添加预测结果更新到记录表
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232
config_jingbo.py
232
config_jingbo.py
@ -197,52 +197,124 @@ ClassifyId = 1214
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################################################################################################################ 变量定义--测试环境
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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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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://192.168.100.53:8080/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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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 = True # 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 = True # 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_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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# 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://192.168.100.53:8080/jingbo-dev/api/warehouse/dwDataItem/queryDataListItemNos"
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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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# login_data = {
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# "data": {
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# "data": {
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# "account": "api_test",
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# "account": "api-dev",
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# # "password": "MmVmNzNlOWI0MmY0ZDdjZGUwNzE3ZjFiMDJiZDZjZWU=", # Shihua@123456
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# "password": "ZTEwYWRjMzk0OWJhNTlhYmJlNTZlMDU3ZjIwZjg4M2U=",
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# "password": "ZTEwYWRjMzk0OWJhNTlhYmJlNTZlMDU3ZjIwZjg4M2U=", # 123456
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# "tenantHashCode": "1eb24ab5a6af12e30daf78af276664f1",
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# "tenantHashCode": "8a4577dbd919675758d57999a1e891fe",
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# "terminal": "API"
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# "terminal": "API"
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# },
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# },
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# "funcModule": "API",
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# "funcModule": "API",
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# "funcOperation": "获取token"
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# "funcOperation": "获取token"
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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":'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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# upload_data = {
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# "funcModule":'研究报告信息',
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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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# "funcOperation":'上传原油价格预测报告',
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# "data":{
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# "data":{
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# "ownerAccount":'arui', #报告所属用户账号
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# "ownerAccount":'rui.liu', #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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# "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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# "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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# "fileBase64": '' ,#文件内容base64
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# "categoryNo":'yyjgycbg', # 研究报告分类编码
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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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# "smartBusinessClassCode":'1', #分析报告分类编码
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# "reportEmployeeCode":"E40116", # 报告人
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# "reportEmployeeCode":"U270018", # 报告人
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# "reportDeptCode" :"D0044" ,# 报告部门
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# "reportDeptCode" :"D270001" ,# 报告部门
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# # "reportDeptCode" :"000001" ,# 报告部门
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# "productGroupCode":"RAW_MATERIAL" # 商品分类
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# "productGroupCode":"RAW_MATERIAL" # 商品分类
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# }
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# }
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# }
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# }
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@ -294,108 +366,6 @@ ClassifyId = 1214
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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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# 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://192.168.100.115:9090/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-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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"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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# 连接到数据库
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# 连接到数据库
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db_mysql = MySQLDB(host=host, user=dbusername, password=password, database=dbname)
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db_mysql = MySQLDB(host=host, user=dbusername, password=password, database=dbname)
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@ -448,12 +418,14 @@ settings = f'{input_size}-{horizon}-{train_steps}--{k}-{data_set}-{y}'
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now = datetime.datetime.now().strftime('%Y-%m-%d') # 获取当前日期时间
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now = datetime.datetime.now().strftime('%Y-%m-%d') # 获取当前日期时间
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reportname = f'Brent原油大模型预测--{now}.pdf' # 报告文件名
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reportname = f'Brent原油大模型预测--{now}.pdf' # 报告文件名
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reportname = reportname.replace(':', '-') # 替换冒号
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reportname = reportname.replace(':', '-') # 替换冒号
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if end_time == '':
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end_time = now
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### 邮件配置
|
### 邮件配置
|
||||||
username='1321340118@qq.com'
|
username='1321340118@qq.com'
|
||||||
passwd='wgczgyhtyyyyjghi'
|
passwd='wgczgyhtyyyyjghi'
|
||||||
# recv=['liurui_test@163.com','52585119@qq.com']
|
# recv=['liurui_test@163.com','52585119@qq.com']
|
||||||
recv=['liurui_test@163.com']
|
recv=['liurui_test@163.com','jin.wang@chambroad.com']
|
||||||
|
# recv=['liurui_test@163.com']
|
||||||
title='reportname'
|
title='reportname'
|
||||||
content='brent价格预测报告请看附件'
|
content='brent价格预测报告请看附件'
|
||||||
file=os.path.join(dataset,'reportname')
|
file=os.path.join(dataset,'reportname')
|
||||||
|
|||||||
@ -140,12 +140,13 @@ def get_head_auth_report():
|
|||||||
str: 如果登录成功，返回认证令牌；否则返回 None。
|
str: 如果登录成功，返回认证令牌；否则返回 None。
|
||||||
"""
|
"""
|
||||||
logger.info("获取token中...")
