添加雍安环境配置
This commit is contained in:
workpc
parent
19f76e6b83
commit
c9c456f750
@ -1,4 +0,0 @@
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@echo on
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d:
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cd code/PricePredict/
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C:/Users/Hello/.conda/envs/predict-py397/python.exe main.py
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@ -1,65 +0,0 @@
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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 6,
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"id": "b3cde8ea",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"{'ovx index': '原油波动率', 'dxy curncy': '美元指数', 'C2403128043': 'Brent连1合约价格拟合残差/美元指数', 'C2403150124': 'Brent连1合约价格拟合残差/Brent 连2-连3', 'DOESCRUD Index': '美国商业原油库存', 'FVHCM1 INDEX': '美国取暖油裂解C1', 'doedtprd index': '美国成品油表需', 'CFFDQMMN INDEX': 'WTI管理资金净多持仓', 'C2403083739': 'WTI基金多空持仓比', 'C2404167878': 'WTI基金净持仓COT指标（代码运算）', 'lmcads03 lme comdty': 'LME铜价', 'GC1 COMB Comdty': '黄金连1合约', 'C2404167855': '金油比'}\n"
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]
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}
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],
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"source": [
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"data = \"\"\"\n",
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"ovx index 原油波动率\n",
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"dxy curncy 美元指数\n",
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"C2403128043 Brent连1合约价格拟合残差/美元指数\n",
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"C2403150124 Brent连1合约价格拟合残差/Brent 连2-连3\n",
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"DOESCRUD Index 美国商业原油库存\n",
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"FVHCM1 INDEX 美国取暖油裂解C1\n",
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"doedtprd index 美国成品油表需\n",
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"CFFDQMMN INDEX WTI管理资金净多持仓\n",
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"C2403083739 WTI基金多空持仓比\n",
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"C2404167878 WTI基金净持仓COT指标（代码运算）\n",
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"lmcads03 lme comdty LME铜价\n",
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"GC1 COMB Comdty 黄金连1合约\n",
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"C2404167855 金油比\n",
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"\"\"\"\n",
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"\n",
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"result_dict = {}\n",
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"lines = data.strip().split('\\n')\n",
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"for line in lines:\n",
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" key, value = line.strip().split(' ')\n",
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" result_dict[key] = value\n",
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"\n",
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"print(result_dict)"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "base",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.11.7"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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139
config_jingbo.py
139
config_jingbo.py
@ -197,24 +197,142 @@ ClassifyId = 1214
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################################################################################################################ 变量定义--测试环境
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login_pushreport_url = "http://192.168.100.53:8080/jingbo-dev/api/server/login"
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upload_url = "http://192.168.100.53:8080/jingbo-dev/api/analysis/reportInfo/researchUploadReportSave"
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# login_pushreport_url = "http://192.168.100.53:8080/jingbo-dev/api/server/login"
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# upload_url = "http://192.168.100.53:8080/jingbo-dev/api/analysis/reportInfo/researchUploadReportSave"
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# # upload_url = "http://192.168.100.109:8080/jingbo/api/analysis/reportInfo/researchUploadReportSave" # zhaoqiwei
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# upload_warning_url = "http://192.168.100.53:8080/jingbo-dev/api/basicBuiness/crudeOilWarning/save"
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# query_data_list_item_nos_url = "http://192.168.100.53:8080/jingbo-dev/api/warehouse/dwDataItem/queryDataListItemNos"
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# login_data = {
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# "data": {
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# "account": "api_test",
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# # "password": "MmVmNzNlOWI0MmY0ZDdjZGUwNzE3ZjFiMDJiZDZjZWU=", # Shihua@123456
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# "password": "ZTEwYWRjMzk0OWJhNTlhYmJlNTZlMDU3ZjIwZjg4M2U=", # 123456
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# "tenantHashCode": "8a4577dbd919675758d57999a1e891fe",
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# "terminal": "API"
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# },
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# "funcModule": "API",
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# "funcOperation": "获取token"
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# }
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# # upload_data = {
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# # "funcModule":'研究报告信息',
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# # "funcOperation":'上传原油价格预测报告',
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# # "data":{
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# # "ownerAccount":'arui', #报告所属用户账号
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# # "reportType":'OIL_PRICE_FORECAST', # 报告类型，固定为OIL_PRICE_FORECAST
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# # "fileName": '2000-40-5-50--100-原油指标数据.xlsx-Brent活跃合约--2024-09-06-15-01-29-预测报告.pdf', #文件名称
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# # "fileBase64": '' ,#文件内容base64
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# # "categoryNo":'yyjgycbg', # 研究报告分类编码
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# # "smartBusinessClassCode":'YCJGYCBG', #分析报告分类编码
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# # "reportEmployeeCode":"E40116", # 报告人
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# # "reportDeptCode" :"D0044" ,# 报告部门
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# # "productGroupCode":"RAW_MATERIAL" # 商品分类
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# # }
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# # }
