From 2896312a483380aef6258def82f85b08f841db2b Mon Sep 17 00:00:00 2001 From: workpc Date: Fri, 20 Dec 2024 10:44:15 +0800 Subject: [PATCH] =?UTF-8?q?=E5=A6=82=E6=9E=9C=E4=B8=BAis=5Ffivemodels?= =?UTF-8?q?=E4=B8=BA=E7=9C=9F=EF=BC=8C=E5=88=99=E4=B8=8D=E4=BF=AE=E6=94=B9?= =?UTF-8?q?=E6=9C=80=E4=BD=B3=E6=A8=A1=E5=9E=8B?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- auptest.py | 419 ++++++++++++++++++++++++++++++----- models/nerulforcastmodels.py | 7 +- 原油价格预测准确率计算.ipynb | 246 +++++++++++++------- 3 files changed, 540 insertions(+), 132 deletions(-) diff --git a/auptest.py b/auptest.py index fe4fd88..3980936 100644 --- a/auptest.py +++ b/auptest.py @@ -1,10 +1,14 @@ -from fastapi import FastAPI, HTTPException, Body -from fastapi.middleware.cors import CORSMiddleware import requests +import json +import functools +from fastapi import FastAPI, HTTPException, Body,Request +from fastapi.middleware.cors import CORSMiddleware from requests_ntlm import HttpNtlmAuth import urllib3 urllib3.disable_warnings(urllib3.exceptions.InsecureRequestWarning) +from typing import Dict + ''' sql: @@ -40,7 +44,7 @@ from datetime import datetime # host = 'rm-2zehj3r1n60ttz9x5.mysql.rds.aliyuncs.com' # 服务器访问使用 # database = 'jingbo_test' # 服务器访问使用 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 @@ -49,26 +53,42 @@ config = { "user": "jingbo", "password": "shihua@123", "host": host, - "database": "jingbo_test" + "database": database } +''' + `ID` varchar(128) NOT NULL COMMENT 'ID', + `REQUEST_METHOD` varchar(128) DEFAULT NULL COMMENT '方法名称', + `REQUEST_TIME` datetime DEFAULT NULL COMMENT '请求时间', + `REQUEST_URL` varchar(256) DEFAULT NULL COMMENT '请求URL', + `USING_FLAG` varchar(1) DEFAULT NULL COMMENT '启用状态', + `REQUEST_PARAMS` text COMMENT '接收到的请求参数', + `RESPONSE_CONTENT` text COMMENT '响应内容', + `RESPONSE_TIME` datetime DEFAULT NULL COMMENT '响应时间', + + +''' - -def insert_api_log(request_time, request_ip, request_url, request_method, request_params, response_status_code, response_content, response_time): +def insert_api_log(request_time, request_url, request_method, request_params, response_content, response_time): try: # 建立数据库连接 cnx = mysql.connector.connect(**config) cursor = cnx.cursor() + # 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" + cursor.execute(count_query) + 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 # 插入数据的SQL语句 insert_query = """ - INSERT INTO pims_api_log (request_time, request_ip, request_url, request_method, request_params, response_status_code, response_content, response_time) - VALUES (%s, %s, %s, %s, %s, %s, %s, %s) + 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) """ # 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 = (request_time, request_ip, request_url, request_method, request_params, response_status_code, response_content, response_time) + data = (new_id,request_time, request_url, request_method, request_params, response_content, response_time) # 执行插入操作 cursor.execute(insert_query, data) # 提交事务，使插入生效 @@ -98,14 +118,36 @@ app.add_middleware( 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' -graphql_host = '10.88.14.86' +graphql_host = 'http://10.88.14.86' graphql_path = '/AspenTech/AspenUnified/api/v1/model/Chambroad20241205/graphql' - +url = graphql_host + graphql_path query = """ mutation{ purchases{ - update(inputs:[%s + 