|
logger.info("获取token中...")
|
||||||
|
logger.info(f'url:{login_pushreport_url},login_data:{login_data}')
|
||||||
# 发送 POST 请求到登录 URL，携带登录数据
|
# 发送 POST 请求到登录 URL，携带登录数据
|
||||||
login_res = requests.post(url=login_pushreport_url, json=login_data, timeout=(3, 30))
|
login_res = requests.post(url=login_pushreport_url, json=login_data, timeout=(3, 30))
|
||||||
|
|
||||||
# 将响应内容转换为 JSON 格式
|
# 将响应内容转换为 JSON 格式
|
||||||
text = json.loads(login_res.text)
|
text = json.loads(login_res.text)
|
||||||
|
logger.info(f'token接口响应：{text}')
|
||||||
# 如果响应状态为成功
|
# 如果响应状态为成功
|
||||||
if text["status"]:
|
if text["status"]:
|
||||||
# 从响应数据中获取认证令牌
|
# 从响应数据中获取认证令牌
|
||||||
@ -1662,6 +1663,7 @@ def get_market_data(end_time,df):
|
|||||||
logger.info('获取数据中...')
|
logger.info('获取数据中...')
|
||||||
items_res = requests.post(url=query_data_list_item_nos_url, headers=headers, json=query_data_list_item_nos_data, timeout=(3, 35))
|
items_res = requests.post(url=query_data_list_item_nos_url, headers=headers, json=query_data_list_item_nos_data, timeout=(3, 35))
|
||||||
json_data = json.loads(items_res.text)
|
json_data = json.loads(items_res.text)
|
||||||
|
logger.info(f"获取到的数据:{json_data}")
|
||||||
df3 = pd.DataFrame(json_data['data'])
|
df3 = pd.DataFrame(json_data['data'])
|
||||||
# 按照dataItemNo 分组 得到多个dataframe ，最后根据dataDate merge 成一个dataframe
|
# 按照dataItemNo 分组 得到多个dataframe ，最后根据dataDate merge 成一个dataframe
|
||||||
df2 = pd.DataFrame()
|
df2 = pd.DataFrame()
|
||||||
|
|||||||
288
main_yongan.py
Normal file
288
main_yongan.py
Normal file
@ -0,0 +1,288 @@
|
|||||||
|
# 读取配置
|
||||||
|
from lib.dataread import *
|
||||||
|
from lib.tools import SendMail,exception_logger
|
||||||
|
from models.nerulforcastmodels import ex_Model,model_losss,model_losss_juxiting,brent_export_pdf,tansuanli_export_pdf,pp_export_pdf,model_losss_juxiting
|
||||||
|
|
||||||
|
import glob
|
||||||
|
import torch
|
||||||
|
torch.set_float32_matmul_precision("high")
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
def predict_main():
|
||||||
|
"""
|
||||||
|
主预测函数，用于从 ETA 获取数据、处理数据、训练模型并进行预测。
|
||||||
|
|
||||||
|
参数:
|
||||||
|
signature (BinanceAPI): Binance API 实例。
|
||||||
|
etadata (EtaReader): ETA 数据读取器实例。
|
||||||
|
is_eta (bool): 是否从 ETA 获取数据。
|
||||||
|
data_set (str): 数据集名称。
|
||||||
|
dataset (str): 数据集路径。
|
||||||
|
add_kdj (bool): 是否添加 KDJ 指标。
|
||||||
|
is_timefurture (bool): 是否添加时间衍生特征。
|
||||||
|
end_time (str): 结束时间。
|
||||||
|
is_edbnamelist (bool): 是否使用 EDB 名称列表。
|
||||||
|
edbnamelist (list): EDB 名称列表。
|
||||||
|
y (str): 预测目标列名。
|
||||||
|
sqlitedb (SQLiteDB): SQLite 数据库实例。
|
||||||
|
is_corr (bool): 是否进行相关性分析。
|
||||||
|
horizon (int): 预测时域。
|
||||||
|
input_size (int): 输入数据大小。
|
||||||
|
train_steps (int): 训练步数。
|
||||||
|
val_check_steps (int): 验证检查步数。
|
||||||
|
early_stop_patience_steps (int): 早停耐心步数。
|
||||||
|
is_debug (bool): 是否调试模式。
|
||||||
|
dataset (str): 数据集名称。
|
||||||
|
is_train (bool): 是否训练模型。
|
||||||
|
is_fivemodels (bool): 是否使用五个模型。
|
||||||
|
val_size (float): 验证集大小。
|
||||||
|
test_size (float): 测试集大小。
|
||||||
|
settings (dict): 模型设置。
|
||||||
|
now (str): 当前时间。
|
||||||
|
etadata (EtaReader): ETA 数据读取器实例。
|
||||||
|
modelsindex (list): 模型索引列表。
|
||||||
|
data (str): 数据类型。
|
||||||
|
is_eta (bool): 是否从 ETA 获取数据。
|
||||||
|
|
||||||
|
返回:
|
||||||
|
None
|
||||||
|
"""
