recommend-emr-dataprocess/ad/pai_flow_operator.py

# -*- coding: utf-8 -*-
import os
import re
import sys

from typing import List


import time
import json
import pandas as pd
from alibabacloud_paistudio20210202.client import Client as PaiStudio20210202Client
from alibabacloud_tea_openapi import models as open_api_models
from alibabacloud_paistudio20210202 import models as pai_studio_20210202_models
from alibabacloud_tea_util import models as util_models
from alibabacloud_tea_util.client import Client as UtilClient
from alibabacloud_eas20210701.client import Client as eas20210701Client
from alibabacloud_paiflow20210202 import models as paiflow_20210202_models
from alibabacloud_paiflow20210202.client import Client as PAIFlow20210202Client
from datetime import datetime, timedelta
from odps import ODPS
from ad_monitor_util import _monitor

target_names = {
    '样本shuffle',
    '模型训练-样本shufle',
    '模型训练-自定义',
    '模型增量训练',
    '模型导出-2',
    '更新EAS服务(Beta)-1',
    '虚拟起始节点',
    '二分类评估-1',
    '二分类评估-2',
    '预测结果对比'
}

experiment_id = "draft-kbezr8f0q3cpee9eqc"

def get_odps_instance(project):
    odps = ODPS(
        access_id='LTAIWYUujJAm7CbH',
        secret_access_key='RfSjdiWwED1sGFlsjXv0DlfTnZTG1P',
        project=project,
        endpoint='http://service.cn.maxcompute.aliyun.com/api',
    )
    return odps


def get_data_from_odps(project, table, num):
    odps = get_odps_instance(project)
    try:
        # 要查询的 SQL 语句
        sql = f'select * from {table} limit {num}'
        # 执行 SQL 查询
        with odps.execute_sql(sql).open_reader() as reader:
            # 查询数量小于目标数量时 返回空
            if reader.count < num:
                return None
            # 获取字段名称
            column_names = reader.schema.names
            # 获取查询结果数据
            data = []
            for record in reader:
                record_list = list(record)
                numbers = []
                for item in record_list:
                    numbers.append(item[1])
                data.append(numbers)
            # 将数据和字段名称组合成 DataFrame
            df = pd.DataFrame(data, columns=column_names)
            return df
    except Exception as e:
        print(f"发生错误: {e}")


def get_dict_from_odps(project, table):
    odps = get_odps_instance(project)
    try:
        # 要查询的 SQL 语句
        sql = f'select * from {table}'
        # 执行 SQL 查询
        with odps.execute_sql(sql).open_reader() as reader:
            data = {}
            for record in reader:
                record_list = list(record)
                key = record_list[0][1]
                value = record_list[1][1]
                data[key] = value
            return data
    except Exception as e:
        print(f"发生错误: {e}")


def get_dates_between(start_date_str, end_date_str):
    start_date = datetime.strptime(start_date_str, '%Y%m%d')
    end_date = datetime.strptime(end_date_str, '%Y%m%d')
    dates = []
    current_date = start_date
    while current_date <= end_date:
        dates.append(current_date.strftime('%Y%m%d'))
        current_date += timedelta(days=1)
    return dates


def read_file_to_list():
    try:
        current_dir = os.getcwd()
        file_path = os.path.join(current_dir, 'holidays.txt')
        with open(file_path, 'r', encoding='utf-8') as file:
            content = file.read()
            return content.split('\n')
    except FileNotFoundError:
        print(f"错误：未找到 {file_path} 文件。")
    except Exception as e:
        print(f"错误：发生了一个未知错误: {e}")
    return []


def get_previous_days_date(days):
    current_date = datetime.now()
    previous_date = current_date - timedelta(days=days)
    return previous_date.strftime('%Y%m%d')


def remove_elements(lst1, lst2):
    return [element for element in lst1 if element not in lst2]


