auptest v0.0.1接口文档
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598
auptest.py
598
auptest.py
@ -6,37 +6,7 @@ from fastapi.middleware.cors import CORSMiddleware
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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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from typing import Dict
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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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@ -46,8 +16,6 @@ from datetime import datetime
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host = 'rm-2zehj3r1n60ttz9x5ko.mysql.rds.aliyuncs.com' # 北京访问使用
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database = 'jingbo-test' # 北京访问使用
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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": "jingbo",
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@ -56,45 +24,50 @@ config = {
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"database": database
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}
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'''
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`ID` varchar(128) NOT NULL COMMENT 'ID',
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`REQUEST_METHOD` varchar(128) DEFAULT NULL COMMENT '方法名称',
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`REQUEST_TIME` datetime DEFAULT NULL COMMENT '请求时间',
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`REQUEST_URL` varchar(256) DEFAULT NULL COMMENT '请求URL',
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`USING_FLAG` varchar(1) DEFAULT NULL COMMENT '启用状态',
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`REQUEST_PARAMS` text COMMENT '接收到的请求参数',
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`RESPONSE_CONTENT` text COMMENT '响应内容',
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`RESPONSE_TIME` datetime DEFAULT NULL COMMENT '响应时间',
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'''
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# GraphQL API eg： url = 'http://10.88.14.86/AspenTech/AspenUnified/api/v1/model/Chambroad20241205/graphql'
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graphql_host = 'http://10.88.14.86'
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graphql_path = '/AspenTech/AspenUnified/api/v1/model/Chambroad20241205/graphql'
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url = graphql_host + graphql_path
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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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# 请求头设置
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headers = {'content-type': 'application/json;charset=UTF-8'}
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def insert_api_log(request_time, request_url, request_method, request_params, response_content, response_time):
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'''
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请求日志表 v_tbl_aup_api_log 写入
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'''
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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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# 先查询表中已有记录的数量，用于生成新记录的ID
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count_query = "SELECT COUNT(*) FROM v_tbl_aup_api_log"
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cursor.execute(count_query)
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result = cursor.fetchone()
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new_id = result[0] + 1 if result else 1 # 如果表为空，ID设为1，否则数量加1
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# count_query = "SELECT max(ID) FROM v_tbl_aup_api_log"
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# cursor.execute(count_query)
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# result = cursor.fetchone()
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# new_id = int(result[0]) + 1 if result else 1 # 如果表为空，ID设为1，否则数量加1
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# 插入数据的SQL语句
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# insert_query = """
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# INSERT INTO v_tbl_aup_api_log (ID,REQUEST_TIME, REQUEST_URL, REQUEST_METHOD, REQUEST_PARAMS, RESPONSE_CONTENT, RESPONSE_TIME)
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# VALUES (%s,%s, %s, %s, %s, %s, %s)
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# """
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insert_query = """
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INSERT INTO v_tbl_aup_api_log (ID,REQUEST_TIME, REQUEST_URL, REQUEST_METHOD, REQUEST_PARAMS, RESPONSE_CONTENT, RESPONSE_TIME)
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VALUES (%s,%s, %s, %s, %s, %s, %s)
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INSERT INTO v_tbl_aup_api_log (REQUEST_TIME, REQUEST_URL, REQUEST_METHOD, REQUEST_PARAMS, RESPONSE_CONTENT, RESPONSE_TIME)
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VALUES (%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 = (new_id,request_time, request_url, request_method, request_params, response_content, response_time)
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# data = (new_id,request_time, request_url, request_method, request_params, response_content, response_time)
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data = (request_time, request_url, request_method, request_params.encode('utf-8'), response_content.encode('utf-8'), 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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except UnboundLocalError 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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@ -105,7 +78,23 @@ def insert_api_log(request_time, request_url, request_method, request_params, re
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app = FastAPI(docs_url="/docs")
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tags_metadata = [
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{
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"name": "get_cases",
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"description": "获取所有cases",
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},
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{
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"name": "generate_graphql_query",
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"description": "生成Graphql查询语句，并接收查询结果",
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},
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]
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app = FastAPI(
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title="AUP数据集成信息化接口",
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version="0.0.1",
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openapi_tags=tags_metadata,
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# openapi_url=""
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)
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# 允许跨域请求
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app.add_middleware(
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@ -115,17 +104,14 @@ app.add_middleware(
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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 = 'http://10.88.14.86'
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graphql_path = '/AspenTech/AspenUnified/api/v1/model/Chambroad20241205/graphql'
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url = graphql_host + graphql_path
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query = """
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mutation{
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purchases{
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update(inputs:[{
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class GraphqlQueryTemplates:
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'''
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从GraphqlQuery 查询语句中找到模板，并保留配置示例
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'''
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def __init__(self):
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# 参数模板
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self.purchase_inputs_template = """
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name:"11月度计划"
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inputs:[
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{
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@ -149,11 +135,8 @@ mutation{
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]
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},
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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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"""
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self.case_execution_input_template = """
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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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@ -164,34 +147,9 @@ mutation{
