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auptest v0.0.1接口文档

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workpc 2024-12-20 17:44:41 +08:00
parent 53b30ab7a7
commit debc562906
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auptest.py
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@ -6,37 +6,7 @@ from fastapi.middleware.cors import CORSMiddleware
from requests_ntlm import HttpNtlmAuth from requests_ntlm import HttpNtlmAuth
import urllib3 import urllib3
urllib3.disable_warnings(urllib3.exceptions.InsecureRequestWarning) urllib3.disable_warnings(urllib3.exceptions.InsecureRequestWarning)
from typing import Dict from typing import Dict
'''
sql:
-- 创建名为pims_api_log的表
CREATE TABLE pims_api_log (
-- 自增的唯一标识主键
id INT AUTO_INCREMENT PRIMARY KEY,
-- 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
request_time DATETIME NOT NULL,
-- 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
request_ip VARCHAR(15) NOT NULL,
-- 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
request_url VARCHAR(255) NOT NULL,
-- 请求方法，如GET、POST等，使用VARCHAR类型存储简短的方法名称
request_method VARCHAR(10) NOT NULL,
-- 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
request_params TEXT,
-- 响应状态码，记录接口返回给客户端的HTTP状态码，INT类型
response_status_code INT NOT NULL,
-- 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
response_content TEXT,
-- 响应时间，记录接口完成处理并返回响应的时间戳，DATETIME类型
response_time DATETIME NOT NULL
);
'''
import mysql.connector import mysql.connector
from datetime import datetime from datetime import datetime
@ -46,8 +16,6 @@ from datetime import datetime
host = 'rm-2zehj3r1n60ttz9x5ko.mysql.rds.aliyuncs.com' # 北京访问使用 host = 'rm-2zehj3r1n60ttz9x5ko.mysql.rds.aliyuncs.com' # 北京访问使用
database = 'jingbo-test' # 北京访问使用 database = 'jingbo-test' # 北京访问使用
# 配置数据库连接信息，根据实际情况修改 # 配置数据库连接信息，根据实际情况修改
config = { config = {
"user": "jingbo", "user": "jingbo",
@ -56,45 +24,50 @@ config = {
"database": database "database": database
} }
''' # GraphQL API eg： url = 'http://10.88.14.86/AspenTech/AspenUnified/api/v1/model/Chambroad20241205/graphql'
`ID` varchar(128) NOT NULL COMMENT 'ID', graphql_host = 'http://10.88.14.86'
`REQUEST_METHOD` varchar(128) DEFAULT NULL COMMENT '方法名称', graphql_path = '/AspenTech/AspenUnified/api/v1/model/Chambroad20241205/graphql'
`REQUEST_TIME` datetime DEFAULT NULL COMMENT '请求时间', url = graphql_host + graphql_path
`REQUEST_URL` varchar(256) DEFAULT NULL COMMENT '请求URL', graphql_username = "bw19382"
`USING_FLAG` varchar(1) DEFAULT NULL COMMENT '启用状态', graphql_password = "Fudong3!"