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# upload_data = {
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# "funcModule":'研究报告信息',
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# "funcOperation":'上传原油价格预测报告',
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# "data":{
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# "ownerAccount":'arui', #报告所属用户账号
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# "reportType":'OIL_PRICE_FORECAST', # 报告类型，固定为OIL_PRICE_FORECAST
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# "fileName": '2000-40-5-50--100-原油指标数据.xlsx-Brent活跃合约--2024-09-06-15-01-29-预测报告.pdf', #文件名称
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# "fileBase64": '' ,#文件内容base64
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# "categoryNo":'yyjgycbg', # 研究报告分类编码
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# "smartBusinessClassCode":'1', #分析报告分类编码
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# "reportEmployeeCode":"E40116", # 报告人
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# "reportDeptCode" :"D0044" ,# 报告部门
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# "productGroupCode":"RAW_MATERIAL" # 商品分类
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# }
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# }
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# warning_data = {
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# "funcModule":'原油特征停更预警',
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# "funcOperation":'原油特征停更预警',
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# "data":{
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# 'WARNING_TYPE_NAME':'特征数据停更预警',
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# 'WARNING_CONTENT':'',
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# 'WARNING_DATE':''
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# }
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# }
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# query_data_list_item_nos_data = {
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# "funcModule": "数据项",
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# "funcOperation": "查询",
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# "data": {
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# "dateStart":"20200101",
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# "dateEnd":"20241231",
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# "dataItemNoList":["Brentzdj","Brentzgj"] # 数据项编码,代表 brent最低价和最高价
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# }
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# }
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# # 北京环境数据库
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# host = '192.168.101.27'
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# port = 3306
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# dbusername ='root'
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# password = '123456'
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# dbname = 'jingbo_test'
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# table_name = 'v_tbl_crude_oil_warning'
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# ### 开关
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# is_train = False # 是否训练
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# is_debug = False # 是否调试
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# is_eta = False # 是否使用eta接口
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# is_market = True # 是否通过市场信息平台获取特征 ,在is_eta 为true 的情况下生效
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# is_timefurture = True # 是否使用时间特征
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# is_fivemodels = False # 是否使用之前保存的最佳的5个模型
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# is_edbcode = False # 特征使用edbcoding列表中的
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# is_edbnamelist = False # 自定义特征，对应上面的edbnamelist
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# is_update_eta = False # 预测结果上传到eta
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# is_update_report = True # 是否上传报告
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# is_update_warning_data = False # 是否上传预警数据
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# is_del_corr = 0.6 # 是否删除相关性高的特征,取值为 0-1 ，0 为不删除，0.6 表示删除相关性小于0.6的特征
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# is_del_tow_month = True # 是否删除两个月不更新的特征
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################################################################################################################ 变量定义--雍安测试环境
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login_pushreport_url = "http://192.168.100.115:9090/dom-api/api/server/login"
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upload_url = "http://192.168.100.115:9090/dom-api/api/analysis/reportInfo/researchUploadReportSave"
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# upload_url = "http://192.168.100.109:8080/jingbo/api/analysis/reportInfo/researchUploadReportSave" # zhaoqiwei
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upload_warning_url = "http://192.168.100.53:8080/jingbo-dev/api/basicBuiness/crudeOilWarning/save"
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query_data_list_item_nos_url = "http://192.168.100.53:8080/jingbo-dev/api/warehouse/dwDataItem/queryDataListItemNos"
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upload_warning_url = "http://192.168.100.115:9090/dom-api/api/basicBuiness/crudeOilWarning/save"
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query_data_list_item_nos_url = "http://192.168.100.115:9090/dom-api/api/warehouse/dwDataItem/queryDataListItemNos"
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login_data = {
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"data": {
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"account": "api_test",
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# "password": "MmVmNzNlOWI0MmY0ZDdjZGUwNzE3ZjFiMDJiZDZjZWU=", # Shihua@123456
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"password": "ZTEwYWRjMzk0OWJhNTlhYmJlNTZlMDU3ZjIwZjg4M2U=", # 123456
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"tenantHashCode": "8a4577dbd919675758d57999a1e891fe",
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"account": "api-dev",
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"password": "ZTEwYWRjMzk0OWJhNTlhYmJlNTZlMDU3ZjIwZjg4M2U=",
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"tenantHashCode": "1eb24ab5a6af12e30daf78af276664f1",
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"terminal": "API"
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},
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"funcModule": "API",
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"funcOperation": "获取token"
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}
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# upload_data = {
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# "funcModule":'研究报告信息',