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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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query2 = '''
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self.wait_for_case_stack_job_template = "waitForCaseStackJob(name: \"Job2\")"
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self.case_qurey = '''
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query
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{
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cases
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@ -203,140 +161,64 @@ query
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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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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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# 参数示例
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self.purchase_inputs = '''
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[{
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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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"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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"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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"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: 3333
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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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]
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}]
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'''
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def log_api_call(func):
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@functools.wraps(func)
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def wrapper(*args, **kwargs):
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request_time = datetime.now()
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request_url = None
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request_method = 'post'
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request_params = None
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try:
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# 执行被装饰的函数，获取其返回的响应
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request_time = datetime.now()
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response = func(*args, **kwargs)
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response_time = datetime.now()
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# 准备请求参数和响应内容，转换为合适的字符串格式用于记录（例如JSON格式字符串）
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request_params_str = json.dumps(request_params) if request_params else None
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response_content = response.text if hasattr(response, 'text') else None
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# 调用插入日志的函数，将相关信息记录到数据库中（假设insert_api_log函数已正确定义且可访问）
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insert_api_log(
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request_time,
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request_url,
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request_method,
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request_params_str,
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response_content,
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response_time
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)
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return response
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except Exception as e:
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print(f"Error occurred during API call: {e}")
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raise
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return wrapper
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@app.post("/graphql")
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async def post_execute_graphql_query(request: Request,
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query:str = Body(query,example_query=query)
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):
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payload_json = {
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"query": query
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self.case_execution_input = '''
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{
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"name": "Job2",
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"cases": [
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{
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"name": "11月度计划"
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},
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{
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"name": "二催开工"
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},
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{
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"name": "一焦化停工"
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},
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{
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"name": "焦化加工油浆"
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},
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{
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"name": "焦化加工低硫原油"
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},
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{
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"name": "焦化加工低硫渣油"
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}
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request_time = datetime.now()
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full_path = str(request.url.path)
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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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response_time = datetime.now()
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# 调用插入日志的函数，将相关信息记录到数据库中（假设insert_api_log函数已正确定义且可访问）
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insert_api_log(
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request_time,
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full_path,
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'POST',
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json.dumps(payload_json),
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json.dumps(response.json()),
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response_time
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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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# def insert_api_log(request_time, request_url, request_method, request_params, response_content, response_time):
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@app.post("/cases")
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async def post_cases_query_async(request: Request):
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payload_json2 = {
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"query": query2
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]
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}
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full_path = str(request.url.path)
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request_time = datetime.now()
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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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response_time = datetime.now()
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'''
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self.wait_for_case_stack_job_name = 'Job2'
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# 调用插入日志的函数，将相关信息记录到数据库中（假设insert_api_log函数已正确定义且可访问）
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insert_api_log(
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request_time,
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full_path,
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'POST',
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json.dumps(payload_json),
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json.dumps(response.json()),
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response_time
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)
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templates = GraphqlQueryTemplates()
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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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# 定义一个函数用于生成定制化的GraphQL查询语句，添加了参数类型检查
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def generate_custom_graphql_query(
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purchase_inputs=None,
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case_execution_input=None,
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@ -423,33 +305,7 @@ mutation{
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raise TypeError("wait_for_case_stack_job_name should be a string or None.")