`REQUEST_PARAMS` text COMMENT '接收到的请求参数', auth = HttpNtlmAuth(f'{graphql_username}', f'{graphql_password}')
`RESPONSE_CONTENT` text COMMENT '响应内容',
`RESPONSE_TIME` datetime DEFAULT NULL COMMENT '响应时间',
'''
# 请求头设置
headers = {'content-type': 'application/json;charset=UTF-8'}
def insert_api_log(request_time, request_url, request_method, request_params, response_content, response_time): def insert_api_log(request_time, request_url, request_method, request_params, response_content, response_time):
'''
请求日志表 v_tbl_aup_api_log 写入
'''
try: try:
# 建立数据库连接 # 建立数据库连接
cnx = mysql.connector.connect(**config) cnx = mysql.connector.connect(**config)
cursor = cnx.cursor() cursor = cnx.cursor()
# 先查询表中已有记录的数量，用于生成新记录的ID # 先查询表中已有记录的数量，用于生成新记录的ID
count_query = "SELECT COUNT(*) FROM v_tbl_aup_api_log" # count_query = "SELECT max(ID) FROM v_tbl_aup_api_log"
cursor.execute(count_query) # cursor.execute(count_query)
result = cursor.fetchone() # result = cursor.fetchone()
new_id = result[0] + 1 if result else 1 # 如果表为空，ID设为1，否则数量加1 # 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
# 插入数据的SQL语句 # 插入数据的SQL语句
# insert_query = """
# INSERT INTO v_tbl_aup_api_log (ID,REQUEST_TIME, REQUEST_URL, REQUEST_METHOD, REQUEST_PARAMS, RESPONSE_CONTENT, RESPONSE_TIME)
# VALUES (%s,%s, %s, %s, %s, %s, %s)
# """
insert_query = """ insert_query = """
INSERT INTO v_tbl_aup_api_log (ID,REQUEST_TIME, REQUEST_URL, REQUEST_METHOD, REQUEST_PARAMS, RESPONSE_CONTENT, RESPONSE_TIME) INSERT INTO v_tbl_aup_api_log (REQUEST_TIME, REQUEST_URL, REQUEST_METHOD, REQUEST_PARAMS, RESPONSE_CONTENT, RESPONSE_TIME)
VALUES (%s,%s, %s, %s, %s, %s, %s) VALUES (%s, %s, %s, %s, %s, %s)
""" """
# 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 # 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
data = (new_id,request_time, request_url, request_method, request_params, response_content, response_time) # data = (new_id,request_time, request_url, request_method, request_params, response_content, response_time)
data = (request_time, request_url, request_method, request_params.encode('utf-8'), response_content.encode('utf-8'), response_time)
# 执行插入操作 # 执行插入操作
cursor.execute(insert_query, data) cursor.execute(insert_query, data)
# 提交事务，使插入生效 # 提交事务，使插入生效
cnx.commit() cnx.commit()
except mysql.connector.Error as err: except mysql.connector.Error as err:
print(f"Error: {err}") print(f"Error: {err}")
except UnboundLocalError as err:
print(f"Error: {err}")
finally: finally:
# 关闭游标和连接 # 关闭游标和连接
if cursor: if cursor:
@ -105,7 +78,23 @@ def insert_api_log(request_time, request_url, request_method, request_params, re
app = FastAPI(docs_url="/docs") tags_metadata = [
{
"name": "get_cases",
"description": "获取所有cases",
},
{
"name": "generate_graphql_query",
"description": "生成Graphql查询语句，并接收查询结果",
},
]
app = FastAPI(
title="AUP数据集成信息化接口",
version="0.0.1",
openapi_tags=tags_metadata,
# openapi_url=""
)
# 允许跨域请求 # 允许跨域请求
app.add_middleware( app.add_middleware(
@ -115,17 +104,14 @@ app.add_middleware(
allow_headers=["*"], allow_headers=["*"],
) )
headers = {'content-type': 'application/json;charset=UTF-8'}
# 您的 GraphQL API eg： url = 'http://10.88.14.86/AspenTech/AspenUnified/api/v1/model/Chambroad20241205/graphql' class GraphqlQueryTemplates:
graphql_host = 'http://10.88.14.86' '''
graphql_path = '/AspenTech/AspenUnified/api/v1/model/Chambroad20241205/graphql' 从GraphqlQuery 查询语句中找到模板，并保留配置示例
url = graphql_host + graphql_path '''
def __init__(self):
query = """ # 参数模板
mutation{ self.purchase_inputs_template = """
purchases{
update(inputs:[{
name:"11月度计划" name:"11月度计划"
inputs:[ inputs:[
{ {
@ -149,194 +135,90 @@ mutation{
] ]
}, },
] ]
}]) """
} self.case_execution_input_template = """
caseExecution {
submitCaseStack(
input:{
name: "Job2" name: "Job2"
cases: [ cases: [
{name: "11月度计划"} {name: "11月度计划"}
{name: "二催开工"} {name: "二催开工"}
{name: "一焦化停工"} {name: "一焦化停工"}
{name: "焦化加工油浆"} {name: "焦化加工油浆"}
{name: "焦化加工低硫原油"} {name: "焦化加工低硫原油"}
{name: "焦化加工低硫渣油"} {name: "焦化加工低硫渣油"}
] ]
} """
) self.wait_for_case_stack_job_template = "waitForCaseStackJob(name: \"Job2\")"
{id} self.case_qurey = '''
waitForCaseStackJob(name: "Job2") query
{ {
started cases
submitted {
finished items
executionStatus {
cases{ name
items{ }
name }
objectiveValue }
'''
# 参数示例
self.purchase_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
}
]
}
]
}]
'''
self.case_execution_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"
}
]
} }
} '''
} self.wait_for_case_stack_job_name = 'Job2'
}
}
"""
payload_json = { templates = GraphqlQueryTemplates()
"query": query,
"operationName": ""
}
query2 = '''
query
{
cases
{
items
{
name
}
}
}
'''
payload_json2 = {
"query": query2,
"operationName": ""
}
graphql_username = "bw19382"
graphql_password = "Fudong3!"
