rov-offline/ad_threshold_auto_update.py

import copy
import datetime
import traceback
import math
import numpy as np
from threading import Timer

import pandas as pd

from utils import RedisHelper, data_check, get_feature_data, send_msg_to_feishu, request_get
from config import set_config
from log import Log
config_, _ = set_config()
log_ = Log()
redis_helper = RedisHelper()

features = [
    'apptype',
    '分组',
    '广告uv'
]


def get_threshold_record_new(ad_abtest_abcode_config, feature_df, threshold_record):
    """根据活跃人数变化计算新的阈值参数"""
    robot_msg_record = []
    threshold_record_new = threshold_record.copy()
    for app_type, config_params in ad_abtest_abcode_config.items():
        # 获取对应端的数据, 更新阈值参数
        # log_.info(f"app_type = {app_type}")
        temp_df = feature_df[feature_df['apptype'] == app_type]
        ab_test_id = config_params.get('ab_test_id')
        ab_test_config = config_params.get('ab_test_config')
        up_threshold_update = config_params.get('up_threshold_update')
        down_threshold_update = config_params.get('down_threshold_update')
        for config_name, ab_code_list in ab_test_config.items():
            ad_abtest_tag = f"{ab_test_id}-{config_name}"
            # log_.info(f"ad_abtest_tag = {ad_abtest_tag}")
            if len(ab_code_list) > 0:
                b_mean = temp_df[temp_df['adcode'].isin(ab_code_list)]['b'].mean()
                if b_mean < 0:
                    # 阈值按梯度调高
                    gradient = up_threshold_update[config_name].get('gradient')
                    update_range = up_threshold_update[config_name].get('update_range')
                    b_i = (b_mean * -1) // gradient + 1
                    threshold_param_new = float(threshold_record.get(ad_abtest_tag)) + update_range * b_i
                elif b_mean > 0.1:
                    # 阈值按梯度调低
                    gradient = down_threshold_update[config_name].get('gradient')
                    update_range = down_threshold_update[config_name].get('update_range')
                    b_i = (b_mean - 0.1) // gradient + 1
                    threshold_param_new = float(threshold_record.get(ad_abtest_tag)) - update_range * b_i
                else:
                    continue
                if threshold_param_new > 0:
                    threshold_record_new[ad_abtest_tag] = threshold_param_new
                    robot_msg_record.append({'appType': app_type, 'abtestTag': ad_abtest_tag,
                                             'gradient': round(gradient, 4), 'range': round(update_range, 4),
                                             'i': int(b_i),
                                             'paramOld': round(float(threshold_record.get(ad_abtest_tag)), 4),
                                             'paramNew': round(threshold_param_new, 4)})
    return threshold_record_new, robot_msg_record