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if purchase_inputs:
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# 购买相关的inputs部分的模板
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purchase_inputs_template = """
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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:"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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"""
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new_purchase_inputs_str = ""
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new_purchase_inputs_str = "["
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for input_data in purchase_inputs:
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input_str = f"""
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name: "{input_data['name']}"
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@ -457,33 +313,27 @@ mutation{
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"""
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inner_inputs = input_data.get('inputs', [])
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for inner_input in inner_inputs:
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inner_str = f"""
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name: "{inner_input['name']}"
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inputs: [
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"""
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input_str += inner_str
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for input in inner_input['inputs']:
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inner_str = f"""
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{{
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field: "{inner_input['field']}"
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periodName: "{inner_input['periodName']}"
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value: {inner_input['value']}
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field: "{input['field']}"
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periodName: "{input['periodName']}"
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value: {input['value']}
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}}
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"""
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input_str += inner_str
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input_str += " ]"
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input_str += " ]"
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new_purchase_inputs_str += input_str
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base_query = base_query.replace(purchase_inputs_template, new_purchase_inputs_str)
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base_query = base_query.replace(templates.purchase_inputs_template, new_purchase_inputs_str)
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if case_execution_input:
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# caseExecution相关的input部分的模板
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case_execution_input_template = """
|
||||
name: "Job2"
|
||||
cases: [
|
||||
{name: "11月度计划"}
|
||||
{name: "二催开工"}
|
||||
{name: "一焦化停工"}
|
||||
{name: "焦化加工油浆"}
|
||||
{name: "焦化加工低硫原油"}
|
||||
{name: "焦化加工低硫渣油"}
|
||||
|
||||
]
|
||||
"""
|
||||
input_dict_str = f"""
|
||||
name: "{case_execution_input['name']}"
|
||||
cases: [
|
||||
@ -495,24 +345,20 @@ mutation{
|
||||
input_dict_str += case_str
|
||||
input_dict_str += " ]"
|
||||
|
||||
base_query = base_query.replace(case_execution_input_template, input_dict_str)
|
||||
base_query = base_query.replace(templates.case_execution_input_template, input_dict_str)
|
||||
|
||||
if wait_for_case_stack_job_name:
|
||||
# waitForCaseStackJob部分的模板
|