|
||||||
|
# global end_time
|
||||||
|
# signature = BinanceAPI(APPID, SECRET)
|
||||||
|
# etadata = EtaReader(signature=signature,
|
||||||
|
# classifylisturl=classifylisturl,
|
||||||
|
# classifyidlisturl=classifyidlisturl,
|
||||||
|
# edbcodedataurl=edbcodedataurl,
|
||||||
|
# edbcodelist=edbcodelist,
|
||||||
|
# edbdatapushurl=edbdatapushurl,
|
||||||
|
# edbdeleteurl=edbdeleteurl,
|
||||||
|
# edbbusinessurl=edbbusinessurl
|
||||||
|
# )
|
||||||
|
# # 获取数据
|
||||||
|
# if is_eta:
|
||||||
|
# logger.info('从eta获取数据...')
|
||||||
|
# signature = BinanceAPI(APPID, SECRET)
|
||||||
|
# etadata = EtaReader(signature=signature,
|
||||||
|
# classifylisturl=classifylisturl,
|
||||||
|
# classifyidlisturl=classifyidlisturl,
|
||||||
|
# edbcodedataurl=edbcodedataurl,
|
||||||
|
# edbcodelist=edbcodelist,
|
||||||
|
# edbdatapushurl=edbdatapushurl,
|
||||||
|
# edbdeleteurl=edbdeleteurl,
|
||||||
|
# edbbusinessurl=edbbusinessurl,
|
||||||
|
# )
|
||||||
|
# df_zhibiaoshuju, df_zhibiaoliebiao = etadata.get_eta_api_yuanyou_data(data_set=data_set, dataset=dataset) # 原始数据，未处理
|
||||||
|
|
||||||
|
# if is_market:
|
||||||
|
# logger.info('从市场信息平台获取数据...')
|
||||||
|
# try:
|
||||||
|
# df_zhibiaoshuju = get_market_data(end_time,df_zhibiaoshuju)
|
||||||
|
# except :
|
||||||
|
# logger.info('从市场信息平台获取数据失败')
|
||||||
|
|
||||||
|
# # 保存到xlsx文件的sheet表
|
||||||
|
# with pd.ExcelWriter(os.path.join(dataset,data_set)) as file:
|
||||||
|
# df_zhibiaoshuju.to_excel(file, sheet_name='指标数据', index=False)
|
||||||
|
# df_zhibiaoliebiao.to_excel(file, sheet_name='指标列表', index=False)
|
||||||
|
|
||||||
|
|
||||||
|
# # 数据处理
|
||||||
|
# df = datachuli(df_zhibiaoshuju, df_zhibiaoliebiao, y=y, dataset=dataset, add_kdj=add_kdj, is_timefurture=is_timefurture,
|
||||||
|
# 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
|
||||||
|
|
||||||
|
# # 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
|
||||||
|
|
||||||
|
# # 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}')
|
||||||
|
|
||||||
|
# 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)
|
||||||
|
|
||||||
|
# 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)
|
||||||
|
|
||||||
|
# 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,
|
||||||
|
# )
|
||||||
|
|
||||||
|
|
||||||
|
# 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')
|
||||||
|
|
||||||
|
# 模型报告
|
||||||
|
logger.info('制作报告ing')
|
||||||
|
title = f'{settings}--{end_time}-预测报告' # 报告标题
|
||||||
|
reportname = f'Brent原油大模型预测--{end_time}.pdf' # 报告文件名
|
||||||
|
reportname = reportname.replace(':', '-') # 替换冒号
|
||||||
|
brent_export_pdf(dataset=dataset,num_models = 5 if is_fivemodels else 22,time=end_time,
|
||||||
|
reportname=reportname,sqlitedb=sqlitedb),
|
||||||
|
|
||||||
|
logger.info('制作报告end')
|
||||||
|
logger.info('模型训练完成')
|
||||||
|
|
||||||
|
# # LSTM 单变量模型
|
||||||
|
# ex_Lstm(df,input_seq_len=input_size,output_seq_len=horizon,is_debug=is_debug,dataset=dataset)
|
||||||
|
|