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# "funcOperation":'上传原油价格预测报告',
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# "data":{
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# "ownerAccount":'arui', #报告所属用户账号
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# "reportType":'OIL_PRICE_FORECAST', # 报告类型，固定为OIL_PRICE_FORECAST
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# "fileName": '2000-40-5-50--100-原油指标数据.xlsx-Brent活跃合约--2024-09-06-15-01-29-预测报告.pdf', #文件名称
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# "fileBase64": '' ,#文件内容base64
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# "categoryNo":'yyjgycbg', # 研究报告分类编码
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# "smartBusinessClassCode":'YCJGYCBG', #分析报告分类编码
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# "reportEmployeeCode":"E40116", # 报告人
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# "reportDeptCode" :"D0044" ,# 报告部门
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# "productGroupCode":"RAW_MATERIAL" # 商品分类
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# }
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# }
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upload_data = {
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"funcModule":'研究报告信息',
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"funcOperation":'上传原油价格预测报告',
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@ -224,13 +342,14 @@ upload_data = {
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"fileName": '2000-40-5-50--100-原油指标数据.xlsx-Brent活跃合约--2024-09-06-15-01-29-预测报告.pdf', #文件名称
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"fileBase64": '' ,#文件内容base64
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"categoryNo":'yyjgycbg', # 研究报告分类编码
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"smartBusinessClassCode":'YCJGYCBG', #分析报告分类编码
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"smartBusinessClassCode":'1', #分析报告分类编码
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"reportEmployeeCode":"E40116", # 报告人
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"reportDeptCode" :"D0044" ,# 报告部门
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"productGroupCode":"RAW_MATERIAL" # 商品分类
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}
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}
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warning_data = {
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"funcModule":'原油特征停更预警',
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"funcOperation":'原油特征停更预警',
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@ -271,7 +390,7 @@ is_fivemodels = False # 是否使用之前保存的最佳的5个模型
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is_edbcode = False # 特征使用edbcoding列表中的
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is_edbnamelist = False # 自定义特征，对应上面的edbnamelist
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is_update_eta = False # 预测结果上传到eta
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is_update_report = False # 是否上传报告
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is_update_report = True # 是否上传报告
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is_update_warning_data = False # 是否上传预警数据
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is_del_corr = 0.6 # 是否删除相关性高的特征,取值为 0-1 ，0 为不删除，0.6 表示删除相关性小于0.6的特征
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is_del_tow_month = True # 是否删除两个月不更新的特征
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358
main_yuanyou.py
358
main_yuanyou.py
@ -48,203 +48,203 @@ def predict_main():
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返回:
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None
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"""
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global end_time
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signature = BinanceAPI(APPID, SECRET)
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etadata = EtaReader(signature=signature,
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classifylisturl=classifylisturl,
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classifyidlisturl=classifyidlisturl,
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edbcodedataurl=edbcodedataurl,
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edbcodelist=edbcodelist,
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edbdatapushurl=edbdatapushurl,
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edbdeleteurl=edbdeleteurl,
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edbbusinessurl=edbbusinessurl
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)
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# 获取数据
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if is_eta:
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logger.info('从eta获取数据...')
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signature = BinanceAPI(APPID, SECRET)
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etadata = EtaReader(signature=signature,
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classifylisturl=classifylisturl,
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classifyidlisturl=classifyidlisturl,
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edbcodedataurl=edbcodedataurl,
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edbcodelist=edbcodelist,
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edbdatapushurl=edbdatapushurl,
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edbdeleteurl=edbdeleteurl,
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edbbusinessurl=edbbusinessurl,
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)
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df_zhibiaoshuju, df_zhibiaoliebiao = etadata.get_eta_api_yuanyou_data(data_set=data_set, dataset=dataset) # 原始数据，未处理
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# global end_time
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# signature = BinanceAPI(APPID, SECRET)
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# etadata = EtaReader(signature=signature,
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# classifylisturl=classifylisturl,
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# classifyidlisturl=classifyidlisturl,
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# edbcodedataurl=edbcodedataurl,
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# edbcodelist=edbcodelist,
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# edbdatapushurl=edbdatapushurl,
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# edbdeleteurl=edbdeleteurl,
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# edbbusinessurl=edbbusinessurl
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# )
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# # 获取数据
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# if is_eta:
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# logger.info('从eta获取数据...')