||||
wait_for_case_stack_job_template = "waitForCaseStackJob(name: \"Job2\")"
|
||||
new_wait_for_case_stack_job_str = f"waitForCaseStackJob(name: \"{wait_for_case_stack_job_name}\")"
|
||||
base_query = base_query.replace(wait_for_case_stack_job_template, new_wait_for_case_stack_job_str)
|
||||
base_query = base_query.replace(templates.wait_for_case_stack_job_template, new_wait_for_case_stack_job_str)
|
||||
|
||||
return base_query
|
||||
|
||||
|
||||
# 定义一个POST请求的接口，用于接收参数并生成GraphQL查询语句
|
||||
@app.post("/generate_graphql_query")
|
||||
@app.post("/generate_graphql_query",tags=['generate_graphql_query'])
|
||||
async def generate_graphql_query(
|
||||
request = Request,
|
||||
purchase_inputs: list[dict] = Body(None, embed=True),
|
||||
case_execution_input: dict = Body(None, embed=True),
|
||||
wait_for_case_stack_job_name: str = Body(None, embed=True)
|
||||
request: Request,
|
||||
purchase_inputs: list[dict] = Body(templates.purchase_inputs, embed=True,example_query=json.loads(templates.purchase_inputs)),
|
||||
case_execution_input: dict = Body(templates.case_execution_input, embed=True,example_query=json.loads(templates.case_execution_input)),
|
||||
wait_for_case_stack_job_name: str = Body(templates.wait_for_case_stack_job_name, embed=True,example_query=templates.wait_for_case_stack_job_name),
|
||||
):
|
||||
try:
|
||||
custom_query = generate_custom_graphql_query(purchase_inputs, case_execution_input, wait_for_case_stack_job_name)
|
||||
@ -522,26 +368,226 @@ async def generate_graphql_query(
|
||||
request_time = datetime.now()
|
||||
full_path = str(request.url.path)
|
||||
session = requests.Session()
|
||||
response = session.post(url=url, headers=headers, json=payload_json, auth=auth, verify=False)
|
||||
response_time = datetime.now()
|
||||
try:
|
||||
response = await session.post(url=url, headers=headers, json=payload_json, auth=auth, verify=False, timeout=300)
|
||||
print(type(response))
|
||||
except requests.exceptions.ConnectTimeout as e:
|
||||
# 构造符合错误情况的响应数据字典
|
||||
error_response_data = {
|
||||
"errors": [{"message": "连接超时，请检查网络或稍后重试"}],
|
||||
"data": {},
|
||||
"status_code": 503 # 使用合适的状态码，如503表示服务暂时不可用，可根据具体错误类型调整
|
||||
}
|
||||
response = error_response_data
|
||||
raise HTTPException(status_code=503, detail=response) # 抛出合适状态码的HTTPException
|
||||
except requests.exceptions.RequestException as e:
|
||||
# 捕获其他请求相关的异常，统一处理
|
||||
error_response_data = {
|
||||
"errors": [{"message": "请求出现其他错误，请联系管理员"}],
|
||||
"data": {},
|
||||
"status_code": 500
|
||||
}
|
||||
response = error_response_data
|
||||
raise HTTPException(status_code=500, detail=response)
|
||||
|
||||
finally:
|
||||
response_time = datetime.now()
|
||||
try:
|
||||
res = response.json()
|
||||
except (UnboundLocalError,AttributeError):
|
||||
res = response
|
||||
# 调用插入日志的函数，将相关信息记录到数据库中（假设insert_api_log函数已正确定义且可访问）
|
||||
insert_api_log(
|
||||
request_time,
|
||||
full_path,
|
||||
'POST',
|
||||
request.method,
|
||||
json.dumps(payload_json),
|
||||
json.dumps(response.json()),
|
||||
json.dumps(res),
|
||||
response_time
|
||||
)
|
||||
|
||||
|
||||
if response.status_code!= 200:
|
||||
raise HTTPException(status_code=response.status_code, detail=response.text)
|
||||
return response.json()
|
||||
except TypeError as e:
|
||||
return {"error": str(e)}
|
||||
|
||||
@app.get("/get_cases",tags=['get_cases'])
|
||||
async def post_cases_query_async(request: Request):
|
||||
payload_json2 = {
|
||||
"query": templates.case_qurey
|
||||
}
|
||||
full_path = str(request.url.path)
|
||||
request_time = datetime.now()
|
||||
session = requests.Session()
|
||||
try:
|
||||
response = await session.post(url=url, headers=headers, json=payload_json2, auth=auth, verify=False)
|
||||
except requests.exceptions.ConnectTimeout as e:
|
||||
# 构造符合错误情况的响应数据字典
|
||||
error_response_data = {