||||||
|
# # lstm 多变量模型
|
||||||
|
# ex_Lstm_M(df,n_days=input_size,out_days=horizon,is_debug=is_debug,datasetpath=dataset)
|
||||||
|
|
||||||
|
# # GRU 模型
|
||||||
|
# # ex_GRU(df)
|
||||||
|
|
||||||
|
# 发送邮件
|
||||||
|
# m = SendMail(
|
||||||
|
# username=username,
|
||||||
|
# passwd=passwd,
|
||||||
|
# recv=recv,
|
||||||
|
# title=title,
|
||||||
|
# content=content,
|
||||||
|
# file=max(glob.glob(os.path.join(dataset,'*.pdf')), key=os.path.getctime),
|
||||||
|
# ssl=ssl,
|
||||||
|
# )
|
||||||
|
# m.send_mail()
|
||||||
|
|
||||||
|
|
||||||
|
if __name__ == '__main__':
|
||||||
|
global end_time
|
||||||
|
is_on = True
|
||||||
|
# 遍历2024-11-25 到 2024-12-3 之间的工作日日期
|
||||||
|
for i_time in pd.date_range('2024-12-27', '2024-12-28', freq='B'):
|
||||||
|
end_time = i_time.strftime('%Y-%m-%d')
|
||||||
|
predict_main()
|
||||||
390
main_yuanyou.py
390
main_yuanyou.py
@ -48,203 +48,203 @@ def predict_main():
|
|||||||
返回:
|
返回:
|
||||||
None
|
None
|
||||||
"""
|
"""
|
||||||
# global end_time
|
global end_time
|
||||||
# signature = BinanceAPI(APPID, SECRET)
|
signature = BinanceAPI(APPID, SECRET)
|
||||||
# etadata = EtaReader(signature=signature,
|
etadata = EtaReader(signature=signature,
|
||||||
# classifylisturl=classifylisturl,
|
classifylisturl=classifylisturl,
|
||||||
# classifyidlisturl=classifyidlisturl,
|
classifyidlisturl=classifyidlisturl,
|
||||||
# edbcodedataurl=edbcodedataurl,
|
edbcodedataurl=edbcodedataurl,
|
||||||
# edbcodelist=edbcodelist,
|
edbcodelist=edbcodelist,
|
||||||
# edbdatapushurl=edbdatapushurl,
|
edbdatapushurl=edbdatapushurl,
|
||||||
# edbdeleteurl=edbdeleteurl,
|
edbdeleteurl=edbdeleteurl,
|
||||||
# edbbusinessurl=edbbusinessurl
|
edbbusinessurl=edbbusinessurl
|
||||||
# )
|
)
|
||||||
# # 获取数据
|
# 获取数据
|
||||||
# if is_eta:
|
if is_eta:
|
||||||
# logger.info('从eta获取数据...')
|
logger.info('从eta获取数据...')
|
||||||
# signature = BinanceAPI(APPID, SECRET)
|
signature = BinanceAPI(APPID, SECRET)
|
||||||
# etadata = EtaReader(signature=signature,
|
etadata = EtaReader(signature=signature,
|
||||||
# classifylisturl=classifylisturl,
|
classifylisturl=classifylisturl,
|
||||||
# classifyidlisturl=classifyidlisturl,
|
classifyidlisturl=classifyidlisturl,
|
||||||
# edbcodedataurl=edbcodedataurl,
|
edbcodedataurl=edbcodedataurl,
|
||||||
# edbcodelist=edbcodelist,
|
edbcodelist=edbcodelist,
|
||||||
# edbdatapushurl=edbdatapushurl,
|
edbdatapushurl=edbdatapushurl,
|
||||||
# edbdeleteurl=edbdeleteurl,
|
edbdeleteurl=edbdeleteurl,
|
||||||
# edbbusinessurl=edbbusinessurl,
|
edbbusinessurl=edbbusinessurl,
|
||||||
# )
|
)
|
||||||
# df_zhibiaoshuju, df_zhibiaoliebiao = etadata.get_eta_api_yuanyou_data(data_set=data_set, dataset=dataset) # 原始数据，未处理
|
df_zhibiaoshuju, df_zhibiaoliebiao = etadata.get_eta_api_yuanyou_data(data_set=data_set, dataset=dataset) # 原始数据，未处理
|
||||||
|
|
||||||
# if is_market:
|
if is_market:
|
||||||
# logger.info('从市场信息平台获取数据...')
|
logger.info('从市场信息平台获取数据...')