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 + } + ] + }, ] }]) } @@ -120,6 +162,7 @@ mutation{ {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"} + ] } ) @@ -146,6 +189,29 @@ payload_json = { "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}') @@ -176,57 +242,306 @@ example_query = ''' }, ''' + +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 execute_graphql_query( - query: str = Body(..., example=example_query) # 使用Body和example参数添加示例 -): +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) - if response.status_code != 200: - raise HTTPException(status_code=response.status_code, detail=response.text) - return response.json() + response_time = datetime.now() -query2 = ''' -query -{ - cases - { - items - { - name - } - } -} -''' - -payload_json2 = { - "query": query2, - "operationName": "" -} - -@app.get("/cases") -async def get_cases_query_async(): - session = requests.Session() - response = session.post(url=url, headers=headers, json=payload_json2, auth=auth, verify=False) + # 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( - datetime.now(), - 'IP_ADDRESS '12 - 'IP_ADDRESS', - 'URL_ADDRESS, - 'http://127.0.0.1:8000/cases', - 'GET', - '', - response.status_code, - response.text, - datetime.now() + 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( + purchase_inputs=None, + case_execution_input=None, + wait_for_case_stack_job_name=None +): + base_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 + } + } + } +} +} + """ + + # 检查purchase_inputs参数类型，如果不为None，需为列表类型，且列表元素需为字典类型 + if purchase_inputs is not None: + if not isinstance(purchase_inputs, list): + raise TypeError("purchase_inputs should be a list or None.") + for input_data in purchase_inputs: + if not isinstance(input_data, dict): + raise TypeError("Elements in purchase_inputs should be dictionaries.") + + # 检查case_execution_input参数类型，如果不为None，需为字典类型 + if case_execution_input is not None: + if not isinstance(case_execution_input, dict): + raise TypeError("case_execution_input should be a dictionary or None.") + + # 检查wait_for_case_stack_job_name参数类型，如果不为None，需为字符串类型 + if wait_for_case_stack_job_name is not None: + if not isinstance(wait_for_case_stack_job_name, str): + raise TypeError("wait_for_case_stack_job_name should be a string or None.") + + if purchase_inputs: + # 购买相关的inputs部分的模板 + 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: + input_str = f""" + name: "{input_data['name']}" + inputs: [ + """ + inner_inputs = input_data.get('inputs', []) + for inner_input in inner_inputs: + inner_str = f""" + {{ + field: "{inner_input['field']}" + periodName: "{inner_input['periodName']}" + value: {inner_input['value']} + }} + """ + input_str += inner_str + input_str += " ]" + new_purchase_inputs_str += input_str + + base_query = base_query.replace(purchase_inputs_template, new_purchase_inputs_str) + + 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""" + name: "{case_execution_input['name']}" + cases: [ + """ + for case in case_execution_input['cases']: + case_str = f""" + {{name: "{case['name']}"}} + """ + input_dict_str += case_str + input_dict_str += " ]" + + base_query = base_query.replace(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) + + 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") +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) +): + try: + custom_query = generate_custom_graphql_query(purchase_inputs, case_execution_input, wait_for_case_stack_job_name) + payload_json = { + "query": custom_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() + except TypeError as e: + return {"error": str(e)} + if __name__ == "__main__": import uvicorn - uvicorn.run(app, host="127.0.0.1", port=8000) + uvicorn.run(app, host="127.0.0.1", port=8001) diff --git a/models/nerulforcastmodels.py b/models/nerulforcastmodels.py index 5afb0b2..48252d4 100644 --- a/models/nerulforcastmodels.py +++ b/models/nerulforcastmodels.py @@ -678,8 +678,11 @@ def model_losss_juxiting(sqlitedb): # 保存5个最佳模型的名称 if len(modelnames) > 5: modelnames = modelnames[0:5] - with open(os.path.join(dataset,"best_modelnames.txt"), 'w') as f: - f.write(','.join(modelnames) + '\n') + if is_fivemodels: + pass + else: + with open(os.path.join(dataset,"best_modelnames.txt"), 'w') as f: + f.write(','.join(modelnames) + '\n') # 预测值与真实值对比图 plt.rcParams['font.sans-serif'] = ['SimHei'] diff --git a/原油价格预测准确率计算.ipynb b/原油价格预测准确率计算.ipynb index fd7f512..e2f3285 100644 --- a/原油价格预测准确率计算.ipynb +++ b/原油价格预测准确率计算.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 13, + "execution_count": 9, "id": "9daadf20-caa6-4b25-901c-6cc3ef563f58", "metadata": {}, "outputs": [ @@ -10,65 +10,65 @@ "name": "stdout", "output_type": "stream", "text": [ - "(85, 28)\n", - "(22, 4)\n", - "(85, 31)\n", - " ds NHITS Informer LSTM iTransformer TSMixer \\\n", - "0 2024-11-25 75.714300 75.523370 73.614220 75.27068 75.03936 \n", - "1 2024-11-26 76.039635 75.558270 73.692310 75.04110 74.60100 \n", - "2 2024-11-27 77.375790 75.361885 73.826140 74.99121 74.37731 \n", - "3 2024-11-28 78.872400 76.339920 73.883484 75.79425 74.04826 \n", - "4 2024-11-29 79.576970 76.333170 73.876396 75.89008 74.07330 \n", + "(255, 28)\n", + "(78, 4)\n", + "(255, 31)\n", + " ds NHITS Informer LSTM iTransformer TSMixer \\\n", + "0 2024-10-08 79.76823 80.197660 79.802414 78.391460 80.200510 \n", + "1 2024-10-09 78.75903 80.235740 79.844154 78.888565 79.861180 \n", + "2 2024-10-10 79.43970 80.186230 79.885100 79.488700 79.483086 \n", + "3 2024-10-11 79.62268 80.502975 79.878560 79.406670 79.313965 \n", + "4 2024-10-14 79.91698 80.931946 79.936270 79.758575 79.197430 \n", "\n", - " TSMixerx PatchTST RNN GRU ... y \\\n", - "0 74.581190 75.70277 74.721280 74.512060 ... 73.010002 \n", - "1 73.496025 75.97611 74.588060 74.713425 ... 72.809998 \n", - "2 73.522026 76.48628 74.486400 74.946010 ... 72.830002 \n", - "3 73.416306 76.38267 75.195710 74.946014 ... 73.279999 \n", - "4 73.521570 76.20661 75.089966 74.935165 ... 72.940002 \n", + " TSMixerx PatchTST RNN GRU ... y \\\n", + "0 79.243256 80.883450 80.836105 81.276060 ... 77.180000 \n", + "1 78.068150 80.950096 80.917860 81.303505 ... 76.580002 \n", + "2 77.189064 80.347400 80.866040 81.798050 ... 79.400002 \n", + "3 77.840096 80.545296 81.167710 81.552810 ... 79.040001 \n", + "4 77.904300 81.432976 81.144210 81.483215 ... 