > 0:
            redis_helper.add_data_with_zset(key_name=day_dup_key_name, data=day_dup, expire_time=23 * 3600)

    # ##### 去重小程序模型更新结果，并另存为redis中
    model_key_name = get_rov_redis_key(now_date=now_date)
    model_data = redis_helper.get_all_data_from_zset(key_name=model_key_name, with_scores=True)
    log_.info(f'model data count = {len(model_data)}')
    model_data_dup = {}
    for video_id, score in model_data:
        if int(video_id) not in h_video_ids:
            model_data_dup[int(video_id)] = score
            h_video_ids.append(int(video_id))
    log_.info(f"model data dup count = {len(model_data_dup)}")
    model_data_dup_key_name = \
        f"{config_.RECALL_KEY_NAME_PREFIX_DUP_REGION_24H}{region}.{rule_key}." \
        f"{datetime.datetime.strftime(now_date, '%Y%m%d')}.{now_h}"
    if len(model_data_dup) > 0:
        redis_helper.add_data_with_zset(key_name=model_data_dup_key_name, data=model_data_dup, expire_time=23 * 3600)


def h_rank_bottom(now_date, now_h, rule_params, region_code_list):
    """未按时更新数据，用上一小时结果作为当前小时的数据"""
    redis_helper = RedisHelper()
    if now_h == 0:
        redis_dt = datetime.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_24H
    for param in rule_params.get('params_list'):
        data_key = param.get('data')
        rule_key = param.get('rule')
        log_.info(f"data_key = {data_key}, rule_key = {rule_key}")
        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 * 3600)

    """
    for app_type, params in rule_params.items():
        log_.info(f"app_type = {app_type}")
        for param in params.get('params_list'):
            data_key = param.get('data')
            rule_key = param.get('rule')
            log_.info(f"data_key = {data_key}, rule_key = {rule_key}")
            for region in region_code_list:
                log_.info(f"region = {region}")
                key_name = f"{key_prefix}{region}:{app_type}:{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}:{app_type}:{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 * 3600)
                # 清空线上过滤应用列表
                # redis_helper.del_keys(key_name=f"{config_.REGION_H_VIDEO_FILER_24H}{region}.{app_type}.{data_key}.{rule_key}")

                # 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, region=region)
    """


def h_timer_check():
    rule_params = config_.RULE_PARAMS_REGION_24H_APP_TYPE
    project = config_.PROJECT_REGION_24H_APP_TYPE
    table = config_.TABLE_REGION_24H_APP_TYPE
    region_code_list = [code for region, code in region_code.items() if code != '-1']
    now_date = datetime.datetime.today()
    now_h = datetime.datetime.now().hour
    now_min = datetime.datetime.now().minute
    log_.info(f"now_date: {datetime.datetime.strftime(now_date, '%Y%m%d%H')}")
    # 查看当天更新的数据是否已准备好
    h_data_count = data_check(project=project, table=table, now_date=now_date)
    if h_data_count > 0:
        log_.info(f'region_24h_data_count = {h_data_count}')
        # 数据准备好，进行更新
        rank_by_24h(now_date=now_date, now_h=now_h, rule_params=rule_params,
                    project=project, table=table, region_code_list=region_code_list)
        log_.info(f"region_24h_data end!")
    elif now_min > 50:
        log_.info('24h_recall data is None, use bottom data!')
        h_rank_bottom(now_date=now_date, now_h=now_h, rule_params=rule_params, region_code_list=region_code_list)
        log_.info(f"region_24h_data end!")
    else:
        # 数据没准备好，1分钟后重新检查
        Timer(60, h_timer_check).start()


if __name__ == '__main__':
    log_.info(f"region_24h_data start...")
    h_timer_check()