|
||||||
# try:
|
try:
|
||||||
# df_zhibiaoshuju = get_market_data(end_time,df_zhibiaoshuju)
|
df_zhibiaoshuju = get_market_data(end_time,df_zhibiaoshuju)
|
||||||
# except :
|
except :
|
||||||
# logger.info('从市场信息平台获取数据失败')
|
logger.info('从市场信息平台获取数据失败')
|
||||||
|
|
||||||
# # 保存到xlsx文件的sheet表
|
# 保存到xlsx文件的sheet表
|
||||||
# with pd.ExcelWriter(os.path.join(dataset,data_set)) as file:
|
with pd.ExcelWriter(os.path.join(dataset,data_set)) as file:
|
||||||
# df_zhibiaoshuju.to_excel(file, sheet_name='指标数据', index=False)
|
df_zhibiaoshuju.to_excel(file, sheet_name='指标数据', index=False)
|
||||||
# df_zhibiaoliebiao.to_excel(file, sheet_name='指标列表', index=False)
|
df_zhibiaoliebiao.to_excel(file, sheet_name='指标列表', index=False)
|
||||||
|
|
||||||
|
|
||||||
# # 数据处理
|
# 数据处理
|
||||||
# df = datachuli(df_zhibiaoshuju, df_zhibiaoliebiao, y=y, dataset=dataset, add_kdj=add_kdj, is_timefurture=is_timefurture,
|
df = datachuli(df_zhibiaoshuju, df_zhibiaoliebiao, y=y, dataset=dataset, add_kdj=add_kdj, is_timefurture=is_timefurture,
|
||||||
# end_time=end_time)
|
end_time=end_time)
|
||||||
|
|
||||||
# else:
|
else:
|
||||||
# # 读取数据
|
# 读取数据
|
||||||
# logger.info('读取本地数据：' + os.path.join(dataset, data_set))
|
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,
|
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
|
is_timefurture=is_timefurture, end_time=end_time) # 原始数据，未处理
|
||||||
|
|
||||||
# # 更改预测列名称
|
# 更改预测列名称
|
||||||
# df.rename(columns={y: 'y'}, inplace=True)
|
df.rename(columns={y: 'y'}, inplace=True)
|
||||||
|
|
||||||
# if is_edbnamelist:
|
if is_edbnamelist:
|
||||||
# df = df[edbnamelist]
|
df = df[edbnamelist]
|
||||||
# df.to_csv(os.path.join(dataset, '指标数据.csv'), index=False)
|
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
|
# 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
|
# 取第一行数据存储到数据库中
|
||||||
# first_row = df[['ds', 'y']].tail(1)
|
first_row = df[['ds', 'y']].tail(1)
|
||||||
# print(first_row['ds'].values[0])
|
print(first_row['ds'].values[0])
|
||||||
# print(first_row['y'].values[0])
|
print(first_row['y'].values[0])
|
||||||
# # 判断y的类型是否为float
|
# 判断y的类型是否为float
|
||||||
# if not isinstance(first_row['y'].values[0], 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')
|
logger.info(f'{end_time}预测目标数据为空，跳过')
|
||||||
# return None
|
return None
|
||||||
|
|
||||||
# # 将最新真实值保存到数据库
|
# 将最新真实值保存到数据库
|
||||||
# if not sqlitedb.check_table_exists('trueandpredict'):
|
if not sqlitedb.check_table_exists('trueandpredict'):
|
||||||
# first_row.to_sql('trueandpredict', sqlitedb.connection, index=False)
|
first_row.to_sql('trueandpredict', sqlitedb.connection, index=False)
|
||||||
# else:
|
else:
|
||||||
# for row in first_row.itertuples(index=False):
|
for row in first_row.itertuples(index=False):
|
||||||
# row_dict = row._asdict()
|
row_dict = row._asdict()
|
||||||
# row_dict['ds'] = row_dict['ds'].strftime('%Y-%m-%d %H:%M:%S')
|
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}'")
|
check_query = sqlitedb.select_data('trueandpredict', where_condition=f"ds = '{row.ds}'")
|
||||||
# if len(check_query) > 0:
|
if len(check_query) > 0:
|
||||||
# set_clause = ", ".join([f"{key} = '{value}'" for key, value in row_dict.items()])
|
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}'")
|
sqlitedb.update_data('trueandpredict', set_clause, where_condition=f"ds = '{row.ds}'")
|
||||||
# continue
|
continue
|
||||||
# sqlitedb.insert_data('trueandpredict', tuple(row_dict.values()), columns=row_dict.keys())
|
sqlitedb.insert_data('trueandpredict', tuple(row_dict.values()), columns=row_dict.keys())
|
||||||
|
|
||||||
# # 更新accuracy表的y值
|
# 更新accuracy表的y值
|
||||||
# if not sqlitedb.check_table_exists('accuracy'):
|