def process_list(lst, append_str):
    # 给列表中每个元素拼接相同的字符串
    appended_list = [append_str + element for element in lst]
    # 将拼接后的列表元素用逗号拼接成一个字符串
    result_str = ','.join(appended_list)
    return result_str


def get_train_data_list():
    start_date = '20250223'
    end_date = get_previous_days_date(2)
    date_list = get_dates_between(start_date, end_date)
    filter_date_list = read_file_to_list()
    date_list = remove_elements(date_list, filter_date_list)
    return date_list


def update_train_tables(old_str):
    date_list = get_train_data_list()
    address = 'odps://loghubods/tables/ad_easyrec_train_data_v3_sampled/dt='
    train_tables = process_list(date_list, address)
    start_index = old_str.find('-Dtrain_tables="')
    if start_index != -1:
        # 确定等号的位置
        equal_sign_index = start_index + len('-Dtrain_tables="')
        # 找到下一个双引号的位置
        next_quote_index = old_str.find('"', equal_sign_index)
        if next_quote_index != -1:
            # 进行替换
            new_value = old_str[:equal_sign_index] + train_tables + old_str[next_quote_index:]
            return new_value
    return None

def new_update_train_tables(old_str):
    date_list = get_train_data_list()
    train_list = ["'" + item + "'" for item in date_list]
    result = ','.join(train_list)
    start_index = old_str.find('where dt in (')
    if start_index != -1:
        equal_sign_index = start_index + len('where dt in (')
        # 找到下一个双引号的位置
        next_quote_index = old_str.find(')', equal_sign_index)
        if next_quote_index != -1:
            # 进行替换
            new_value = old_str[:equal_sign_index] + result + old_str[next_quote_index:]
            return new_value
    return None


class PAIClient:
    def __init__(self):
        pass

    @staticmethod
    def create_client() -> PaiStudio20210202Client:
        """
        使用AK&SK初始化账号Client
        @return: Client
        @throws Exception
        """
        # 工程代码泄露可能会导致 AccessKey 泄露，并威胁账号下所有资源的安全性。以下代码示例仅供参考。
        # 建议使用更安全的 STS 方式，更多鉴权访问方式请参见：https://help.aliyun.com/document_detail/378659.html。
        config = open_api_models.Config(
            access_key_id="LTAI5tFGqgC8f3mh1fRCrAEy",
            access_key_secret="XhOjK9XmTYRhVAtf6yii4s4kZwWzvV"
        )
        # Endpoint 请参考 https://api.aliyun.com/product/PaiStudio
        config.endpoint = f'pai.cn-hangzhou.aliyuncs.com'
        return PaiStudio20210202Client(config)

    @staticmethod
    def create_eas_client() -> eas20210701Client:
        """
        使用AK&SK初始化账号Client
        @return: Client
        @throws Exception
        """
        # 工程代码泄露可能会导致 AccessKey 泄露，并威胁账号下所有资源的安全性。以下代码示例仅供参考。
        # 建议使用更安全的 STS 方式，更多鉴权访问方式请参见：https://help.aliyun.com/document_detail/378659.html。
        config = open_api_models.Config(
            access_key_id="LTAI5tFGqgC8f3mh1fRCrAEy",
            access_key_secret="XhOjK9XmTYRhVAtf6yii4s4kZwWzvV"
        )
        # Endpoint 请参考 https://api.aliyun.com/product/PaiStudio
        config.endpoint = f'pai-eas.cn-hangzhou.aliyuncs.com'
        return eas20210701Client(config)