def get_threshold_record_new_by_uv(ad_abtest_abcode_config, feature_df, threshold_record, ad_target_uv):
    """根据广告uv计算新的阈值参数"""
    robot_msg_record = []
    threshold_record_new = copy.deepcopy(threshold_record)
    # 根据目标uv进行调整
    for app_type, target_uv_mapping in ad_target_uv.items():
        # 获取app_type对应的目标uv
        temp_df = feature_df[feature_df['apptype'] == int(app_type)]
        # 获取app_type对应的阈值调整参数
        update_threshold_params = ad_abtest_abcode_config.get(int(app_type))
        ab_test_id = update_threshold_params.get('ab_test_id')
        not_update = update_threshold_params.get('not_update')
        gradient = update_threshold_params.get('gradient')
        max_update_step = update_threshold_params.get('max_update_step')
        threshold_update_mapping = update_threshold_params.get('threshold_update')
        threshold_record_old = threshold_record.get(ab_test_id)
        # print(ab_test_id, threshold_record, threshold_record_old)
        for ab_test_group, target_uv in target_uv_mapping.items():
            if target_uv is None:
                continue
            update_range = threshold_update_mapping.get(ab_test_group)
            # 获取对应组的当前uv
            try:
                current_uv = temp_df[temp_df['分组'] == ab_test_group]['广告uv'].values[0]
            except Exception as e:
                continue
            # 计算uv差值
            uv_differ = current_uv - target_uv
            if abs(uv_differ) < not_update:
                continue
            # 获取当前阈值参数
            threshold_param_old = threshold_record_old[ab_test_group].get('group')
            if uv_differ < 0:
                # 当前uv < 目标uv，阈值按梯度调低
                step = math.ceil((uv_differ * -1) / gradient)
                step = max_update_step if step > max_update_step else step
                threshold_param_new = float(threshold_param_old) - update_range * step
            elif uv_differ > 0:
                # 当前uv > 目标uv，阈值按梯度调高
                step = math.ceil(uv_differ / gradient)
                step = max_update_step if step > max_update_step else step
                threshold_param_new = float(threshold_param_old) + update_range * step
            else:
                continue
            if threshold_param_new > 0:
                log_.info(
                    {
                        'appType': app_type, 'abtestid': ab_test_id, 'abTestGroup': ab_test_group,
                        'targetUv': target_uv, 'currentUv': round(current_uv, 4),
                        'uvDiffer':  round(uv_differ, 4), 'gradient': round(gradient, 4), 'step': step,
                        'range': round(update_range, 4),
                        'paramOld': round(float(threshold_param_old), 4),
                        'paramNew': round(threshold_param_new, 4)
                    }
                )
                threshold_record_new[ab_test_id][ab_test_group]['group'] = threshold_param_new
                threshold_record_new[ab_test_id][ab_test_group]['mean_group'] = threshold_param_new
                robot_msg_record.append(
                    {
                        'appType': app_type, 'abtestid': ab_test_id, 'abTestGroup': ab_test_group,
                        'targetUv': target_uv, 'currentUv': round(current_uv, 4),
                        'uvDiffer':  round(uv_differ, 4), 'gradient': round(gradient, 4), 'step': step,
                        'range': round(update_range, 4),
                        'paramOld': round(float(threshold_param_old), 4),
                        'paramNew': round(threshold_param_new, 4)
                    }
                )
    return threshold_record_new, robot_msg_record


def update_threshold(threshold_record_old, threshold_record_new):
    """更新阈值"""
    # 获取用户组列表
    ad_mid_group_list = [group for class_key, group_list in config_.AD_MID_GROUP.items()
                         for group in group_list]
    ad_mid_group_list.append("mean_group")
    ad_mid_group_list = list(set(ad_mid_group_list))
    # 获取实验配置列表
    ad_abtest_config_mapping = {}
    abtest_id_list = []
    for key, val in config_.AD_ABTEST_CONFIG.items():
        abtest_id, abtest_config_tag = key.split('-')
        if abtest_id in abtest_id_list:
            ad_abtest_config_mapping[abtest_id].append((abtest_config_tag, val))
        else:
            abtest_id_list.append(abtest_id)
            ad_abtest_config_mapping[abtest_id] = [(abtest_config_tag, val)]
    log_.info(f"ad_abtest_config_mapping = {ad_abtest_config_mapping}")

    # 计算新的阈值并更新
    for abtest_id, threshold_param_mapping in threshold_record_new.items():
        for abtest_group, threshold_param_new in threshold_param_mapping.items():
            threshold_param_old = threshold_record_old[abtest_id].get(abtest_group)
            if str(threshold_param_old) == str(threshold_param_new):
                # print(abtest_id, abtest_group, threshold_param_old, threshold_param_new)
                continue
            log_.info(f"abtest_id = {abtest_id}, abtest_group = {abtest_group}, "
                      f"threshold_param_old = {threshold_param_old}, threshold_param_new = {threshold_param_new}")
            for abtest_config_tag, config_val in ad_abtest_config_mapping.get(abtest_id, []):
                for group_key in ad_mid_group_list:
                    # 获取对应的阈值
                    key_name = \
                        f"{config_.KEY_NAME_PREFIX_AD_THRESHOLD}{abtest_id}:{abtest_config_tag}:{abtest_group}:{group_key}"
                    threshold_old = redis_helper.get_data_from_redis(key_name=key_name)
                    if threshold_old is None:
                        continue
                    # 计算新的阈值
                    if group_key == 'mean_group':
                        threshold_new = \
                            float(threshold_old) / threshold_param_old['mean_group'] * threshold_param_new['mean_group']
                    else:
                        threshold_new = \
                            float(threshold_old) / threshold_param_old['group'] * threshold_param_new['group']