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# signature = BinanceAPI(APPID, SECRET)
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# etadata = EtaReader(signature=signature,
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# classifylisturl=classifylisturl,
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# classifyidlisturl=classifyidlisturl,
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# edbcodedataurl=edbcodedataurl,
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# edbcodelist=edbcodelist,
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# edbdatapushurl=edbdatapushurl,
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# edbdeleteurl=edbdeleteurl,
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# edbbusinessurl=edbbusinessurl,
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# )
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# df_zhibiaoshuju, df_zhibiaoliebiao = etadata.get_eta_api_yuanyou_data(data_set=data_set, dataset=dataset) # 原始数据，未处理
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if is_market:
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logger.info('从市场信息平台获取数据...')
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try:
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df_zhibiaoshuju = get_market_data(end_time,df_zhibiaoshuju)
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except :
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logger.info('从市场信息平台获取数据失败')
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# if is_market:
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# logger.info('从市场信息平台获取数据...')
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# try:
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# df_zhibiaoshuju = get_market_data(end_time,df_zhibiaoshuju)
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# except :
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# logger.info('从市场信息平台获取数据失败')
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# 保存到xlsx文件的sheet表
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with pd.ExcelWriter(os.path.join(dataset,data_set)) as file:
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df_zhibiaoshuju.to_excel(file, sheet_name='指标数据', index=False)
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df_zhibiaoliebiao.to_excel(file, sheet_name='指标列表', index=False)
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# # 保存到xlsx文件的sheet表
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# with pd.ExcelWriter(os.path.join(dataset,data_set)) as file:
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# df_zhibiaoshuju.to_excel(file, sheet_name='指标数据', index=False)
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# df_zhibiaoliebiao.to_excel(file, sheet_name='指标列表', index=False)
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# 数据处理
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df = datachuli(df_zhibiaoshuju, df_zhibiaoliebiao, y=y, dataset=dataset, add_kdj=add_kdj, is_timefurture=is_timefurture,
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end_time=end_time)
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# # 数据处理
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# df = datachuli(df_zhibiaoshuju, df_zhibiaoliebiao, y=y, dataset=dataset, add_kdj=add_kdj, is_timefurture=is_timefurture,
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# end_time=end_time)
|
||||
|
||||
else:
|
||||
# 读取数据
|
||||
logger.info('读取本地数据：' + os.path.join(dataset, data_set))
|
||||
df,df_zhibiaoliebiao = getdata(filename=os.path.join(dataset, data_set), y=y, dataset=dataset, add_kdj=add_kdj,
|
||||
is_timefurture=is_timefurture, end_time=end_time) # 原始数据，未处理
|
||||
# else:
|
||||
# # 读取数据
|
||||
# logger.info('读取本地数据：' + os.path.join(dataset, data_set))
|
||||