    @staticmethod
    def create_flow_client() -> PAIFlow20210202Client:
        """
        使用AK&SK初始化账号Client
        @return: Client
        @throws Exception
        """
        # 工程代码泄露可能会导致 AccessKey 泄露，并威胁账号下所有资源的安全性。以下代码示例仅供参考。
        # 建议使用更安全的 STS 方式，更多鉴权访问方式请参见：https://help.aliyun.com/document_detail/378659.html。
        config = open_api_models.Config(
            # 必填，请确保代码运行环境设置了环境变量 ALIBABA_CLOUD_ACCESS_KEY_ID。,
            access_key_id="LTAI5tFGqgC8f3mh1fRCrAEy",
            # 必填，请确保代码运行环境设置了环境变量 ALIBABA_CLOUD_ACCESS_KEY_SECRET。,
            access_key_secret="XhOjK9XmTYRhVAtf6yii4s4kZwWzvV"
        )
        # Endpoint 请参考 https://api.aliyun.com/product/PAIFlow
        config.endpoint = f'paiflow.cn-hangzhou.aliyuncs.com'
        return PAIFlow20210202Client(config)

    @staticmethod
    def get_work_flow_draft_list(workspace_id: str):
        client = PAIClient.create_client()
        list_experiments_request = pai_studio_20210202_models.ListExperimentsRequest(
            workspace_id=workspace_id
        )
        runtime = util_models.RuntimeOptions()
        headers = {}
        try:
            resp = client.list_experiments_with_options(list_experiments_request, headers, runtime)
            return resp.body.to_map()
        except Exception as error:
            print(error.message)
            print(error.data.get("Recommend"))
            UtilClient.assert_as_string(error.message)

    @staticmethod
    def get_work_flow_draft(experiment_id: str):
        client = PAIClient.create_client()
        runtime = util_models.RuntimeOptions()
        headers = {}
        try:
            # 复制代码运行请自行打印 API 的返回值
            resp = client.get_experiment_with_options(experiment_id, headers, runtime)
            return resp.body.to_map()
        except Exception as error:
            # 此处仅做打印展示，请谨慎对待异常处理，在工程项目中切勿直接忽略异常。
            # 错误 message
            print(error.message)
            # 诊断地址
            print(error.data.get("Recommend"))
            UtilClient.assert_as_string(error.message)

    @staticmethod
    def get_describe_service(service_name: str):
        client = PAIClient.create_eas_client()
        runtime = util_models.RuntimeOptions()
        headers = {}
        try:
            # 复制代码运行请自行打印 API 的返回值
            resp = client.describe_service_with_options('cn-hangzhou', service_name, headers, runtime)
            return resp.body.to_map()
        except Exception as error:
            # 此处仅做打印展示，请谨慎对待异常处理，在工程项目中切勿直接忽略异常。
            # 错误 message
            print(error.message)
            # 诊断地址
            print(error.data.get("Recommend"))
            UtilClient.assert_as_string(error.message)

    @staticmethod
    def update_experiment_content(experiment_id: str, content: str, version: int):
        client = PAIClient.create_client()
        update_experiment_content_request = pai_studio_20210202_models.UpdateExperimentContentRequest(content=content,
                                                                                                      version=version)
        runtime = util_models.RuntimeOptions()
        headers = {}
        try:
            # 复制代码运行请自行打印 API 的返回值
            resp = client.update_experiment_content_with_options(experiment_id, update_experiment_content_request,
                                                                 headers, runtime)
            print(resp.body.to_map())
        except Exception as error:
            # 此处仅做打印展示，请谨慎对待异常处理，在工程项目中切勿直接忽略异常。
            # 错误 message
            print(error.message)
            # 诊断地址
            print(error.data.get("Recommend"))
            UtilClient.assert_as_string(error.message)

    @staticmethod
    def create_job(experiment_id: str, node_id: str, execute_type: str):
        client = PAIClient.create_client()
        create_job_request = pai_studio_20210202_models.CreateJobRequest()
        create_job_request.experiment_id = experiment_id
        create_job_request.node_id = node_id
        create_job_request.execute_type = execute_type
        runtime = util_models.RuntimeOptions()
        headers = {}
        try:
            # 复制代码运行请自行打印 API 的返回值
            resp = client.create_job_with_options(create_job_request, headers, runtime)
            return resp.body.to_map()
        except Exception as error:
            # 此处仅做打印展示，请谨慎对待异常处理，在工程项目中切勿直接忽略异常。
            # 错误 message
            print(error.message)
            # 诊断地址
            print(error.data.get("Recommend"))
            UtilClient.assert_as_string(error.message)