                    # 更新redis
                    redis_helper.set_data_to_redis(key_name=key_name, value=threshold_new, expire_time=2 * 24 * 3600)
                    log_.info(f"abtest_id = {abtest_id}, abtest_config_tag = {abtest_config_tag}, "
                              f"abtest_group = {abtest_group}, group_key = {group_key}, "
                              f"threshold_old = {threshold_old}, threshold_new = {threshold_new}")

                    # 关怀模式实验阈值更新
                    care_model = config_val.get('care_model', None)
                    threshold_rate = config_val.get('threshold_rate', None)
                    if care_model is True:
                        care_model_key_name = \
                            f"{config_.KEY_NAME_PREFIX_AD_THRESHOLD_CARE_MODEL}{abtest_id}:{abtest_config_tag}:{abtest_group}:{group_key}"
                        care_model_threshold_old = redis_helper.get_data_from_redis(key_name=care_model_key_name)
                        care_model_threshold_new = threshold_new * threshold_rate
                        redis_helper.set_data_to_redis(key_name=care_model_key_name,
                                                       value=care_model_threshold_new, expire_time=2 * 24 * 3600)
                        log_.info(f"abtest_id = {abtest_id}, abtest_config_tag = {abtest_config_tag}, "
                                  f"abtest_group = {abtest_group}, group_key = {group_key}, "
                                  f"care_model_threshold_old = {care_model_threshold_old}, "
                                  f"care_model_threshold_new = {care_model_threshold_new}")


def update_ad_abtest_threshold(project, table, dt, ad_abtest_abcode_config, ad_target_uv):
    # 获取当前阈值参数值
    threshold_record = redis_helper.get_data_from_redis(key_name=config_.KEY_NAME_PREFIX_AD_THRESHOLD_RECORD)
    threshold_record = eval(threshold_record)
    log_.info(f"threshold_record = {threshold_record}")
    # 获取uv数据
    feature_df = get_feature_data(project=project, table=table, features=features, dt=dt)
    feature_df['apptype'] = feature_df['apptype'].astype(int)
    feature_df['广告uv'] = feature_df['广告uv'].astype(float)
    # 根据广告uv变化计算新的阈值参数
    threshold_record_new, robot_msg_record = get_threshold_record_new_by_uv(
        ad_abtest_abcode_config=ad_abtest_abcode_config, feature_df=feature_df,
        threshold_record=threshold_record, ad_target_uv=ad_target_uv)
    log_.info(f"threshold_record_new = {threshold_record_new}")
    # 更新阈值
    update_threshold(threshold_record_old=threshold_record, threshold_record_new=threshold_record_new)
    # 更新阈值参数
    redis_helper.set_data_to_redis(key_name=config_.KEY_NAME_PREFIX_AD_THRESHOLD_RECORD,
                                   value=str(threshold_record_new), expire_time=2 * 24 * 3600)
    return robot_msg_record