# df,df_zhibiaoliebiao = getdata(filename=os.path.join(dataset, data_set), y=y, dataset=dataset, add_kdj=add_kdj,
|
||||
# is_timefurture=is_timefurture, end_time=end_time) # 原始数据，未处理
|
||||
|
||||
# 更改预测列名称
|
||||
df.rename(columns={y: 'y'}, inplace=True)
|
||||
# # 更改预测列名称
|
||||
# df.rename(columns={y: 'y'}, inplace=True)
|
||||
|
||||
if is_edbnamelist:
|
||||
df = df[edbnamelist]
|
||||
df.to_csv(os.path.join(dataset, '指标数据.csv'), index=False)
|
||||
# 保存最新日期的y值到数据库
|
||||
# 取第一行数据存储到数据库中
|
||||
first_row = df[['ds', 'y']].tail(1)
|
||||
print(first_row['ds'].values[0])
|
||||
print(first_row['y'].values[0])
|
||||
# 判断y的类型是否为float
|
||||
if not isinstance(first_row['y'].values[0], float):
|
||||
logger.info(f'{end_time}预测目标数据为空，跳过')
|
||||
return None
|
||||
# if is_edbnamelist:
|
||||
# df = df[edbnamelist]
|
||||
# df.to_csv(os.path.join(dataset, '指标数据.csv'), index=False)
|
||||
# # 保存最新日期的y值到数据库
|
||||
# # 取第一行数据存储到数据库中
|
||||
# first_row = df[['ds', 'y']].tail(1)
|
||||
# print(first_row['ds'].values[0])
|
||||
# print(first_row['y'].values[0])
|
||||
# # 判断y的类型是否为float
|
||||
# if not isinstance(first_row['y'].values[0], float):
|
||||
# logger.info(f'{end_time}预测目标数据为空，跳过')
|
||||
# return None
|
||||
|
||||
# 将最新真实值保存到数据库
|
||||
if not sqlitedb.check_table_exists('trueandpredict'):
|
||||
first_row.to_sql('trueandpredict', sqlitedb.connection, index=False)
|
||||
else:
|
||||
for row in first_row.itertuples(index=False):
|
||||
row_dict = row._asdict()
|
||||
row_dict['ds'] = row_dict['ds'].strftime('%Y-%m-%d %H:%M:%S')
|
||||
check_query = sqlitedb.select_data('trueandpredict', where_condition=f"ds = '{row.ds}'")
|
||||
if len(check_query) > 0:
|
||||
set_clause = ", ".join([f"{key} = '{value}'" for key, value in row_dict.items()])
|
||||
sqlitedb.update_data('trueandpredict', set_clause, where_condition=f"ds = '{row.ds}'")
|
||||
continue
|
||||
sqlitedb.insert_data('trueandpredict', tuple(row_dict.values()), columns=row_dict.keys())
|
||||
# # 将最新真实值保存到数据库
|
||||
# if not sqlitedb.check_table_exists('trueandpredict'):
|
||||
# first_row.to_sql('trueandpredict', sqlitedb.connection, index=False)
|
||||
# else:
|
||||
# for row in first_row.itertuples(index=False):
|
||||
# row_dict = row._asdict()
|
||||
# row_dict['ds'] = row_dict['ds'].strftime('%Y-%m-%d %H:%M:%S')
|
||||
# check_query = sqlitedb.select_data('trueandpredict', where_condition=f"ds = '{row.ds}'")
|
||||
# if len(check_query) > 0:
|
||||
# set_clause = ", ".join([f"{key} = '{value}'" for key, value in row_dict.items()])
|
||||
# sqlitedb.update_data('trueandpredict', set_clause, where_condition=f"ds = '{row.ds}'")
|
||||
# continue
|
||||
# sqlitedb.insert_data('trueandpredict', tuple(row_dict.values()), columns=row_dict.keys())
|
||||
|
||||
# 更新accuracy表的y值
|
||||
if not sqlitedb.check_table_exists('accuracy'):
|
||||
pass
|
||||
else:
|
||||
update_y = sqlitedb.select_data('accuracy',where_condition="y is null")
|
||||
if len(update_y) > 0:
|
||||
logger.info('更新accuracy表的y值')
|
||||
# 找到update_y 中ds且df中的y的行
|
||||
update_y = update_y[update_y['ds']<=end_time]
|
||||
logger.info(f'要更新y的信息：{update_y}')
|
||||
try:
|
||||
for row in update_y.itertuples(index=False):
|
||||
row_dict = row._asdict()
|
||||
yy = df[df['ds']==row_dict['ds']]['y'].values[0]
|
||||
LOW = df[df['ds']==row_dict['ds']]['Brentzdj'].values[0]
|
||||
HIGH = df[df['ds']==row_dict['ds']]['Brentzgj'].values[0]
|
||||
sqlitedb.update_data('accuracy', f"y = {yy},LOW_PRICE = {LOW},HIGH_PRICE = {HIGH}", where_condition=f"ds = '{row_dict['ds']}'")
|
||||
except Exception as e:
|
||||
logger.info(f'更新accuracy表的y值失败：{e}')
|
||||
# # 更新accuracy表的y值
|
||||
# if not sqlitedb.check_table_exists('accuracy'):
|
||||
# pass
|
||||
# else:
|
||||
# update_y = sqlitedb.select_data('accuracy',where_condition="y is null")
|
||||
# if len(update_y) > 0:
|
||||