    @staticmethod
    def get_job_detail(job_id: str):
        client = PAIClient.create_client()
        get_job_request = pai_studio_20210202_models.GetJobRequest(
            verbose=False
        )
        runtime = util_models.RuntimeOptions()
        headers = {}
        try:
            # 复制代码运行请自行打印 API 的返回值
            resp = client.get_job_with_options(job_id, get_job_request, headers, runtime)
            return resp.body.to_map()
        except Exception as error:
            # 此处仅做打印展示，请谨慎对待异常处理，在工程项目中切勿直接忽略异常。
            # 错误 message
            print(error.message)
            # 诊断地址
            print(error.data.get("Recommend"))
            UtilClient.assert_as_string(error.message)

    @staticmethod
    def get_flow_out_put(pipeline_run_id: str, node_id: str, depth: int):
        client = PAIClient.create_flow_client()
        list_pipeline_run_node_outputs_request = paiflow_20210202_models.ListPipelineRunNodeOutputsRequest(
            depth=depth
        )
        runtime = util_models.RuntimeOptions()
        headers = {}
        try:
            # 复制代码运行请自行打印 API 的返回值
            resp = client.list_pipeline_run_node_outputs_with_options(pipeline_run_id, node_id,
                                                                      list_pipeline_run_node_outputs_request, headers,
                                                                      runtime)
            return resp.body.to_map()
        except Exception as error:
            # 此处仅做打印展示，请谨慎对待异常处理，在工程项目中切勿直接忽略异常。
            # 错误 message
            print(error.message)
            # 诊断地址
            print(error.data.get("Recommend"))
            UtilClient.assert_as_string(error.message)


def extract_date_yyyymmdd(input_string):
    pattern = r'\d{8}'
    matches = re.findall(pattern, input_string)
    if matches:
        return matches[0]
    return None


def get_online_version_dt(service_name: str):
    model_detail = PAIClient.get_describe_service(service_name)
    service_config_str = model_detail['ServiceConfig']
    service_config = json.loads(service_config_str)
    model_path = service_config['model_path']
    online_date = extract_date_yyyymmdd(model_path)
    return online_date


def update_online_flow():
    online_version_dt = get_online_version_dt('ad_rank_dnn_v11_easyrec')
    draft = PAIClient.get_work_flow_draft(experiment_id)
    print(json.dumps(draft, ensure_ascii=False))
    content = draft['Content']
    version = draft['Version']
    print(content)
    content_json = json.loads(content)
    nodes = content_json.get('nodes')
    global_params = content_json.get('globalParams')
    bizdate = get_previous_days_date(1)
    for global_param in global_params:
        if global_param['name'] == 'bizdate':
            global_param['value'] = bizdate
        if global_param['name'] == 'online_version_dt':
            global_param['value'] = online_version_dt
        if global_param['name'] == 'eval_date':
            global_param['value'] = bizdate
    for node in nodes:
        name = node['name']
        if name == '模型训练-自定义':
            properties = node['properties']
            for property in properties:
                if property['name'] == 'sql':
                    value = property['value']
                    new_value = update_train_tables(value)
                    if new_value is None:
                        print("error")
                    property['value'] = new_value
    new_content = json.dumps(content_json, ensure_ascii=False)
    PAIClient.update_experiment_content(experiment_id, new_content, version)


