准确率计算调试代码
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auptest.py
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auptest.py
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from fastapi import FastAPI, HTTPException, Body
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from fastapi.middleware.cors import CORSMiddleware
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import requests
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from requests_ntlm import HttpNtlmAuth
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import urllib3
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urllib3.disable_warnings(urllib3.exceptions.InsecureRequestWarning)
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'''
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sql:
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-- 创建名为pims_api_log的表
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CREATE TABLE pims_api_log (
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-- 自增的唯一标识主键
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id INT AUTO_INCREMENT PRIMARY KEY,
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-- 请求时间，记录请求到达服务器的时间戳，使用DATETIME类型方便查看具体时间
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request_time DATETIME NOT NULL,
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-- 请求的IP地址，用于标识请求来源，VARCHAR类型根据实际IP长度设置合适的长度
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request_ip VARCHAR(15) NOT NULL,
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-- 请求的URL，记录具体是向哪个接口路径发起的请求，VARCHAR类型可根据预计最长路径长度来设置长度
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request_url VARCHAR(255) NOT NULL,
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-- 请求方法，如GET、POST等，使用VARCHAR类型存储简短的方法名称
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request_method VARCHAR(10) NOT NULL,
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-- 接收到的请求参数，以JSON格式的文本存储，方便应对复杂结构的参数情况，TEXT类型可存储较长的文本内容
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request_params TEXT,
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-- 响应状态码，记录接口返回给客户端的HTTP状态码，INT类型
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response_status_code INT NOT NULL,
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-- 响应内容，同样以JSON格式的文本存储，便于保存各种格式的数据返回情况，TEXT类型
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response_content TEXT,
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-- 响应时间，记录接口完成处理并返回响应的时间戳，DATETIME类型
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response_time DATETIME NOT NULL
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);
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'''
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import mysql.connector
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from datetime import datetime
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# 配置数据库连接信息，根据实际情况修改
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config = {
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"user": "your_username",
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"password": "your_password",
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"host": "your_host",
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"database": "your_database"
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}
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def insert_api_log(request_time, request_ip, request_url, request_method, request_params, response_status_code, response_content, response_time):
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try:
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# 建立数据库连接
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cnx = mysql.connector.connect(**config)
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cursor = cnx.cursor()
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# 插入数据的SQL语句
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insert_query = """
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INSERT INTO pims_api_log (request_time, request_ip, request_url, request_method, request_params, response_status_code, response_content, response_time)
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VALUES (%s, %s, %s, %s, %s, %s, %s, %s)
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"""
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# 准备要插入的数据，注意数据顺序要和SQL语句中的占位符顺序一致
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data = (request_time, request_ip, request_url, request_method, request_params, response_status_code, response_content, response_time)
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# 执行插入操作
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cursor.execute(insert_query, data)
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# 提交事务，使插入生效
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cnx.commit()
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except mysql.connector.Error as err:
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print(f"Error: {err}")
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finally:
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# 关闭游标和连接
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if cursor:
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cursor.close()
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if cnx:
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cnx.close()
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app = FastAPI(docs_url="/docs")
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# 允许跨域请求
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app.add_middleware(
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CORSMiddleware,
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allow_origins=["*"],
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allow_methods=["*"],
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allow_headers=["*"],
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)
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headers = {'content-type': 'application/json;charset=UTF-8'}
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# 您的 GraphQL API eg： url = 'http://10.88.14.86/AspenTech/AspenUnified/api/v1/model/Chambroad20241205/graphql'
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graphql_host = '10.88.14.86'
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graphql_path = '/AspenTech/AspenUnified/api/v1/model/Chambroad20241205/graphql'
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query = """
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mutation{
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purchases{
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update(inputs:[%s
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]
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}])
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}
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caseExecution {
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submitCaseStack(
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input:{
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name: "Job2"
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cases: [
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{name: "11月度计划"}
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{name: "二催开工"}
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{name: "一焦化停工"}
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{name: "焦化加工油浆"}
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{name: "焦化加工低硫原油"}
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{name: "焦化加工低硫渣油"}
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]
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}
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)
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{id}
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waitForCaseStackJob(name: "Job2")
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{
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started
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submitted
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finished
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executionStatus
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cases{
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items{
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name
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objectiveValue
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}
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}
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}
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}
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}
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"""
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payload_json = {
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"query": query,
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"operationName": ""
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}
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graphql_username = "bw19382"
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graphql_password = "Fudong3!"