|
||||
"errors": [{"message": "连接超时，请检查网络或稍后重试"}],
|
||||
"data": {},
|
||||
"status_code": 503 # 使用合适的状态码，如503表示服务暂时不可用，可根据具体错误类型调整
|
||||
}
|
||||
response = error_response_data
|
||||
raise HTTPException(status_code=503, detail=response) # 抛出合适状态码的HTTPException
|
||||
except requests.exceptions.RequestException as e:
|
||||
# 捕获其他请求相关的异常，统一处理
|
||||
error_response_data = {
|
||||
"errors": [{"message": "请求出现其他错误，请联系管理员"}],
|
||||
"data": {},
|
||||
"status_code": 500
|
||||
}
|
||||
|
||||
response = error_response_data
|
||||
raise HTTPException(status_code=500, detail=response)
|
||||
finally:
|
||||
response_time = datetime.now()
|
||||
try:
|
||||
res = response.json()
|
||||
except (UnboundLocalError,AttributeError):
|
||||
res = response
|
||||
# 调用插入日志的函数，将相关信息记录到数据库中（假设insert_api_log函数已正确定义且可访问）
|
||||
insert_api_log(
|
||||
request_time,
|
||||
full_path,
|
||||
request.method,
|
||||
json.dumps(payload_json2),
|
||||
json.dumps(res),
|
||||
response_time
|
||||
)
|
||||
|
||||
if response.status_code!= 200:
|
||||
raise HTTPException(status_code=response.status_code, detail=response.text)
|
||||
|
||||
# 将JSON字符串解析为Python字典对象
|
||||
data_dict = response.json()
|
||||
# 提取name列表
|
||||
name_list = [item["name"] for item in data_dict["data"]["cases"]["items"]]
|
||||
data_dict['name_lsit'] = name_list
|
||||
return json.dumps(data_dict)
|
||||
|
||||
if __name__ == "__main__":
|
||||
import uvicorn
|
||||
uvicorn.run(app, host="127.0.0.1", port=8001)
|
||||
uvicorn.run(app, host="127.0.0.1", port=8003)
|
||||
|
||||
|
||||
# query = """
|
||||
# mutation{
|
||||
# purchases{
|
||||
# update(inputs:[{
|
||||
# name:"11月度计划"
|
||||
# inputs:[
|
||||
# {
|
||||
# name:"CWT"
|
||||
# inputs:[
|
||||
# {
|
||||
# field:Cost
|
||||
# periodName:"1"
|
||||
# value: 3100
|
||||
# }
|
||||
# ]
|
||||
# },
|
||||
# {
|
||||
# name:"CWT"
|
||||
# inputs:[
|
||||
# {
|
||||
# field:Cost
|
||||
# periodName:"1"
|
||||
# value: 3100
|
||||
# }
|
||||
# ]
|
||||
# },
|
||||
# ]
|
||||
# }])
|
||||
# }
|
||||
# caseExecution {
|
||||
# submitCaseStack(
|
||||
# input:{
|
||||
# name: "Job2"
|
||||
# cases: [
|
||||
# {name: "11月度计划"}
|
||||
# {name: "二催开工"}
|
||||
# {name: "一焦化停工"}
|
||||
# {name: "焦化加工油浆"}
|
||||
# {name: "焦化加工低硫原油"}
|
||||
# {name: "焦化加工低硫渣油"}
|
||||
|
||||
# ]
|
||||
# }
|
||||
# )
|
||||
# {id}
|
||||
# waitForCaseStackJob(name: "Job2")
|
||||
# {
|
||||
# started
|
||||
# submitted
|
||||
# finished
|
||||
# executionStatus
|
||||
# cases{
|
||||
# items{
|
||||
# name
|
||||
# objectiveValue
|
||||
# }
|
||||
# }
|
||||
# }
|
||||
# }
|
||||
# }
|
||||
# """
|
||||
|
||||
# payload_json = {
|
||||
# "query": query,
|
||||
# "operationName": ""
|
||||
# }
|
||||
|
||||
|
||||
|
||||
# query2 = '''
|
||||
# query
|
||||
# {
|
||||
# cases
|
||||
# {
|
||||
# items
|
||||
# {
|
||||
# name
|
||||
# }
|
||||
# }
|
||||
# }
|
||||
# '''
|
||||
|
||||
# payload_json2 = {
|
||||
# "query": query2,
|
||||
# "operationName": ""
|
||||
# }
|
||||
|
||||
|
||||
|
||||
|
||||
# @app.post("/graphql")
|
||||
# async def post_execute_graphql_query(request: Request,
|
||||
# query:str = Body(query,example_query=query)
|
||||
# ):
|
||||
# payload_json = {
|
||||
# "query": query
|
||||
# }
|
||||
# request_time = datetime.now()
|
||||
# full_path = str(request.url.path)
|
||||
# session = requests.Session()
|
||||
# response = session.post(url=url, headers=headers, json=payload_json, auth=auth, verify=False)
|
||||
# response_time = datetime.now()
|
||||
|
||||
# # 调用插入日志的函数，将相关信息记录到数据库中（假设insert_api_log函数已正确定义且可访问）
|
||||
# insert_api_log(
|
||||
# request_time,
|
||||
# full_path,
|
||||
# 'POST',
|
||||
# json.dumps(payload_json),
|
||||
# json.dumps(response.json()),
|
||||
# response_time
|
||||
# )
|
||||
|
||||
|
||||
# if response.status_code!= 200:
|
||||