def update_online_new_flow():
    online_version_dt = get_online_version_dt('ad_rank_dnn_v11_easyrec')
    draft = PAIClient.get_work_flow_draft(experiment_id)
    print(json.dumps(draft, ensure_ascii=False))
    content = draft['Content']
    version = draft['Version']
    print(content)
    content_json = json.loads(content)
    nodes = content_json.get('nodes')
    global_params = content_json.get('globalParams')
    bizdate = get_previous_days_date(1)
    for global_param in global_params:
        if global_param['name'] == 'bizdate':
            global_param['value'] = bizdate
        if global_param['name'] == 'online_version_dt':
            global_param['value'] = online_version_dt
        if global_param['name'] == 'eval_date':
            global_param['value'] = bizdate
    for node in nodes:
        name = node['name']
        if name == '样本shuffle':
            properties = node['properties']
            for property in properties:
                if property['name'] == 'sql':
                    value = property['value']
                    new_value = new_update_train_tables(value)
                    if new_value is None:
                        print("error")
                    property['value'] = new_value
    new_content = json.dumps(content_json, ensure_ascii=False)
    PAIClient.update_experiment_content(experiment_id, new_content, version)


def wait_job_end(job_id: str):
    while True:
        job_detail = PAIClient.get_job_detail(job_id)
        print(job_detail)
        statue = job_detail['Status']
        # Initialized: 初始化完成 Starting：开始 WorkflowServiceStarting：准备提交 Running：运行中 ReadyToSchedule：准备运行（前序节点未完成导致）
        if (statue == 'Initialized' or statue == 'Starting' or statue == 'WorkflowServiceStarting'
                or statue == 'Running' or statue == 'ReadyToSchedule'):
            # 睡眠300s 等待下次获取
            time.sleep(300)
            continue
        # Failed：运行失败 Terminating：终止中 Terminated：已终止 Unknown：未知 Skipped：跳过（前序节点失败导致） Succeeded：运行成功
        if statue == 'Failed' or statue == 'Terminating' or statue == 'Unknown' or statue == 'Skipped' or statue == 'Succeeded':
            return job_detail


def get_node_dict():
    experiment_id = "draft-7u3e9v1uc5pohjl0t6"
    draft = PAIClient.get_work_flow_draft(experiment_id)
    content = draft['Content']
    content_json = json.loads(content)
    nodes = content_json.get('nodes')
    node_dict = {}
    for node in nodes:
        name = node['name']
        # 检查名称是否在目标名称集合中
        if name in target_names:
            node_dict[name] = node['id']
    return node_dict


def train_model():
    node_dict = get_node_dict()
    train_node_id = node_dict['模型训练-自定义']
    execute_type = 'EXECUTE_ONE'
    train_res = PAIClient.create_job(experiment_id, train_node_id, execute_type)
    train_job_id = train_res['JobId']
    train_job_detail = wait_job_end(train_job_id)
    if train_job_detail['Status'] == 'Succeeded':
        export_node_id = node_dict['模型导出-2']
        export_res = PAIClient.create_job(experiment_id, export_node_id, execute_type)
        export_job_id = export_res['JobId']
        export_job_detail = wait_job_end(export_job_id)
        if export_job_detail['Status'] == 'Succeeded':
            return True
    return False


def update_online_model():
    node_dict = get_node_dict()
    experiment_id = "draft-7u3e9v1uc5pohjl0t6"
    train_node_id = node_dict['更新EAS服务(Beta)-1']
    execute_type = 'EXECUTE_ONE'
    train_res = PAIClient.create_job(experiment_id, train_node_id, execute_type)
    train_job_id = train_res['JobId']
    train_job_detail = wait_job_end(train_job_id)
    if train_job_detail['Status'] == 'Succeeded':
        return True
    return False