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auth = HttpNtlmAuth(f'{graphql_username}', f'{graphql_password}')
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example_query = '''
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'inputs':{
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name:"11月度计划"
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inputs:[
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{
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name:"CWT"
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inputs:[
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{
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field:Cost
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periodName:"1"
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value: 3100
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}
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]
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},
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{
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name:"VRa"
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inputs:[
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{
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field:Cost
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periodName:"1"
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value: 3333
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}
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]
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},
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'''
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@app.post("/graphql")
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async def execute_graphql_query(
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query: str = Body(..., example=example_query) # 使用Body和example参数添加示例
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):
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session = requests.Session()
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response = session.post(url=url, headers=headers, json=payload_json, auth=auth, verify=False)
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if response.status_code != 200:
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raise HTTPException(status_code=response.status_code, detail=response.text)
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return response.json()
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query2 = '''
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query
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{
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cases
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{
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items
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{
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name
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}
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}
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}
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'''
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payload_json2 = {
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"query": query2,
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"operationName": ""
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}
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@app.get("/cases")
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async def get_cases_query_async():
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session = requests.Session()
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response = session.post(url=url, headers=headers, json=payload_json2, auth=auth, verify=False)
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insert_api_log(
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datetime.now(),
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'IP_ADDRESS '12
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'IP_ADDRESS',
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'URL_ADDRESS,
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'http://127.0.0.1:8000/cases',
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'GET',
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'',
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response.status_code,
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response.text,
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datetime.now()
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)
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if response.status_code!= 200:
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raise HTTPException(status_code=response.status_code, detail=response.text)
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return response.json()
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if __name__ == "__main__":
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import uvicorn
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uvicorn.run(app, host="127.0.0.1", port=8000)
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@ -2,7 +2,7 @@
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 136,
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"execution_count": 2,
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"id": "9daadf20-caa6-4b25-901c-6cc3ef563f58",
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"metadata": {},
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"outputs": [
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@ -10,35 +10,67 @@
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"(75, 9)\n",
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"(85, 28)\n",
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"(20, 4)\n",
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"(75, 12)\n",
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" id PREDICT_DATE CREAT_DATE MIN_PRICE MAX_PRICE HIGH_PRICE_x LOW_PRICE_x \\\n",