# raise HTTPException(status_code=response.status_code, detail=response.text)
|
||||
# return response.json()
|
||||
|
||||
# def insert_api_log(request_time, request_url, request_method, request_params, response_content, response_time):
|
||||
|
||||
@ -2,7 +2,7 @@
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 9,
|
||||
"execution_count": 13,
|
||||
"id": "9daadf20-caa6-4b25-901c-6cc3ef563f58",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
@ -126,7 +126,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 10,
|
||||
"execution_count": 14,
|
||||
"id": "0d77ab7d",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
@ -138,7 +138,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 11,
|
||||
"execution_count": 15,
|
||||
"id": "e51c3fd0-6bff-45de-b8b6-971e7986c7a7",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
@ -151,103 +151,109 @@
|
||||
" 开始日期 结束日期 准确率\n",
|
||||
"0 2024-09-27 2024-10-04 0\n",
|
||||
" 开始日期 结束日期 准确率\n",
|
||||
"0 2024-09-27 2024-10-04 0\n",
|
||||
"0 2024-09-27 2024-10-04 0\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
" 开始日期 结束日期 准确率\n",
|
||||
"0 2024-09-27 2024-10-04 0\n",
|
||||
" 开始日期 结束日期 准确率\n",
|
||||
"0 2024-09-27 2024-10-04 0\n",
|
||||
" 开始日期 结束日期 准确率\n",
|
||||
"0 2024-10-04 2024-10-11 0.495046\n",
|
||||
"0 2024-10-04 2024-10-11 0.433988\n",
|
||||
" 开始日期 结束日期 准确率\n",
|
||||
"0 2024-10-04 2024-10-11 0.495046\n",
|
||||
"0 2024-10-04 2024-10-11 0.433988\n",
|
||||
" 开始日期 结束日期 准确率\n",
|
||||
"0 2024-10-04 2024-10-11 0.495046\n",
|
||||
"0 2024-10-04 2024-10-11 0.433988\n",
|
||||
" 开始日期 结束日期 准确率\n",
|
||||
"0 2024-10-04 2024-10-11 0.495046\n",
|
||||
"0 2024-10-04 2024-10-11 0.433988\n",
|
||||
" 开始日期 结束日期 准确率\n",
|
||||
"0 2024-10-04 2024-10-11 0.495046\n",
|
||||
"0 2024-10-04 2024-10-11 0.433988\n",
|
||||
" 开始日期 结束日期 准确率\n",
|
||||
"0 2024-10-11 2024-10-18 0.449368\n",
|
||||
"0 2024-10-11 2024-10-18 0.367557\n",
|
||||
" 开始日期 结束日期 准确率\n",
|
||||
"0 2024-10-11 2024-10-18 0.449368\n",
|
||||
"0 2024-10-11 2024-10-18 0.367557\n",
|
||||
" 开始日期 结束日期 准确率\n",
|
||||
"0 2024-10-11 2024-10-18 0.449368\n",
|
||||
"0 2024-10-11 2024-10-18 0.367557\n",
|
||||
" 开始日期 结束日期 准确率\n",
|
||||
"0 2024-10-11 2024-10-18 0.449368\n",
|
||||
"0 2024-10-11 2024-10-18 0.367557\n",
|
||||
" 开始日期 结束日期 准确率\n",
|
||||
"0 2024-10-11 2024-10-18 0.449368\n",
|
||||
"0 2024-10-11 2024-10-18 0.367557\n",
|
||||
" 开始日期 结束日期 准确率\n",
|
||||
"0 2024-10-18 2024-10-25 0.814057\n",
|
||||
"0 2024-10-18 2024-10-25 0.342808\n",
|
||||
" 开始日期 结束日期 准确率\n",
|
||||
"0 2024-10-18 2024-10-25 0.814057\n",
|
||||
"0 2024-10-18 2024-10-25 0.342808\n",
|
||||
" 开始日期 结束日期 准确率\n",
|
||||
"0 2024-10-18 2024-10-25 0.814057\n",
|
||||
"0 2024-10-18 2024-10-25 0.342808\n",
|
||||
" 开始日期 结束日期 准确率\n",
|
||||
"0 2024-10-18 2024-10-25 0.814057\n",
|
||||
"0 2024-10-18 2024-10-25 0.342808\n",
|
||||
" 开始日期 结束日期 准确率\n",
|
||||
"0 2024-10-18 2024-10-25 0.814057\n",
|
||||
"0 2024-10-18 2024-10-25 0.342808\n",
|
||||
" 开始日期 结束日期 准确率\n",
|
||||
"0 2024-10-25 2024-11-01 0.433599\n",
|
||||
"0 2024-10-25 2024-11-01 0.397058\n",
|
||||
" 开始日期 结束日期 准确率\n",
|
||||
"0 2024-10-25 2024-11-01 0.433599\n",
|
||||
"0 2024-10-25 2024-11-01 0.397058\n",
|
||||
" 开始日期 结束日期 准确率\n",
|
||||
"0 2024-10-25 2024-11-01 0.433599\n",
|
||||
"0 2024-10-25 2024-11-01 0.397058\n",
|
||||
" 开始日期 结束日期 准确率\n",
|
||||
"0 2024-10-25 2024-11-01 0.433599\n",
|
||||
"0 2024-10-25 2024-11-01 0.397058\n",
|
||||
" 开始日期 结束日期 准确率\n",
|
||||
"0 2024-10-25 2024-11-01 0.433599\n",
|
||||
"0 2024-10-25 2024-11-01 0.397058\n",
|
||||
" 开始日期 结束日期 准确率\n",
|
||||
"0 2024-11-01 2024-11-08 0.894767\n",
|
||||
"0 2024-11-01 2024-11-08 0.666605\n",
|
||||
" 开始日期 结束日期 准确率\n",
|
||||
"0 2024-11-01 2024-11-08 0.894767\n",
|
||||
"0 2024-11-01 2024-11-08 0.666605\n",
|
||||
" 开始日期 结束日期 准确率\n",
|
||||
"0 2024-11-01 2024-11-08 0.894767\n",
|
||||