def get_ad_target_uv():
    """获取管理后台开启自动调整阈值开关的目标uv值"""
    ad_target_uv = {}
    result = request_get(request_url=config_.GET_AD_TARGET_UV_URL)
    if result is None:
        log_.info('获取管理后台广告目标uv值失败！')
        return ad_target_uv
    if result['code'] != 0:
        log_.info('获取管理后台广告目标uv值失败！')
        return ad_target_uv
    if not result['content']:
        return ad_target_uv
    for item in result['content']:
        app_type = item['productId']
        target_uv_mapping = {}
        for uv_item in item['uvTargetDetails']:
            ab_group = uv_item['abParam']
            target_uv = uv_item['uvTarget']
            target_uv_mapping[ab_group] = target_uv
        ad_target_uv[app_type] = target_uv_mapping
    return ad_target_uv


def timer_check():
    try:
        # 获取自动调整阈值参数
        ad_abtest_abcode_config = config_.AD_ABTEST_ABCODE_CONFIG
        # 自动调整阈值参数存储至redis
        redis_helper.set_data_to_redis(key_name=config_.KEY_NAME_PREFIX_AD_THRESHOLD_PARAM_RECORD,
                                       value=str(ad_abtest_abcode_config),
                                       expire_time=24 * 3600)
        project = config_.AD_THRESHOLD_AUTO_UPDATE_DATA.get('project')
        table = config_.AD_THRESHOLD_AUTO_UPDATE_DATA.get('table')
        now_date = datetime.datetime.today()
        now_min = datetime.datetime.now().minute
        log_.info(f"now_date: {datetime.datetime.strftime(now_date, '%Y%m%d%H')}")

        # 管理后台获取开启自动调整阈值开关的目标uv值
        ad_target_uv = get_ad_target_uv()
        log_.info(f"ad_target_uv: {ad_target_uv}")
        if len(ad_target_uv) == 0:
            return

        # 查看当前更新的数据是否已准备好
        dt = datetime.datetime.strftime(now_date - datetime.timedelta(hours=1), '%Y%m%d%H')
        data_count = data_check(project=project, table=table, dt=dt)
        if data_count > 0:
            log_.info(f"data count = {data_count}")
            # 数据准备好，进行更新
            robot_msg_record = update_ad_abtest_threshold(
                project=project, table=table, dt=dt,
                ad_abtest_abcode_config=ad_abtest_abcode_config, ad_target_uv=ad_target_uv)
            if len(robot_msg_record) > 0:
                robot_msg_record_text = "\n".join([str(item) for item in robot_msg_record])
                msg = f"threshold_param_update: \n{robot_msg_record_text.replace(', ', ',   ')}\n"
            else:
                msg = "无需更新！\n"
            send_msg_to_feishu(
                webhook=config_.FEISHU_ROBOT['ad_threshold_auto_update_robot'].get('webhook'),
                key_word=config_.FEISHU_ROBOT['ad_threshold_auto_update_robot'].get('key_word'),
                msg_text=f"rov-offline{config_.ENV_TEXT} - 阈值更新完成！\n{msg}"

            )
            log_.info(f"threshold update end!")
        elif now_min > 30:
            log_.info('threshold update data is None!')
            send_msg_to_feishu(
                webhook=config_.FEISHU_ROBOT['ad_threshold_auto_update_robot'].get('webhook'),
                key_word=config_.FEISHU_ROBOT['ad_threshold_auto_update_robot'].get('key_word'),
                msg_text=f"rov-offline{config_.ENV_TEXT} - 阈值更新相关数据未准备好！\n"
            )
        else:
            # 数据没准备好，1分钟后重新检查
            Timer(60, timer_check).start()

    except Exception as e:
        log_.error(f"阈值更新失败, exception: {e}, traceback: {traceback.format_exc()}")
        send_msg_to_feishu(
            webhook=config_.FEISHU_ROBOT['ad_threshold_auto_update_robot'].get('webhook'),
            key_word=config_.FEISHU_ROBOT['ad_threshold_auto_update_robot'].get('key_word'),
            msg_text=f"rov-offline{config_.ENV_TEXT} - 阈值更新失败\n"
                     f"exception: {e}\n"
                     f"traceback: {traceback.format_exc()}"
        )


if __name__ == '__main__':
    timer_check()