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"0 1 2024-11-26 2024-11-25 71.071556 76.006900 \n",
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"1 2 2024-11-27 2024-11-25 71.003624 75.580560 \n",
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"2 3 2024-11-28 2024-11-25 72.083850 76.204260 \n",
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"3 4 2024-11-29 2024-11-25 71.329730 75.703950 \n",
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"4 5 2024-12-02 2024-11-25 71.720825 76.264275 \n",
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"(85, 31)\n",
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" ds NHITS Informer LSTM iTransformer TSMixer \\\n",
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"0 2024-11-25 75.714300 75.523370 73.614220 75.27068 75.03936 \n",
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"1 2024-11-26 76.039635 75.558270 73.692310 75.04110 74.60100 \n",
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"2 2024-11-27 77.375790 75.361885 73.826140 74.99121 74.37731 \n",
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"3 2024-11-28 78.872400 76.339920 73.883484 75.79425 74.04826 \n",
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"4 2024-11-29 79.576970 76.333170 73.876396 75.89008 74.07330 \n",
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"\n",
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" RIGHT_ROTE ds 序号 LOW_PRICE_y HIGH_PRICE_y \n",
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"0 2024-11-26 9.0 71.63 73.80 \n",
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"1 2024-11-27 8.0 71.71 72.85 \n",
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"2 2024-11-28 7.0 71.85 72.96 \n",
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"3 2024-11-29 6.0 71.75 73.34 \n",
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"4 2024-12-02 5.0 71.52 72.89 \n",
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" id PREDICT_DATE CREAT_DATE MIN_PRICE MAX_PRICE HIGH_PRICE_x \\\n",
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"70 71 2024-11-25 2024-11-22 74.53063 76.673140 \n",
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"71 72 2024-11-26 2024-11-22 74.44043 76.874565 \n",
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"72 73 2024-11-27 2024-11-22 74.66318 76.734130 \n",
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"73 74 2024-11-28 2024-11-22 74.70841 77.141050 \n",
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"74 75 2024-11-29 2024-11-22 74.70321 77.746170 \n",
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" TSMixerx PatchTST RNN GRU ... y \\\n",
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"0 74.581190 75.70277 74.721280 74.512060 ... 73.010002 \n",
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"1 73.496025 75.97611 74.588060 74.713425 ... 72.809998 \n",
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"2 73.522026 76.48628 74.486400 74.946010 ... 72.830002 \n",
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"3 73.416306 76.38267 75.195710 74.946014 ... 73.279999 \n",
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"4 73.521570 76.20661 75.089966 74.935165 ... 72.940002 \n",
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"\n",
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" LOW_PRICE_x RIGHT_ROTE ds 序号 LOW_PRICE_y HIGH_PRICE_y \n",
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"70 2024-11-25 10.0 72.30 74.83 \n",
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"71 2024-11-26 9.0 71.63 73.80 \n",
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"72 2024-11-27 8.0 71.71 72.85 \n",
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"73 2024-11-28 7.0 71.85 72.96 \n",
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"74 2024-11-29 6.0 71.75 73.34 \n"
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" min_within_quantile max_within_quantile id CREAT_DATE min_price \\\n",
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"0 74.41491 75.29100 1 2024-11-22 74.414910 \n",
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"1 74.11780 74.95678 2 2024-11-22 73.496025 \n",
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"2 73.93820 74.50395 3 2024-11-22 73.522026 \n",
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"3 73.85808 74.46382 4 2024-11-22 73.416306 \n",
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"4 73.96690 74.81860 5 2024-11-22 73.521570 \n",
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"\n",
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" max_price 序号 LOW_PRICE HIGH_PRICE \n",
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"0 75.959854 10.0 72.30 74.83 \n",
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"1 77.182580 9.0 71.63 73.80 \n",
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"2 78.378624 8.0 71.71 72.85 \n",
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"3 79.415400 7.0 71.85 72.96 \n",
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"4 79.576970 6.0 71.75 73.34 \n",
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"\n",
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"[5 rows x 31 columns]\n",
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" ds NHITS Informer LSTM iTransformer TSMixer \\\n",
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"80 2024-12-16 74.53431 73.944080 71.68200 74.022340 74.295820 \n",
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"81 2024-12-17 74.81450 73.830450 71.95232 74.314950 74.167290 \n",
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"82 2024-12-18 75.55861 73.525100 72.00824 74.441380 74.212180 \n",
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"83 2024-12-19 75.36518 74.012215 72.20199 74.397190 74.330130 \n",
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"84 2024-12-20 74.78187 73.929596 72.23908 74.510895 74.208084 \n",
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"\n",
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" TSMixerx PatchTST RNN GRU ... y min_within_quantile \\\n",