"0 2024-11-01 2024-11-08 0.666605\n",
|
||||
" 开始日期 结束日期 准确率\n",
|
||||
"0 2024-11-01 2024-11-08 0.894767\n",
|
||||
"0 2024-11-01 2024-11-08 0.666605\n",
|
||||
" 开始日期 结束日期 准确率\n",
|
||||
"0 2024-11-01 2024-11-08 0.894767\n",
|
||||
"0 2024-11-01 2024-11-08 0.666605\n",
|
||||
" 开始日期 结束日期 准确率\n",
|
||||
"0 2024-11-08 2024-11-15 0.915721\n",
|
||||
"0 2024-11-08 2024-11-15 0.805488\n",
|
||||
" 开始日期 结束日期 准确率\n",
|
||||
"0 2024-11-08 2024-11-15 0.915721\n",
|
||||
"0 2024-11-08 2024-11-15 0.805488\n",
|
||||
" 开始日期 结束日期 准确率\n",
|
||||
"0 2024-11-08 2024-11-15 0.915721\n",
|
||||
"0 2024-11-08 2024-11-15 0.805488\n",
|
||||
" 开始日期 结束日期 准确率\n",
|
||||
"0 2024-11-08 2024-11-15 0.915721\n",
|
||||
"0 2024-11-08 2024-11-15 0.805488\n",
|
||||
" 开始日期 结束日期 准确率\n",
|
||||
"0 2024-11-08 2024-11-15 0.915721\n",
|
||||
"0 2024-11-08 2024-11-15 0.805488\n",
|
||||
" 开始日期 结束日期 准确率\n",
|
||||
"0 2024-11-15 2024-11-22 0.835755\n",
|
||||
"0 2024-11-15 2024-11-22 0.744558\n",
|
||||
" 开始日期 结束日期 准确率\n",
|
||||
"0 2024-11-15 2024-11-22 0.835755\n",
|
||||
"0 2024-11-15 2024-11-22 0.744558\n",
|
||||
" 开始日期 结束日期 准确率\n",
|
||||
"0 2024-11-15 2024-11-22 0.835755\n",
|
||||
"0 2024-11-15 2024-11-22 0.744558\n",
|
||||
" 开始日期 结束日期 准确率\n",
|
||||
"0 2024-11-15 2024-11-22 0.835755\n",
|
||||
"0 2024-11-15 2024-11-22 0.744558\n",
|
||||
" 开始日期 结束日期 准确率\n",
|
||||
"0 2024-11-15 2024-11-22 0.835755\n",
|
||||
"0 2024-11-15 2024-11-22 0.744558\n",
|
||||
" 开始日期 结束日期 准确率\n",
|
||||
"0 2024-11-22 2024-11-29 0.718009\n",
|
||||
"0 2024-11-22 2024-11-29 0.351228\n",
|
||||
" 开始日期 结束日期 准确率\n",
|
||||
"0 2024-11-22 2024-11-29 0.718009\n",
|
||||
"0 2024-11-22 2024-11-29 0.351228\n",
|
||||
" 开始日期 结束日期 准确率\n",
|
||||
"0 2024-11-22 2024-11-29 0.718009\n",
|
||||
"0 2024-11-22 2024-11-29 0.351228\n",
|
||||
" 开始日期 结束日期 准确率\n",
|
||||
"0 2024-11-22 2024-11-29 0.718009\n",
|
||||
"0 2024-11-22 2024-11-29 0.351228\n",
|
||||
" 开始日期 结束日期 准确率\n",
|
||||
"0 2024-11-22 2024-11-29 0.718009\n",
|
||||
"0 2024-11-22 2024-11-29 0.351228\n",
|
||||
" 开始日期 结束日期 准确率\n",
|
||||
"0 2024-11-29 2024-12-06 0.948363\n",
|
||||
"0 2024-11-29 2024-12-06 0.727334\n",
|
||||
" 开始日期 结束日期 准确率\n",
|
||||
"0 2024-11-29 2024-12-06 0.948363\n",
|
||||
"0 2024-11-29 2024-12-06 0.727334\n",
|
||||
" 开始日期 结束日期 准确率\n",
|
||||
"0 2024-11-29 2024-12-06 0.948363\n",
|
||||
"0 2024-11-29 2024-12-06 0.727334\n",
|
||||
" 开始日期 结束日期 准确率\n",
|
||||
"0 2024-11-29 2024-12-06 0.948363\n",
|
||||
"0 2024-11-29 2024-12-06 0.727334\n",
|
||||
" 开始日期 结束日期 准确率\n",
|
||||
"0 2024-11-29 2024-12-06 0.948363\n",
|
||||
"0 2024-11-29 2024-12-06 0.727334\n",
|
||||
" 开始日期 结束日期 准确率\n",
|
||||
"0 2024-12-06 2024-12-13 0.947006\n"
|
||||
"0 2024-12-06 2024-12-13 0.835391\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
@ -315,7 +321,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 12,
|
||||
"execution_count": 16,
|
||||
"id": "0f942c69",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
@ -334,7 +340,7 @@
|
||||
"\u001b[1;31mKeyError\u001b[0m: 'PREDICT_DATE'",
|
||||
"\nThe above exception was the direct cause of the following exception:\n",
|
||||
"\u001b[1;31mKeyError\u001b[0m Traceback (most recent call last)",
|
||||
"Cell \u001b[1;32mIn[12], line 4\u001b[0m\n\u001b[0;32m 2\u001b[0m \u001b[38;5;66;03m# ds 按周取\u001b[39;00m\n\u001b[0;32m 3\u001b[0m df[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mDs_Week\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m=\u001b[39m df[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mds\u001b[39m\u001b[38;5;124m'\u001b[39m]\u001b[38;5;241m.\u001b[39mapply(\u001b[38;5;28;01mlambda\u001b[39;00m x: x\u001b[38;5;241m.\u001b[39mstrftime(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m%\u001b[39m\u001b[38;5;124mU\u001b[39m\u001b[38;5;124m'\u001b[39m))\n\u001b[1;32m----> 4\u001b[0m df[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mPre_Week\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m=\u001b[39m df[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mPREDICT_DATE\u001b[39m\u001b[38;5;124m'\u001b[39m]\u001b[38;5;241m.\u001b[39mapply(\u001b[38;5;28;01mlambda\u001b[39;00m x: x\u001b[38;5;241m.\u001b[39mstrftime(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m%\u001b[39m\u001b[38;5;124mU\u001b[39m\u001b[38;5;124m'\u001b[39m))\n",