# logger.info('更新accuracy表的y值')
|
||||
# # 找到update_y 中ds且df中的y的行
|
||||
# update_y = update_y[update_y['ds']<=end_time]
|
||||
# logger.info(f'要更新y的信息：{update_y}')
|
||||
# try:
|
||||
# for row in update_y.itertuples(index=False):
|
||||
# row_dict = row._asdict()
|
||||
# yy = df[df['ds']==row_dict['ds']]['y'].values[0]
|
||||
# LOW = df[df['ds']==row_dict['ds']]['Brentzdj'].values[0]
|
||||
# HIGH = df[df['ds']==row_dict['ds']]['Brentzgj'].values[0]
|
||||
# sqlitedb.update_data('accuracy', f"y = {yy},LOW_PRICE = {LOW},HIGH_PRICE = {HIGH}", where_condition=f"ds = '{row_dict['ds']}'")
|
||||
# except Exception as e:
|
||||
# logger.info(f'更新accuracy表的y值失败：{e}')
|
||||
|
||||
import datetime
|
||||
# 判断当前日期是不是周一
|
||||
is_weekday = datetime.datetime.now().weekday() == 0
|
||||
if is_weekday:
|
||||
logger.info('今天是周一，更新预测模型')
|
||||
# 计算最近60天预测残差最低的模型名称
|
||||
model_results = sqlitedb.select_data('trueandpredict', order_by="ds DESC", limit="60")
|
||||
# 删除空值率为40%以上的列
|
||||
if len(model_results) > 10:
|
||||
model_results = model_results.dropna(thresh=len(model_results)*0.6,axis=1)
|
||||
# 删除空行
|
||||
model_results = model_results.dropna()
|
||||
modelnames = model_results.columns.to_list()[2:]
|
||||
for col in model_results[modelnames].select_dtypes(include=['object']).columns:
|
||||
model_results[col] = model_results[col].astype(np.float32)
|
||||
# 计算每个预测值与真实值之间的偏差率
|
||||
for model in modelnames:
|
||||
model_results[f'{model}_abs_error_rate'] = abs(model_results['y'] - model_results[model]) / model_results['y']
|
||||
# 获取每行对应的最小偏差率值
|
||||
min_abs_error_rate_values = model_results.apply(lambda row: row[[f'{model}_abs_error_rate' for model in modelnames]].min(), axis=1)
|
||||
# 获取每行对应的最小偏差率值对应的列名
|
||||
min_abs_error_rate_column_name = model_results.apply(lambda row: row[[f'{model}_abs_error_rate' for model in modelnames]].idxmin(), axis=1)
|
||||
# 将列名索引转换为列名
|
||||
min_abs_error_rate_column_name = min_abs_error_rate_column_name.map(lambda x: x.split('_')[0])
|
||||
# 取出现次数最多的模型名称
|
||||
most_common_model = min_abs_error_rate_column_name.value_counts().idxmax()
|
||||
logger.info(f"最近60天预测残差最低的模型名称：{most_common_model}")
|
||||
# 保存结果到数据库
|
||||
if not sqlitedb.check_table_exists('most_model'):
|
||||
sqlitedb.create_table('most_model', columns="ds datetime, most_common_model TEXT")
|
||||
sqlitedb.insert_data('most_model', (datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S'), most_common_model,), columns=('ds', 'most_common_model',))
|
||||
# import datetime
|
||||
# # 判断当前日期是不是周一
|
||||
# is_weekday = datetime.datetime.now().weekday() == 0
|
||||
# if is_weekday:
|
||||
# logger.info('今天是周一，更新预测模型')
|
||||
# # 计算最近60天预测残差最低的模型名称
|
||||
# model_results = sqlitedb.select_data('trueandpredict', order_by="ds DESC", limit="60")
|
||||
# # 删除空值率为40%以上的列
|
||||
# if len(model_results) > 10:
|
||||
# model_results = model_results.dropna(thresh=len(model_results)*0.6,axis=1)
|
||||
# # 删除空行
|
||||
# model_results = model_results.dropna()
|
||||
# modelnames = model_results.columns.to_list()[2:]
|
||||
# for col in model_results[modelnames].select_dtypes(include=['object']).columns:
|
||||
# model_results[col] = model_results[col].astype(np.float32)
|
||||
# # 计算每个预测值与真实值之间的偏差率
|
||||
# for model in modelnames:
|
||||
# model_results[f'{model}_abs_error_rate'] = abs(model_results['y'] - model_results[model]) / model_results['y']
|
||||
# # 获取每行对应的最小偏差率值
|
||||
# min_abs_error_rate_values = model_results.apply(lambda row: row[[f'{model}_abs_error_rate' for model in modelnames]].min(), axis=1)
|
||||
# # 获取每行对应的最小偏差率值对应的列名
|
||||
# min_abs_error_rate_column_name = model_results.apply(lambda row: row[[f'{model}_abs_error_rate' for model in modelnames]].idxmin(), axis=1)
|
||||
# # 将列名索引转换为列名
|
||||
# min_abs_error_rate_column_name = min_abs_error_rate_column_name.map(lambda x: x.split('_')[0])
|