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"80 74.41700 74.587390 73.607780 73.747700 ... NaN 74.231680 \n",
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"81 74.36576 74.363060 73.688736 73.833950 ... NaN 73.735420 \n",
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"82 74.29719 74.073555 73.456700 74.146034 ... NaN 74.073555 \n",
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"83 73.79145 74.529945 74.230125 74.144520 ... NaN 74.330130 \n",
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"84 74.59672 74.231255 74.201860 73.996100 ... NaN 74.083810 \n",
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"\n",
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" max_within_quantile id CREAT_DATE min_price max_price 序号 LOW_PRICE \\\n",
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"80 74.621160 81 2024-12-16 72.75007 74.62116 NaN NaN \n",
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"81 74.682365 82 2024-12-16 72.72196 74.81450 NaN NaN \n",
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"82 75.157074 83 2024-12-16 73.12483 75.55861 NaN NaN \n",
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"83 75.339240 84 2024-12-16 73.07359 75.36518 NaN NaN \n",
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"84 74.604610 85 2024-12-16 72.93583 74.78187 NaN NaN \n",
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"\n",
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" HIGH_PRICE \n",
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"80 NaN \n",
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"81 NaN \n",
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"82 NaN \n",
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"83 NaN \n",
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"84 NaN \n",
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"\n",
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"[5 rows x 31 columns]\n"
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]
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}
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],
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@ -48,17 +80,19 @@
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"import pandas as pd\n",
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"\n",
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"# 预测价格数据\n",
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"dbfilename = os.path.join(r'D:\\code\\PriceForecast\\yuanyoudataset','jbsh_yuanyou.db')\n",
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"conn = sqlite3.connect(dbfilename)\n",
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"query = 'SELECT * FROM accuracy'\n",
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"df1 = pd.read_sql_query(query, conn)\n",
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"df1['ds'] = df1['PREDICT_DATE']\n",
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"conn.close()\n",
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"# dbfilename = os.path.join(r'D:\\code\\PriceForecast\\yuanyoudataset','jbsh_yuanyou.db')\n",
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"# conn = sqlite3.connect(dbfilename)\n",
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"# query = 'SELECT * FROM accuracy'\n",
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"# df1 = pd.read_sql_query(query, conn)\n",
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"# df1['ds'] = df1['PREDICT_DATE']\n",
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"# conn.close()\n",
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"# print(df1.shape)\n",
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"\n",
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"# 预测价格数据\n",
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"dfcsvfilename = os.path.join(r'D:\\code\\PriceForecast\\yuanyoudataset','accuracy_ten.csv')\n",
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"df1 = pd.read_csv(dfcsvfilename)\n",
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"print(df1.shape)\n",
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"\n",
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"\n",
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"\n",
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"\n",
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"# 最高最低价\n",
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"xlsfilename = os.path.join(r'D:\\code\\PriceForecast\\yuanyoudataset','数据项下载.xls')\n",
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"df2 = pd.read_excel(xlsfilename)[5:]\n",
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"df = pd.merge(df1,df2,on=['ds'],how='left')\n",
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"\n",
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"df['ds'] = pd.to_datetime(df['ds'])\n",
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"df['PREDICT_DATE'] = pd.to_datetime(df['PREDICT_DATE'])\n",
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"# df['PREDICT_DATE'] = pd.to_datetime(df['PREDICT_DATE'])\n",
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"df = df.reindex()\n",
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"\n",
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"print(df.shape)\n",
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@ -87,28 +121,22 @@
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},
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{
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"cell_type": "code",
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"execution_count": 137,
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"execution_count": 3,
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"id": "e51c3fd0-6bff-45de-b8b6-971e7986c7a7",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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" ds ACCURACY HIGH_PRICE_y LOW_PRICE_y MIN_PRICE MAX_PRICE\n",