77.459999 \n", "\n", " min_within_quantile max_within_quantile id CREAT_DATE min_price \\\n", - "0 74.41491 75.29100 1 2024-11-22 74.414910 \n", - "1 74.11780 74.95678 2 2024-11-22 73.496025 \n", - "2 73.93820 74.50395 3 2024-11-22 73.522026 \n", - "3 73.85808 74.46382 4 2024-11-22 73.416306 \n", - "4 73.96690 74.81860 5 2024-11-22 73.521570 \n", + "0 80.200510 81.163630 51 2024-10-07 79.848624 \n", + "1 79.861180 81.757850 52 2024-10-07 79.981211 \n", + "2 79.483086 81.190400 53 2024-10-07 79.398409 \n", + "3 79.313965 81.371100 54 2024-10-07 79.394607 \n", + "4 79.197430 81.432976 55 2024-10-07 79.351007 \n", "\n", " max_price 序号 LOW_PRICE HIGH_PRICE \n", - "0 75.959854 15.0 72.30 74.83 \n", - "1 77.182580 14.0 71.63 73.80 \n", - "2 78.378624 13.0 71.71 72.85 \n", - "3 79.415400 12.0 71.85 72.96 \n", - "4 79.576970 11.0 71.75 73.34 \n", + "0 81.848624 52.0 76.36 81.14 \n", + "1 81.981211 51.0 75.15 78.02 \n", + "2 81.398409 50.0 76.72 79.72 \n", + "3 81.394607 49.0 78.04 79.50 \n", + "4 81.351007 48.0 74.86 78.55 \n", "\n", "[5 rows x 31 columns]\n", - " ds NHITS Informer LSTM iTransformer TSMixer \\\n", - "80 2024-12-16 74.53431 73.944080 71.68200 74.022340 74.295820 \n", - "81 2024-12-17 74.81450 73.830450 71.95232 74.314950 74.167290 \n", - "82 2024-12-18 75.55861 73.525100 72.00824 74.441380 74.212180 \n", - "83 2024-12-19 75.36518 74.012215 72.20199 74.397190 74.330130 \n", - "84 2024-12-20 74.78187 73.929596 72.23908 74.510895 74.208084 \n", + " ds NHITS Informer LSTM iTransformer TSMixer \\\n", + "250 2024-12-16 74.268654 73.333750 73.090164 74.007034 74.36094 \n", + "251 2024-12-17 74.724630 73.373810 73.242540 74.285530 74.24597 \n", + "252 2024-12-18 74.948860 73.505330 73.400400 74.260290 74.06419 \n", + "253 2024-12-19 74.396740 73.934380 73.764320 74.429800 74.18059 \n", + "254 2024-12-20 73.882930 73.700935 73.769050 73.977585 73.97370 \n", "\n", - " TSMixerx PatchTST RNN GRU ... y min_within_quantile \\\n", - "80 74.41700 74.587390 73.607780 73.747700 ... NaN 74.231680 \n", - "81 74.36576 74.363060 73.688736 73.833950 ... NaN 73.735420 \n", - "82 74.29719 74.073555 73.456700 74.146034 ... NaN 74.073555 \n", - "83 73.79145 74.529945 74.230125 74.144520 ... NaN 74.330130 \n", - "84 74.59672 74.231255 74.201860 73.996100 ... NaN 74.083810 \n", + " TSMixerx PatchTST RNN GRU ... y min_within_quantile \\\n", + "250 74.67781 74.475680 75.63023 74.853800 ... NaN 74.157196 \n", + "251 74.46460 74.628000 75.22519 74.957530 ... NaN 73.711680 \n", + "252 74.32628 74.656815 75.49716 74.890236 ... NaN 74.064190 \n", + "253 74.41026 74.698875 75.87007 75.118866 ... NaN 74.148070 \n", + "254 74.49235 74.345410 75.88466 75.186325 ... NaN 73.816990 \n", "\n", - " max_within_quantile id CREAT_DATE min_price max_price 序号 LOW_PRICE \\\n", - "80 74.621160 81 2024-12-16 72.75007 74.62116 NaN NaN \n", - "81 74.682365 82 2024-12-16 72.72196 74.81450 NaN NaN \n", - "82 75.157074 83 2024-12-16 73.12483 75.55861 NaN NaN \n", - "83 75.339240 84 2024-12-16 73.07359 75.36518 NaN NaN \n", - "84 74.604610 85 2024-12-16 72.93583 74.78187 NaN NaN \n", + " max_within_quantile id CREAT_DATE min_price max_price 序号 \\\n", + "250 74.576454 301 2024-12-16 73.416857 75.416857 3.0 \n", + "251 74.948060 302 2024-12-16 73.434301 75.434301 2.0 \n", + "252 75.200980 303 2024-12-16 73.707471 75.707471 1.0 \n", + "253 75.395440 304 2024-12-16 73.639791 75.639791 NaN \n", + "254 74.345410 305 2024-12-16 73.067399 75.067399 NaN \n", "\n", - " HIGH_PRICE \n", - "80 NaN \n", - "81 NaN \n", - "82 NaN \n", - "83 NaN \n", - "84 NaN \n", + " LOW_PRICE HIGH_PRICE \n", + "250 72.53 73.28 \n", + "251 72.48 74.18 \n", + "252 72.80 74.20 \n", + "253 NaN NaN \n", + "254 NaN NaN \n", "\n", "[5 rows x 31 columns]\n" ] @@ -79,9 +79,9 @@ "import os\n", "import pandas as pd\n", "\n", - "# dataset = r'D:\\liurui\\dev\\code\\PriceForecast\\yuanyoudataset'\n", + "dataset = r'yuanyoudataset'\n", "\n", - "dataset = r'C:\\Users\\Administrator\\Desktop' \n", + "# dataset = r'C:\\Users\\Administrator\\Desktop' \n", "\n", "# 预测价格数据\n", "# dbfilename = os.path.join(r'D:\\code\\PriceForecast\\yuanyoudataset','jbsh_yuanyou.db')\n", @@ -93,7 +93,7 @@ "# print(df1.shape)\n", "\n", "# 预测价格数据\n", - "dfcsvfilename = os.path.join(dataset,'accuracy_ten.csv')\n", + "dfcsvfilename = os.path.join(dataset,'accuracy_five_mean.csv')\n", "df1 = pd.read_csv(dfcsvfilename)\n", "print(df1.shape)\n", "\n", @@ -126,19 +126,19 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 10, "id": "0d77ab7d", "metadata": {}, "outputs": [], "source": [ "# 模型评估前五均值 \n", - "df['min_price'] = df.iloc[:,1:11].mean(axis=1) -2\n", - "df['max_price'] = df.iloc[:,1:11].mean(axis=1) +2" + "df['min_price'] = df.iloc[:,1:6].mean(axis=1) -1.5\n", + "df['max_price'] = df.iloc[:,1:6].mean(axis=1) +1.5" ] }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 11, "id": "e51c3fd0-6bff-45de-b8b6-971e7986c7a7", "metadata": {}, "outputs": [ @@ -147,39 +147,107 @@ "output_type": "stream", "text": [ " 开始日期 结束日期 准确率\n", - "0 2024-11-08 2024-11-15 0\n", + "0 2024-09-27 2024-10-04 0\n", " 开始日期 结束日期 准确率\n", - "0 2024-11-15 2024-11-22 0\n", + "0 2024-09-27 2024-10-04 0\n", " 开始日期 结束日期 准确率\n", - "0 2024-11-15 2024-11-22 0\n", + "0 2024-09-27 2024-10-04 0\n", " 开始日期 结束日期 准确率\n", - "0 2024-11-15 2024-11-22 0\n", + "0 2024-09-27 2024-10-04 0\n", " 开始日期 结束日期 准确率\n", - "0 2024-11-15 2024-11-22 0\n", - " 开始日期 结束日期 准确率\n", - "0 2024-11-15 2024-11-22 0\n", + "0 2024-09-27 2024-10-04 0\n", " 开始日期 结束日期 准确率\n", - "0 2024-11-22 2024-11-29 0.808456\n", + "0 2024-10-04 2024-10-11 0.495046\n", " 开始日期 结束日期 准确率\n", - "0 2024-11-22 2024-11-29 0.808456\n", + "0 2024-10-04 2024-10-11 0.495046\n", " 开始日期 结束日期 准确率\n", - "0 2024-11-22 2024-11-29 0.808456\n", + "0 2024-10-04 2024-10-11 0.495046\n", " 开始日期 结束日期 准确率\n", - "0 2024-11-22 2024-11-29 0.808456\n", + "0 2024-10-04 2024-10-11 0.495046\n", " 开始日期 结束日期 准确率\n", - "0 2024-11-22 2024-11-29 0.808456\n", + "0 2024-10-04 2024-10-11 0.495046\n", " 开始日期 结束日期 准确率\n", - "0 2024-11-29 2024-12-06 0.955061\n", + "0 2024-10-11 2024-10-18 0.449368\n", " 开始日期 结束日期 准确率\n", - "0 2024-11-29 2024-12-06 0.955061\n", + "0 2024-10-11 2024-10-18 0.449368\n", " 开始日期 结束日期 准确率\n", - "0 2024-11-29 2024-12-06 0.955061\n", + "0 2024-10-11 2024-10-18 0.449368\n", " 开始日期 结束日期 准确率\n", - "0 2024-11-29 2024-12-06 0.955061\n", + "0 2024-10-11 2024-10-18 0.449368\n", " 开始日期 结束日期 准确率\n", - "0 2024-11-29 2024-12-06 0.955061\n", + "0 2024-10-11 2024-10-18 0.449368\n", " 开始日期 结束日期 准确率\n", - "0 2024-12-06 2024-12-13 0.905554\n" + "0 2024-10-18 2024-10-25 0.814057\n", + " 开始日期 结束日期 准确率\n", + "0 2024-10-18 2024-10-25 0.814057\n", + " 开始日期 结束日期 准确率\n", + "0 2024-10-18 2024-10-25 0.814057\n", + " 开始日期 结束日期 准确率\n", + "0 2024-10-18 2024-10-25 0.814057\n", + " 开始日期 结束日期 准确率\n", + "0 2024-10-18 2024-10-25 0.814057\n", + " 开始日期 结束日期 准确率\n", + "0 2024-10-25 2024-11-01 0.433599\n", + " 开始日期 结束日期 准确率\n", + "0 2024-10-25 2024-11-01 0.433599\n", + " 开始日期 结束日期 准确率\n", + "0 2024-10-25 2024-11-01 0.433599\n", + " 开始日期 结束日期 准确率\n", + "0 2024-10-25 2024-11-01 0.433599\n", + " 开始日期 结束日期 准确率\n", + "0 