||||
# # 取出现次数最多的模型名称
|
||||
# most_common_model = min_abs_error_rate_column_name.value_counts().idxmax()
|
||||
# logger.info(f"最近60天预测残差最低的模型名称：{most_common_model}")
|
||||
# # 保存结果到数据库
|
||||
# if not sqlitedb.check_table_exists('most_model'):
|
||||
# sqlitedb.create_table('most_model', columns="ds datetime, most_common_model TEXT")
|
||||
# sqlitedb.insert_data('most_model', (datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S'), most_common_model,), columns=('ds', 'most_common_model',))
|
||||
|
||||
try:
|
||||
if is_weekday:
|
||||
# if True:
|
||||
logger.info('今天是周一，发送特征预警')
|
||||
# 上传预警信息到数据库
|
||||
warning_data_df = df_zhibiaoliebiao.copy()
|
||||
warning_data_df = warning_data_df[warning_data_df['停更周期']> 3 ][['指标名称', '指标id', '频度','更新周期','指标来源','最后更新时间','停更周期']]
|
||||
# 重命名列名
|
||||
warning_data_df = warning_data_df.rename(columns={'指标名称': 'INDICATOR_NAME', '指标id': 'INDICATOR_ID', '频度': 'FREQUENCY', '更新周期': 'UPDATE_FREQUENCY', '指标来源': 'DATA_SOURCE', '最后更新时间': 'LAST_UPDATE_DATE', '停更周期': 'UPDATE_SUSPENSION_CYCLE'})
|
||||
from sqlalchemy import create_engine
|
||||
import urllib
|
||||
global password
|
||||
if '@' in password:
|
||||
password = urllib.parse.quote_plus(password)
|
||||
# try:
|
||||
# if is_weekday:
|
||||
# # if True:
|
||||
# logger.info('今天是周一，发送特征预警')
|
||||
# # 上传预警信息到数据库
|
||||
# warning_data_df = df_zhibiaoliebiao.copy()
|
||||
# warning_data_df = warning_data_df[warning_data_df['停更周期']> 3 ][['指标名称', '指标id', '频度','更新周期','指标来源','最后更新时间','停更周期']]
|
||||
# # 重命名列名
|
||||
# warning_data_df = warning_data_df.rename(columns={'指标名称': 'INDICATOR_NAME', '指标id': 'INDICATOR_ID', '频度': 'FREQUENCY', '更新周期': 'UPDATE_FREQUENCY', '指标来源': 'DATA_SOURCE', '最后更新时间': 'LAST_UPDATE_DATE', '停更周期': 'UPDATE_SUSPENSION_CYCLE'})
|
||||
# from sqlalchemy import create_engine
|
||||
# import urllib
|
||||
# global password
|
||||
# if '@' in password:
|
||||
# password = urllib.parse.quote_plus(password)
|
||||
|
||||
engine = create_engine(f'mysql+pymysql://{dbusername}:{password}@{host}:{port}/{dbname}')
|
||||
warning_data_df['WARNING_DATE'] = datetime.date.today().strftime("%Y-%m-%d %H:%M:%S")
|
||||
warning_data_df['TENANT_CODE'] = 'T0004'
|
||||
# 插入数据之前查询表数据然后新增id列
|
||||
existing_data = pd.read_sql(f"SELECT * FROM {table_name}", engine)
|
||||
if not existing_data.empty:
|
||||
max_id = existing_data['ID'].astype(int).max()
|
||||
warning_data_df['ID'] = range(max_id + 1, max_id + 1 + len(warning_data_df))
|
||||
else:
|
||||
warning_data_df['ID'] = range(1, 1 + len(warning_data_df))
|
||||
warning_data_df.to_sql(table_name, con=engine, if_exists='append', index=False)
|
||||
if is_update_warning_data:
|
||||
upload_warning_info(len(warning_data_df))
|
||||
except:
|
||||
logger.info('上传预警信息到数据库失败')
|
||||
# engine = create_engine(f'mysql+pymysql://{dbusername}:{password}@{host}:{port}/{dbname}')
|
||||
# warning_data_df['WARNING_DATE'] = datetime.date.today().strftime("%Y-%m-%d %H:%M:%S")
|
||||
# warning_data_df['TENANT_CODE'] = 'T0004'
|
||||
# # 插入数据之前查询表数据然后新增id列
|
||||
# existing_data = pd.read_sql(f"SELECT * FROM {table_name}", engine)
|
||||
# if not existing_data.empty:
|
||||
# max_id = existing_data['ID'].astype(int).max()
|
||||
# warning_data_df['ID'] = range(max_id + 1, max_id + 1 + len(warning_data_df))
|
||||
# else:
|
||||
# warning_data_df['ID'] = range(1, 1 + len(warning_data_df))
|
||||
# warning_data_df.to_sql(table_name, con=engine, if_exists='append', index=False)
|
||||
# if is_update_warning_data:
|
||||
# upload_warning_info(len(warning_data_df))
|
||||
# except:
|
||||
# logger.info('上传预警信息到数据库失败')
|
||||
|
||||
if is_corr:
|
||||
df = corr_feature(df=df)
|
||||
# if is_corr:
|
||||
# df = corr_feature(df=df)
|
||||
|
||||
df1 = df.copy() # 备份一下，后面特征筛选完之后加入ds y 列用
|
||||
logger.info(f"开始训练模型...")