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"0 2024-11-26 1.000000 73.80 71.63 71.071556 76.006900\n",
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"1 2024-11-27 1.000000 72.85 71.71 71.003624 75.580560\n",
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"2 2024-11-28 0.789324 72.96 71.85 72.083850 76.204260\n",
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"3 2024-11-29 1.000000 73.34 71.75 71.329730 75.703950\n",
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"4 2024-12-02 0.853412 72.89 71.52 71.720825 76.264275\n",
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".. ... ... ... ... ... ...\n",
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"70 2024-11-25 0.118328 74.83 72.30 74.530630 76.673140\n",
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"71 2024-11-26 0.000000 73.80 71.63 74.440430 76.874565\n",
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"72 2024-11-27 0.000000 72.85 71.71 74.663180 76.734130\n",
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"73 2024-11-28 0.000000 72.96 71.85 74.708410 77.141050\n",
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"74 2024-11-29 0.000000 73.34 71.75 74.703210 77.746170\n",
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"\n",
|
||||
"[75 rows x 6 columns]\n"
|
||||
"ename": "KeyError",
|
||||
"evalue": "\"None of [Index(['HIGH_PRICE_y', 'LOW_PRICE_y', 'MIN_PRICE', 'MAX_PRICE'], dtype='object')] are in the [columns]\"",
|
||||
"output_type": "error",
|
||||
"traceback": [
|
||||
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
|
||||
"\u001b[1;31mKeyError\u001b[0m Traceback (most recent call last)",
|
||||
"Cell \u001b[1;32mIn[3], line 15\u001b[0m\n\u001b[0;32m 12\u001b[0m \u001b[38;5;66;03m# 使用 apply 函数来应用计算准确率的函数\u001b[39;00m\n\u001b[0;32m 14\u001b[0m columns \u001b[38;5;241m=\u001b[39m [\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mHIGH_PRICE_y\u001b[39m\u001b[38;5;124m'\u001b[39m,\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mLOW_PRICE_y\u001b[39m\u001b[38;5;124m'\u001b[39m,\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mMIN_PRICE\u001b[39m\u001b[38;5;124m'\u001b[39m,\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mMAX_PRICE\u001b[39m\u001b[38;5;124m'\u001b[39m]\n\u001b[1;32m---> 15\u001b[0m df[columns] \u001b[38;5;241m=\u001b[39m df[columns]\u001b[38;5;241m.\u001b[39mastype(\u001b[38;5;28mfloat\u001b[39m)\n\u001b[0;32m 16\u001b[0m df[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mACCURACY\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m=\u001b[39m df\u001b[38;5;241m.\u001b[39mapply(calculate_accuracy, axis\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1\u001b[39m)\n\u001b[0;32m 19\u001b[0m \u001b[38;5;66;03m# 打印结果\u001b[39;00m\n",
|
||||
"File \u001b[1;32md:\\ProgramData\\anaconda3\\Lib\\site-packages\\pandas\\core\\frame.py:3899\u001b[0m, in \u001b[0;36mDataFrame.__getitem__\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m 3897\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m is_iterator(key):\n\u001b[0;32m 3898\u001b[0m key \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlist\u001b[39m(key)\n\u001b[1;32m-> 3899\u001b[0m indexer \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcolumns\u001b[38;5;241m.\u001b[39m_get_indexer_strict(key, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcolumns\u001b[39m\u001b[38;5;124m\"\u001b[39m)[\u001b[38;5;241m1\u001b[39m]\n\u001b[0;32m 3901\u001b[0m \u001b[38;5;66;03m# take() does not accept boolean indexers\u001b[39;00m\n\u001b[0;32m 3902\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mgetattr\u001b[39m(indexer, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdtype\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m) \u001b[38;5;241m==\u001b[39m \u001b[38;5;28mbool\u001b[39m:\n",
|
||||
"File \u001b[1;32md:\\ProgramData\\anaconda3\\Lib\\site-packages\\pandas\\core\\indexes\\base.py:6115\u001b[0m, in \u001b[0;36mIndex._get_indexer_strict\u001b[1;34m(self, key, axis_name)\u001b[0m\n\u001b[0;32m 6112\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m 6113\u001b[0m keyarr, indexer, new_indexer \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_reindex_non_unique(keyarr)\n\u001b[1;32m-> 6115\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_raise_if_missing(keyarr, indexer, axis_name)\n\u001b[0;32m 6117\u001b[0m keyarr \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtake(indexer)\n\u001b[0;32m 6118\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(key, Index):\n\u001b[0;32m 6119\u001b[0m \u001b[38;5;66;03m# GH 42790 - Preserve name from an Index\u001b[39;00m\n",
|
||||
"File \u001b[1;32md:\\ProgramData\\anaconda3\\Lib\\site-packages\\pandas\\core\\indexes\\base.py:6176\u001b[0m, in \u001b[0;36mIndex._raise_if_missing\u001b[1;34m(self, key, indexer, axis_name)\u001b[0m\n\u001b[0;32m 6174\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m use_interval_msg:\n\u001b[0;32m 6175\u001b[0m key \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlist\u001b[39m(key)\n\u001b[1;32m-> 6176\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mNone of [\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mkey\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m] are in the [\u001b[39m\u001b[38;5;132;01m{\u001b[39;00maxis_name\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m]\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m 6178\u001b[0m not_found \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlist\u001b[39m(ensure_index(key)[missing_mask\u001b[38;5;241m.\u001b[39mnonzero()[\u001b[38;5;241m0\u001b[39m]]\u001b[38;5;241m.\u001b[39munique())\n\u001b[0;32m 6179\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mnot_found\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m not in index\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
|
||||
"\u001b[1;31mKeyError\u001b[0m: \"None of [Index(['HIGH_PRICE_y', 'LOW_PRICE_y', 'MIN_PRICE', 'MAX_PRICE'], dtype='object')] are in the [columns]\""
|
||||
]
|
||||
}
|
||||
],
|
||||
|
||||
Loading…
Reference in New Issue
Block a user