auth = HttpNtlmAuth(f'{graphql_username}', f'{graphql_password}')
example_query = '''
'inputs':{
name:"11月度计划"
inputs:[
{
name:"CWT"
inputs:[
{
field:Cost
periodName:"1"
value: 3100
}
]
},
{
name:"VRa"
inputs:[
{
field:Cost
periodName:"1"
value: 3333
}
]
},
'''
def log_api_call(func):
@functools.wraps(func)
def wrapper(*args, **kwargs):
request_time = datetime.now()
request_url = None
request_method = 'post'
request_params = None
try:
# 执行被装饰的函数，获取其返回的响应
request_time = datetime.now()
response = func(*args, **kwargs)
response_time = datetime.now()
# 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）
request_params_str = json.dumps(request_params) if request_params else None
response_content = response.text if hasattr(response, 'text') else None
# 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,
request_url,
request_method,
request_params_str,
response_content,
response_time
)
return response
except Exception as e:
print(f"Error occurred during API call: {e}")
raise
return wrapper
@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):
@app.post("/cases")
async def post_cases_query_async(request: Request):
payload_json2 = {
"query": query2
}
full_path = str(request.url.path)
request_time = datetime.now()
session = requests.Session()
response = session.post(url=url, headers=headers, json=payload_json2, 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()
# 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
def generate_custom_graphql_query( def generate_custom_graphql_query(
purchase_inputs=None, purchase_inputs=None,
case_execution_input=None, case_execution_input=None,
@ -423,33 +305,7 @@ mutation{
raise TypeError("wait_for_case_stack_job_name should be a string or None.") raise TypeError("wait_for_case_stack_job_name should be a string or None.")
if purchase_inputs: if purchase_inputs:
# 购买相关的inputs部分的模板 new_purchase_inputs_str = "["
purchase_inputs_template = """
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
}
]
},
]
"""
new_purchase_inputs_str = ""
for input_data in purchase_inputs: for input_data in purchase_inputs:
input_str = f""" input_str = f"""
name: "{input_data['name']}" name: "{input_data['name']}"
@ -458,32 +314,26 @@ mutation{
inner_inputs = input_data.get('inputs', []) inner_inputs = input_data.get('inputs', [])
for inner_input in inner_inputs: for inner_input in inner_inputs:
inner_str = f""" inner_str = f"""
{{ name: "{inner_input['name']}"
field: "{inner_input['field']}" inputs: [
periodName: "{inner_input['periodName']}" """
value: {inner_input['value']}
}}
"""
input_str += inner_str input_str += inner_str
for input in inner_input['inputs']:
inner_str = f"""
{{
field: "{input['field']}"
periodName: "{input['periodName']}"
value: {input['value']}
}}
"""
input_str += inner_str
input_str += " ]"
input_str += " ]" input_str += " ]"
new_purchase_inputs_str += input_str new_purchase_inputs_str += input_str
base_query = base_query.replace(purchase_inputs_template, new_purchase_inputs_str) base_query = base_query.replace(templates.purchase_inputs_template, new_purchase_inputs_str)
if case_execution_input: if case_execution_input:
# caseExecution相关的input部分的模板
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""" input_dict_str = f"""
name: "{case_execution_input['name']}" name: "{case_execution_input['name']}"
cases: [ cases: [
@ -495,24 +345,20 @@ mutation{
input_dict_str += case_str input_dict_str += case_str
input_dict_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: 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}\")" 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 return base_query
@app.post("/generate_graphql_query",tags=['generate_graphql_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")
async def generate_graphql_query( async def generate_graphql_query(
request = Request, request: Request,
purchase_inputs: list[dict] = Body(None, embed=True), purchase_inputs: list[dict] = Body(templates.purchase_inputs, embed=True,example_query=json.loads(templates.purchase_inputs)),
case_execution_input: dict = Body(None, embed=True), 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(None, embed=True) 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: try:
custom_query = generate_custom_graphql_query(purchase_inputs, case_execution_input, wait_for_case_stack_job_name) custom_query = generate_custom_graphql_query(purchase_inputs, case_execution_input, wait_for_case_stack_job_name)
@ -522,19 +368,43 @@ async def generate_graphql_query(
request_time = datetime.now() request_time = datetime.now()
full_path = str(request.url.path) full_path = str(request.url.path)
session = requests.Session() session = requests.Session()
response = session.post(url=url, headers=headers, json=payload_json, auth=auth, verify=False) try:
response_time = datetime.now() response = await session.post(url=url, headers=headers, json=payload_json, auth=auth, verify=False, timeout=300)
print(type(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） except requests.exceptions.ConnectTimeout as e:
insert_api_log( # 构造符合错误情况的响应数据字典
request_time, error_response_data = {
full_path, "errors": [{"message": "连接超时，请检查网络或稍后重试"}],