def validate_model_data_accuracy():
    node_dict = get_node_dict()
    train_node_id = node_dict['虚拟起始节点']
    execute_type = 'EXECUTE_FROM_HERE'
    validate_res = PAIClient.create_job(experiment_id, train_node_id, execute_type)
    print(validate_res)
    validate_job_id = validate_res['JobId']
    print(validate_job_id)
    validate_job_detail = wait_job_end(validate_job_id)
    print('res')
    print(validate_job_detail)
    if validate_job_detail['Status'] == 'Succeeded':
        pipeline_run_id = validate_job_detail['RunId']
        node_id = validate_job_detail['PaiflowNodeId']
        flow_out_put_detail = PAIClient.get_flow_out_put(pipeline_run_id, node_id, 3)
        print(flow_out_put_detail)
        tabel_dict = {}
        out_puts = flow_out_put_detail['Outputs']
        for out_put in out_puts:
            if out_put["Producer"] == node_dict['二分类评估-1'] and out_put["Name"] == "outputMetricTable":
                value1 = json.loads(out_put["Info"]['value'])
                tabel_dict['二分类评估-1'] = value1['location']['table']
            if out_put["Producer"] == node_dict['二分类评估-2'] and out_put["Name"] == "outputMetricTable":
                value2 = json.loads(out_put["Info"]['value'])
                tabel_dict['二分类评估-2'] = value2['location']['table']
            if out_put["Producer"] == node_dict['预测结果对比'] and out_put["Name"] == "outputTable":
                value3 = json.loads(out_put["Info"]['value'])
                tabel_dict['预测结果对比'] = value3['location']['table']

        num = 10
        df = get_data_from_odps('pai_algo', tabel_dict['预测结果对比'], 10)
        # 对指定列取绝对值再求和
        old_abs_avg = df['old_error'].abs().sum() / num
        new_abs_avg = df['new_error'].abs().sum() / num
        new_auc = get_dict_from_odps('pai_algo', tabel_dict['二分类评估-1'])['AUC']
        old_auc = get_dict_from_odps('pai_algo', tabel_dict['二分类评估-2'])['AUC']
        bizdate = get_previous_days_date(1)
        score_diff = abs(old_abs_avg - new_abs_avg)
        msg = ""
        level = ""
        if new_abs_avg > 0.1:
            msg += f'线上模型评估{bizdate}的数据，绝对误差大于0.1，请检查'
            level = 'error'
        elif score_diff > 0.05:
            msg += f'两个模型评估${bizdate}的数据，两个模型分数差异为: ${score_diff}, 大于0.05, 请检查'
            level = 'error'
        else:
            # update_online_model()
            msg += 'DNN广告模型更新完成'
            level = 'info'
        step_end_time = int(time.time())
        elapsed = step_end_time - start_time

        # 初始化表格头部
        top10_msg = "| CID  | 老模型相对真实CTCVR的变化 | 新模型相对真实CTCVR的变化 |"
        top10_msg += "\n| ---- | --------- | -------- |"

        for index, row in df.iterrows():
            # 获取指定列的元素
            cid = row['cid']
            old_error = row['old_error']
            new_error = row['new_error']
            top10_msg += f"\n| {int(cid)} | {old_error} | {new_error} | "
        print(top10_msg)
        msg += f"\n\t - 老模型AUC: {old_auc}"
        msg += f"\n\t - 新模型AUC: {new_auc}"
        msg += f"\n\t - 老模型Top10差异平均值: {old_abs_avg}"
        msg += f"\n\t - 新模型Top10差异平均值: {new_abs_avg}"
        _monitor(level, msg, start_time, elapsed, top10_msg)


if __name__ == '__main__':
    start_time = int(time.time())
    # 1.更新工作流
    update_online_new_flow()