2024-10-25 2024-11-01 0.433599\n", + " 开始日期 结束日期 准确率\n", + "0 2024-11-01 2024-11-08 0.894767\n", + " 开始日期 结束日期 准确率\n", + "0 2024-11-01 2024-11-08 0.894767\n", + " 开始日期 结束日期 准确率\n", + "0 2024-11-01 2024-11-08 0.894767\n", + " 开始日期 结束日期 准确率\n", + "0 2024-11-01 2024-11-08 0.894767\n", + " 开始日期 结束日期 准确率\n", + "0 2024-11-01 2024-11-08 0.894767\n", + " 开始日期 结束日期 准确率\n", + "0 2024-11-08 2024-11-15 0.915721\n", + " 开始日期 结束日期 准确率\n", + "0 2024-11-08 2024-11-15 0.915721\n", + " 开始日期 结束日期 准确率\n", + "0 2024-11-08 2024-11-15 0.915721\n", + " 开始日期 结束日期 准确率\n", + "0 2024-11-08 2024-11-15 0.915721\n", + " 开始日期 结束日期 准确率\n", + "0 2024-11-08 2024-11-15 0.915721\n", + " 开始日期 结束日期 准确率\n", + "0 2024-11-15 2024-11-22 0.835755\n", + " 开始日期 结束日期 准确率\n", + "0 2024-11-15 2024-11-22 0.835755\n", + " 开始日期 结束日期 准确率\n", + "0 2024-11-15 2024-11-22 0.835755\n", + " 开始日期 结束日期 准确率\n", + "0 2024-11-15 2024-11-22 0.835755\n", + " 开始日期 结束日期 准确率\n", + "0 2024-11-15 2024-11-22 0.835755\n", + " 开始日期 结束日期 准确率\n", + "0 2024-11-22 2024-11-29 0.718009\n", + " 开始日期 结束日期 准确率\n", + "0 2024-11-22 2024-11-29 0.718009\n", + " 开始日期 结束日期 准确率\n", + "0 2024-11-22 2024-11-29 0.718009\n", + " 开始日期 结束日期 准确率\n", + "0 2024-11-22 2024-11-29 0.718009\n", + " 开始日期 结束日期 准确率\n", + "0 2024-11-22 2024-11-29 0.718009\n", + " 开始日期 结束日期 准确率\n", + "0 2024-11-29 2024-12-06 0.948363\n", + " 开始日期 结束日期 准确率\n", + "0 2024-11-29 2024-12-06 0.948363\n", + " 开始日期 结束日期 准确率\n", + "0 2024-11-29 2024-12-06 0.948363\n", + " 开始日期 结束日期 准确率\n", + "0 2024-11-29 2024-12-06 0.948363\n", + " 开始日期 结束日期 准确率\n", + "0 2024-11-29 2024-12-06 0.948363\n", + " 开始日期 结束日期 准确率\n", + "0 2024-12-06 2024-12-13 0.947006\n" ] } ], @@ -239,7 +307,7 @@ "end_times = df['CREAT_DATE'].unique()\n", "for endtime in end_times:\n", " up_week_dates = get_week_date(endtime)\n", - " _get_accuracy_rate(df,up_week_dates,end_time)\n", + " _get_accuracy_rate(df,up_week_dates,endtime)\n", "\n", "# 打印结果\n", "\n" @@ -247,10 +315,32 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 12, "id": "0f942c69", "metadata": {}, - "outputs": [], + "outputs": [ + { + "ename": "KeyError", + "evalue": "'PREDICT_DATE'", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mKeyError\u001b[0m Traceback (most recent call last)", + "File \u001b[1;32md:\\ProgramData\\anaconda3\\Lib\\site-packages\\pandas\\core\\indexes\\base.py:3791\u001b[0m, in \u001b[0;36mIndex.get_loc\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m 3790\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m-> 3791\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_engine\u001b[38;5;241m.\u001b[39mget_loc(casted_key)\n\u001b[0;32m 3792\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m err:\n", + "File \u001b[1;32mindex.pyx:152\u001b[0m, in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n", + "File \u001b[1;32mindex.pyx:181\u001b[0m, in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n", + "File \u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi:7080\u001b[0m, in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n", + "File \u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi:7088\u001b[0m, in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n", + "\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", + "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'" + ] + } + ], "source": [ "import datetime\n", "# ds 按周取\n",