if not sqlitedb.check_table_exists('accuracy'):
|
||||||
# pass
|
pass
|
||||||
# else:
|
else:
|
||||||
# update_y = sqlitedb.select_data('accuracy',where_condition="y is null")
|
update_y = sqlitedb.select_data('accuracy',where_condition="y is null")
|
||||||
# if len(update_y) > 0:
|
if len(update_y) > 0:
|
||||||
# logger.info('更新accuracy表的y值')
|
logger.info('更新accuracy表的y值')
|
||||||
# # 找到update_y 中ds且df中的y的行
|
# 找到update_y 中ds且df中的y的行
|
||||||
# update_y = update_y[update_y['ds']<=end_time]
|
update_y = update_y[update_y['ds']<=end_time]
|
||||||
# logger.info(f'要更新y的信息：{update_y}')
|
logger.info(f'要更新y的信息：{update_y}')
|
||||||
# try:
|
try:
|
||||||
# for row in update_y.itertuples(index=False):
|
for row in update_y.itertuples(index=False):
|
||||||
# row_dict = row._asdict()
|
row_dict = row._asdict()
|
||||||
# yy = df[df['ds']==row_dict['ds']]['y'].values[0]
|
yy = df[df['ds']==row_dict['ds']]['y'].values[0]
|
||||||
# LOW = df[df['ds']==row_dict['ds']]['Brentzdj'].values[0]
|
LOW = df[df['ds']==row_dict['ds']]['Brentzdj'].values[0]
|
||||||
# HIGH = df[df['ds']==row_dict['ds']]['Brentzgj'].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']}'")
|
sqlitedb.update_data('accuracy', f"y = {yy},LOW_PRICE = {LOW},HIGH_PRICE = {HIGH}", where_condition=f"ds = '{row_dict['ds']}'")
|
||||||
# except Exception as e:
|
except Exception as e:
|
||||||
# logger.info(f'更新accuracy表的y值失败：{e}')
|
logger.info(f'更新accuracy表的y值失败：{e}')
|
||||||
|
|
||||||
# import datetime
|
import datetime
|
||||||
# # 判断当前日期是不是周一
|
# 判断当前日期是不是周一
|
||||||
# is_weekday = datetime.datetime.now().weekday() == 0
|
is_weekday = datetime.datetime.now().weekday() == 0
|
||||||
# if is_weekday:
|
if is_weekday:
|
||||||
# logger.info('今天是周一，更新预测模型')
|
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
|
# 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")
|
model_results = sqlitedb.select_data('trueandpredict', order_by="ds DESC", limit="60")
|
||||||
# # 删除空值率为40%以上的列
|
# 删除空值率为90%以上的列
|
||||||
# if len(model_results) > 10:
|
if len(model_results) > 10:
|
||||||
# model_results = model_results.dropna(thresh=len(model_results)*0.6,axis=1)
|
model_results = model_results.dropna(thresh=len(model_results)*0.1,axis=1)
|
||||||
# # 删除空行
|
# 删除空行
|
||||||
# model_results = model_results.dropna()
|
model_results = model_results.dropna()
|
||||||
# modelnames = model_results.columns.to_list()[2:]
|
modelnames = model_results.columns.to_list()[2:-1]
|
||||||
# for col in model_results[modelnames].select_dtypes(include=['object']).columns:
|
for col in model_results[modelnames].select_dtypes(include=['object']).columns:
|
||||||
# model_results[col] = model_results[col].astype(np.float32)
|
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
|
# 计算每个预测值与真实值之间的偏差率
|
||||||
# for model in modelnames:
|
for model in modelnames:
|
||||||
# model_results[f'{model}_abs_error_rate'] = abs(model_results['y'] - model_results[model]) / model_results['y']
|
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
|
# 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)
|
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
|
# 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)
|
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
|
# 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])
|
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
|
# 取出现次数最多的模型名称
|
||||||
# most_common_model = min_abs_error_rate_column_name.value_counts().idxmax()
|
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}")
|
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
|
# 保存结果到数据库
|
||||||
# if not sqlitedb.check_table_exists('most_model'):
|