    # 2.训练模型
    train_model()

    # 3. 验证模型数据 & 更新模型到线上
    # validate_model_data_accuracy()
    # start_time = int(time.time())
    # node_dict = get_node_dict()
    # str = '{"Creator": "204034041838504386", "ExecuteType": "EXECUTE_FROM_HERE", "ExperimentId": "draft-7u3e9v1uc5pohjl0t6", "GmtCreateTime": "2025-04-01T03:17:42.000+00:00", "JobId": "job-8u3ev2uf5ncoexj9p9", "PaiflowNodeId": "node-9wtveoz1tu89tqfoox", "RequestId": "6ED5FFB1-346B-5075-ACC9-029EB77E9F09", "RunId": "flow-lchat027733ttstdc0", "Status": "Succeeded", "WorkspaceId": "96094"}'
    # validate_job_detail = json.loads(str)
    # if validate_job_detail['Status'] == 'Succeeded':
    #     pipeline_run_id = validate_job_detail['RunId']
    #     node_id = validate_job_detail['PaiflowNodeId']
    #     flow_out_put_detail = PAIClient.get_flow_out_put(pipeline_run_id, node_id, 3)
    #     print(flow_out_put_detail)
    #     tabel_dict = {}
    #     out_puts = flow_out_put_detail['Outputs']
    #     for out_put in out_puts:
    #         if out_put["Producer"] == node_dict['二分类评估-1'] and out_put["Name"] == "outputMetricTable":
    #             value1 = json.loads(out_put["Info"]['value'])
    #             tabel_dict['二分类评估-1'] = value1['location']['table']
    #         if out_put["Producer"] == node_dict['二分类评估-2'] and out_put["Name"] == "outputMetricTable":
    #             value2 = json.loads(out_put["Info"]['value'])
    #             tabel_dict['二分类评估-2'] = value2['location']['table']
    #         if out_put["Producer"] == node_dict['预测结果对比'] and out_put["Name"] == "outputTable":
    #             value3 = json.loads(out_put["Info"]['value'])
    #             tabel_dict['预测结果对比'] = value3['location']['table']
    #
    #     num = 10
    #     df = get_data_from_odps('pai_algo', tabel_dict['预测结果对比'], 10)
    #     # 对指定列取绝对值再求和
    #     old_abs_avg = df['old_error'].abs().sum() / num
    #     new_abs_avg = df['new_error'].abs().sum() / num
    #     new_auc = get_dict_from_odps('pai_algo', tabel_dict['二分类评估-1'])['AUC']
    #     old_auc = get_dict_from_odps('pai_algo', tabel_dict['二分类评估-2'])['AUC']
    #     bizdate = get_previous_days_date(1)
    #     score_diff = abs(old_abs_avg - new_abs_avg)
    #     msg = ""
    #     level = ""
    #     if new_abs_avg > 0.1:
    #         msg += f'线上模型评估{bizdate}的数据，绝对误差大于0.1，请检查'
    #         level = 'error'
    #     elif score_diff > 0.05:
    #         msg += f'两个模型评估${bizdate}的数据，两个模型分数差异为: ${score_diff}, 大于0.05, 请检查'
    #         level = 'error'
    #     else:
    #         # TODO 更新模型到线上
    #         msg += 'DNN广告模型更新完成'
    #         level = 'info'
    #     step_end_time = int(time.time())
    #     elapsed = step_end_time - start_time
    #
    #     # 初始化表格头部
    #     top10_msg = "| CID  | 老模型相对真实CTCVR的变化 | 新模型相对真实CTCVR的变化 |"
    #     top10_msg += "\n| ---- | --------- | -------- |"
    #
    #     for index, row in df.iterrows():
    #         # 获取指定列的元素
    #         cid = row['cid']
    #         old_error = row['old_error']
    #         new_error = row['new_error']
    #         top10_msg += f"\n| {int(cid)} | {old_error} | {new_error} | "
    #     print(top10_msg)
    #     msg += f"\n\t - 老模型AUC: {old_auc}"
    #     msg += f"\n\t - 新模型AUC: {new_auc}"
    #     msg += f"\n\t - 老模型Top10差异平均值: {old_abs_avg}"
    #     msg += f"\n\t - 新模型Top10差异平均值: {new_abs_avg}"
        # _monitor('info', msg, start_time, elapsed, top10_msg)