|
||||
"Cell \u001b[1;32mIn[16], line 4\u001b[0m\n\u001b[0;32m 2\u001b[0m \u001b[38;5;66;03m# ds 按周取\u001b[39;00m\n\u001b[0;32m 3\u001b[0m df[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mDs_Week\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m=\u001b[39m df[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mds\u001b[39m\u001b[38;5;124m'\u001b[39m]\u001b[38;5;241m.\u001b[39mapply(\u001b[38;5;28;01mlambda\u001b[39;00m x: x\u001b[38;5;241m.\u001b[39mstrftime(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m%\u001b[39m\u001b[38;5;124mU\u001b[39m\u001b[38;5;124m'\u001b[39m))\n\u001b[1;32m----> 4\u001b[0m df[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mPre_Week\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m=\u001b[39m df[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mPREDICT_DATE\u001b[39m\u001b[38;5;124m'\u001b[39m]\u001b[38;5;241m.\u001b[39mapply(\u001b[38;5;28;01mlambda\u001b[39;00m x: x\u001b[38;5;241m.\u001b[39mstrftime(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m%\u001b[39m\u001b[38;5;124mU\u001b[39m\u001b[38;5;124m'\u001b[39m))\n",
|
||||
"File \u001b[1;32md:\\ProgramData\\anaconda3\\Lib\\site-packages\\pandas\\core\\frame.py:3893\u001b[0m, in \u001b[0;36mDataFrame.__getitem__\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m 3891\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcolumns\u001b[38;5;241m.\u001b[39mnlevels \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m1\u001b[39m:\n\u001b[0;32m 3892\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_getitem_multilevel(key)\n\u001b[1;32m-> 3893\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[39mget_loc(key)\n\u001b[0;32m 3894\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m is_integer(indexer):\n\u001b[0;32m 3895\u001b[0m indexer \u001b[38;5;241m=\u001b[39m [indexer]\n",
|
||||
"File \u001b[1;32md:\\ProgramData\\anaconda3\\Lib\\site-packages\\pandas\\core\\indexes\\base.py:3798\u001b[0m, in \u001b[0;36mIndex.get_loc\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m 3793\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(casted_key, \u001b[38;5;28mslice\u001b[39m) \u001b[38;5;129;01mor\u001b[39;00m (\n\u001b[0;32m 3794\u001b[0m \u001b[38;5;28misinstance\u001b[39m(casted_key, abc\u001b[38;5;241m.\u001b[39mIterable)\n\u001b[0;32m 3795\u001b[0m \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28many\u001b[39m(\u001b[38;5;28misinstance\u001b[39m(x, \u001b[38;5;28mslice\u001b[39m) \u001b[38;5;28;01mfor\u001b[39;00m x \u001b[38;5;129;01min\u001b[39;00m casted_key)\n\u001b[0;32m 3796\u001b[0m ):\n\u001b[0;32m 3797\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m InvalidIndexError(key)\n\u001b[1;32m-> 3798\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m(key) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01merr\u001b[39;00m\n\u001b[0;32m 3799\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m:\n\u001b[0;32m 3800\u001b[0m \u001b[38;5;66;03m# If we have a listlike key, _check_indexing_error will raise\u001b[39;00m\n\u001b[0;32m 3801\u001b[0m \u001b[38;5;66;03m# InvalidIndexError. Otherwise we fall through and re-raise\u001b[39;00m\n\u001b[0;32m 3802\u001b[0m \u001b[38;5;66;03m# the TypeError.\u001b[39;00m\n\u001b[0;32m 3803\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_check_indexing_error(key)\n",
|
||||
"\u001b[1;31mKeyError\u001b[0m: 'PREDICT_DATE'"
|
||||
|
||||
Loading…
Reference in New Issue
Block a user