|
||||
row, col = df.shape
|
||||
# df1 = df.copy() # 备份一下，后面特征筛选完之后加入ds y 列用
|
||||
# logger.info(f"开始训练模型...")
|
||||
# row, col = df.shape
|
||||
|
||||
now = datetime.datetime.now().strftime('%Y%m%d%H%M%S')
|
||||
ex_Model(df,
|
||||
horizon=horizon,
|
||||
input_size=input_size,
|
||||
train_steps=train_steps,
|
||||
val_check_steps=val_check_steps,
|
||||
early_stop_patience_steps=early_stop_patience_steps,
|
||||
is_debug=is_debug,
|
||||
dataset=dataset,
|
||||
is_train=is_train,
|
||||
is_fivemodels=is_fivemodels,
|
||||
val_size=val_size,
|
||||
test_size=test_size,
|
||||
settings=settings,
|
||||
now=now,
|
||||
etadata=etadata,
|
||||
modelsindex=modelsindex,
|
||||
data=data,
|
||||
is_eta=is_eta,
|
||||
end_time=end_time,
|
||||
)
|
||||
# now = datetime.datetime.now().strftime('%Y%m%d%H%M%S')
|
||||
# ex_Model(df,
|
||||
# horizon=horizon,
|
||||
# input_size=input_size,
|
||||
# train_steps=train_steps,
|
||||
# val_check_steps=val_check_steps,
|
||||
# early_stop_patience_steps=early_stop_patience_steps,
|
||||
# is_debug=is_debug,
|
||||
# dataset=dataset,
|
||||
# is_train=is_train,
|
||||
# is_fivemodels=is_fivemodels,
|
||||
# val_size=val_size,
|
||||
# test_size=test_size,
|
||||
# settings=settings,
|
||||
# now=now,
|
||||
# etadata=etadata,
|
||||
# modelsindex=modelsindex,
|
||||
# data=data,
|
||||
# is_eta=is_eta,
|
||||
# end_time=end_time,
|
||||
# )
|
||||
|
||||
|
||||
logger.info('模型训练完成')
|
||||
# logger.info('模型训练完成')
|
||||
|
||||
logger.info('训练数据绘图ing')
|
||||
model_results3 = model_losss(sqlitedb,end_time=end_time)
|
||||
logger.info('训练数据绘图end')
|
||||
# logger.info('训练数据绘图ing')
|
||||
# model_results3 = model_losss(sqlitedb,end_time=end_time)
|
||||
# logger.info('训练数据绘图end')
|
||||
|
||||
# 模型报告
|
||||
logger.info('制作报告ing')
|
||||
|
||||
@ -945,14 +945,14 @@ def brent_export_pdf(num_indicators=475,num_models=21, num_dayindicator=202,inpu
|
||||
content.append(Graphs.draw_img(os.path.join(dataset,'历史价格-预测值.png')))
|
||||
# 波动率画图逻辑
|
||||
content.append(Graphs.draw_text('图示说明：'))
|
||||
content.append(Graphs.draw_text(' 确定波动率置信区间：设置残差置信阈值，以每周最佳模型为基准，选取在置信区间的预测值作为置信区间;'))
|
||||
content.append(Graphs.draw_text(' 确定置信区间：设置残差置信阈值，以每周最佳模型为基准，选取在置信区间的预测值作为置信区间;'))
|
||||
|
||||
|
||||
# 添加历史走势及预测价格的走势图片
|
||||
content.append(Graphs.draw_img(os.path.join(dataset,'历史价格-预测值1.png')))
|
||||
content.append(Graphs.draw_text('图示说明：'))
|
||||
content.append(Graphs.draw_text(' 确定波动率置信区间：使用模型评估指标MAE得到前十个模型，取平均值上下1.5作为价格波动置信区间;'))
|
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|
||||
content.append(Graphs.draw_text(' 确定置信区间：使用模型评估指标MAE得到前十个模型，取平均值上下1.5作为价格波动置信区间;'))
|
||||
|
||||
|
||||
# 取df中y列为空的行
|
||||
import pandas as pd
|
||||
|
||||
Loading…
Reference in New Issue
Block a user