'POST', "data": {},
json.dumps(payload_json), "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
json.dumps(response.json()), }
response_time 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_json),
json.dumps(res),
response_time
)
if response.status_code!= 200: if response.status_code!= 200:
raise HTTPException(status_code=response.status_code, detail=response.text) raise HTTPException(status_code=response.status_code, detail=response.text)
@ -542,6 +412,182 @@ async def generate_graphql_query(
except TypeError as e: except TypeError as e:
return {"error": str(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__": if __name__ == "__main__":
import uvicorn 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):

110
原油价格预测准确率计算.ipynb
View File

@ -2,7 +2,7 @@
"cells": [ "cells": [
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 9, "execution_count": 13,
"id": "9daadf20-caa6-4b25-901c-6cc3ef563f58", "id": "9daadf20-caa6-4b25-901c-6cc3ef563f58",
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
@ -126,7 +126,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 10, "execution_count": 14,
"id": "0d77ab7d", "id": "0d77ab7d",
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
@ -138,7 +138,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 11, "execution_count": 15,
"id": "e51c3fd0-6bff-45de-b8b6-971e7986c7a7", "id": "e51c3fd0-6bff-45de-b8b6-971e7986c7a7",
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
@ -151,103 +151,109 @@
" 开始日期 结束日期 准确率\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",
" 开始日期 结束日期 准确率\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", " 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",
" 开始日期 结束日期 准确率\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",
" 开始日期 结束日期 准确率\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", " 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", " 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", " 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", " 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", " 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", " 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", " 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", " 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", " 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", " 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", " 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", " 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", " 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", " 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", " 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", " 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", " 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", " 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", " 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", " 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", " 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", " 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", " 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", " 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", " 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", " 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", " 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", " 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", " 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", " 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", " 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", " 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", " 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", " 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", " 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", " 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", " 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", " 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", " 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", " 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", " 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", " 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", " 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", " 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", " 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", "cell_type": "code",
"execution_count": 12, "execution_count": 16,
"id": "0f942c69", "id": "0f942c69",
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
@ -334,7 +340,7 @@
"\u001b[1;31mKeyError\u001b[0m: 'PREDICT_DATE'", "\u001b[1;31mKeyError\u001b[0m: 'PREDICT_DATE'",
"\nThe above exception was the direct cause of the following exception:\n", "\nThe above exception was the direct cause of the following exception:\n",
"\u001b[1;31mKeyError\u001b[0m Traceback (most recent call last)", "\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\\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", "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'" "\u001b[1;31mKeyError\u001b[0m: 'PREDICT_DATE'"