if not sqlitedb.check_table_exists('most_model'):
|
||||||
# sqlitedb.create_table('most_model', columns="ds datetime, most_common_model TEXT")
|
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',))
|
sqlitedb.insert_data('most_model', (datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S'), most_common_model,), columns=('ds', 'most_common_model',))
|
||||||
|
|
||||||
# try:
|
try:
|
||||||
# if is_weekday:
|
if is_weekday:
|
||||||
# # if True:
|
# if True:
|
||||||
# logger.info('今天是周一，发送特征预警')
|
logger.info('今天是周一，发送特征预警')
|
||||||
# # 上传预警信息到数据库
|
# 上传预警信息到数据库
|
||||||
# warning_data_df = df_zhibiaoliebiao.copy()
|
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']]
|
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
|
# 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'})
|
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
|
from sqlalchemy import create_engine
|
||||||
# import urllib
|
import urllib
|
||||||
# global password
|
global password
|
||||||
# if '@' in password:
|
if '@' in password:
|
||||||
# password = urllib.parse.quote_plus(password)
|
password = urllib.parse.quote_plus(password)
|
||||||
|
|
||||||
# engine = create_engine(f'mysql+pymysql://{dbusername}:{password}@{host}:{port}/{dbname}')
|
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['WARNING_DATE'] = datetime.date.today().strftime("%Y-%m-%d %H:%M:%S")
|
||||||
# warning_data_df['TENANT_CODE'] = 'T0004'
|
warning_data_df['TENANT_CODE'] = 'T0004'
|
||||||
# # 插入数据之前查询表数据然后新增id列
|
# 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)
|
existing_data = pd.read_sql(f"SELECT * FROM {table_name}", engine)
|
||||||
# if not existing_data.empty:
|
if not existing_data.empty:
|
||||||
# max_id = existing_data['ID'].astype(int).max()
|
max_id = existing_data['ID'].astype(int).max()
|
||||||
# warning_data_df['ID'] = range(max_id + 1, max_id + 1 + len(warning_data_df))
|
warning_data_df['ID'] = range(max_id + 1, max_id + 1 + len(warning_data_df))
|
||||||
# else:
|
else:
|
||||||
# warning_data_df['ID'] = range(1, 1 + len(warning_data_df))
|
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)
|
warning_data_df.to_sql(table_name, con=engine, if_exists='append', index=False)
|
||||||
# if is_update_warning_data:
|
if is_update_warning_data:
|
||||||
# upload_warning_info(len(warning_data_df))
|
upload_warning_info(len(warning_data_df))
|
||||||
# except:
|
except:
|
||||||
# logger.info('上传预警信息到数据库失败')
|
logger.info('上传预警信息到数据库失败')
|
||||||
|
|
||||||
# if is_corr:
|
if is_corr:
|
||||||
# df = corr_feature(df=df)
|
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
|
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...")
|
logger.info(f"开始训练模型...")
|
||||||
# row, col = df.shape
|
row, col = df.shape
|
||||||
|
|
||||||
# now = datetime.datetime.now().strftime('%Y%m%d%H%M%S')
|
now = datetime.datetime.now().strftime('%Y%m%d%H%M%S')
|
||||||
# ex_Model(df,
|
ex_Model(df,
|
||||||
# horizon=horizon,
|
horizon=horizon,
|
||||||
# input_size=input_size,
|
input_size=input_size,
|
||||||
# train_steps=train_steps,
|
train_steps=train_steps,
|
||||||
# val_check_steps=val_check_steps,
|
val_check_steps=val_check_steps,
|
||||||
# early_stop_patience_steps=early_stop_patience_steps,
|
early_stop_patience_steps=early_stop_patience_steps,
|
||||||
# is_debug=is_debug,
|
is_debug=is_debug,
|
||||||
# dataset=dataset,
|
dataset=dataset,
|
||||||
# is_train=is_train,
|
is_train=is_train,
|
||||||
# is_fivemodels=is_fivemodels,
|
is_fivemodels=is_fivemodels,
|
||||||
# val_size=val_size,
|
val_size=val_size,
|
||||||
# test_size=test_size,
|
test_size=test_size,
|
||||||
# settings=settings,
|
settings=settings,
|
||||||
# now=now,
|
now=now,
|
||||||
# etadata=etadata,
|
etadata=etadata,
|
||||||
# modelsindex=modelsindex,
|
modelsindex=modelsindex,
|
||||||
# data=data,
|
data=data,
|
||||||
# is_eta=is_eta,
|
is_eta=is_eta,
|
||||||
# end_time=end_time,
|
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')
|
logger.info('训练数据绘图ing')
|
||||||
# model_results3 = model_losss(sqlitedb,end_time=end_time)
|
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end')
|
||||||
|
|
||||||
# 模型报告
|
# 模型报告
|
||||||
logger.info('制作报告ing')
|
logger.info('制作报告ing')
|
||||||
@ -267,22 +267,24 @@ def predict_main():
|
|||||||
# # ex_GRU(df)
|
# # ex_GRU(df)
|
||||||
|
|
||||||
# 发送邮件
|
# 发送邮件
|
||||||
# m = SendMail(
|
m = SendMail(
|
||||||
# username=username,
|
username=username,
|
||||||
# passwd=passwd,
|
passwd=passwd,
|
||||||
# recv=recv,
|
recv=recv,
|
||||||
# title=title,
|
title=title,
|
||||||
# content=content,
|
content=content,
|
||||||
# file=max(glob.glob(os.path.join(dataset,'*.pdf')), key=os.path.getctime),
|
file=max(glob.glob(os.path.join(dataset,'*.pdf')), key=os.path.getctime),
|
||||||
# ssl=ssl,
|
ssl=ssl,
|
||||||
# )
|
)
|
||||||
# m.send_mail()
|
m.send_mail()
|
||||||
|
|
||||||
|
|
||||||
if __name__ == '__main__':
|
if __name__ == '__main__':
|
||||||
global end_time
|
# global end_time
|
||||||
is_on = True
|
# is_on = True
|
||||||
# 遍历2024-11-25 到 2024-12-3 之间的工作日日期
|
# # 遍历2024-11-25 到 2024-12-3 之间的工作日日期
|
||||||
for i_time in pd.date_range('2024-12-27', '2024-12-28', freq='B'):
|
# for i_time in pd.date_range('2024-12-27', '2024-12-28', freq='B'):
|
||||||
end_time = i_time.strftime('%Y-%m-%d')
|
# end_time = i_time.strftime('%Y-%m-%d')
|
||||||
|
# predict_main()
|
||||||
|
|
||||||
predict_main()
|
predict_main()
|
||||||
@ -317,6 +317,28 @@ def model_losss(sqlitedb,end_time):
|
|||||||
except ValueError :
|
except ValueError :
|
||||||
df_predict['ds'] = pd.to_datetime(df_predict['ds'],format=r'%Y/%m/%d')
|
df_predict['ds'] = pd.to_datetime(df_predict['ds'],format=r'%Y/%m/%d')
|
||||||
|
|
||||||
|
def first_row_to_database(df):
|
||||||
|
# # 取第一行数据存储到数据库中
|
||||||
|
first_row = df.head(1)
|
||||||
|
first_row['ds'] = first_row['ds'].dt.strftime('%Y-%m-%d 00:00:00')
|
||||||
|
# 将预测结果保存到数据库
|
||||||
|
if not sqlitedb.check_table_exists('trueandpredict'):
|
||||||
|
first_row.to_sql('trueandpredict',sqlitedb.connection,index=False)
|
||||||
|
else:
|
||||||
|
for col in first_row.columns:
|
||||||
|
sqlitedb.add_column_if_not_exists('trueandpredict',col,'TEXT')
|
||||||
|
for row in first_row.itertuples(index=False):
|
||||||
|
row_dict = row._asdict()
|
||||||
|
columns=row_dict.keys()
|
||||||
|
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=columns)
|
||||||
|
|
||||||
|
first_row_to_database(df_predict)
|
||||||
|
|
||||||
df_combined3 = pd.concat([df_combined3, df_predict]).reset_index(drop=True)
|
df_combined3 = pd.concat([df_combined3, df_predict]).reset_index(drop=True)
|
||||||
|
|
||||||
# 计算每个模型与最佳模型的绝对误差比例，根据设置的阈值rote筛选预测值显示最大最小值
|
# 计算每个模型与最佳模型的绝对误差比例，根据设置的阈值rote筛选预测值显示最大最小值
|
||||||
@ -1018,6 +1040,7 @@ def brent_export_pdf(num_indicators=475,num_models=21, num_dayindicator=202,inpu
|
|||||||
df4 = sqlitedb.select_data('accuracy_rote',order_by='结束日期 desc',limit=1)
|
df4 = sqlitedb.select_data('accuracy_rote',order_by='结束日期 desc',limit=1)
|
||||||
df4 = df4.T
|
df4 = df4.T
|
||||||
df4 = df4.reset_index()
|
df4 = df4.reset_index()
|
||||||
|
df4 = df4.T
|
||||||
data = df4.values.tolist()
|
data = df4.values.tolist()
|
||||||
col_width = 500/len(df4.columns)
|
col_width = 500/len(df4.columns)
|
||||||
content.append(Graphs.draw_table(col_width,*data))
|
content.append(Graphs.draw_table(col_width,*data))
|
||||||
@ -1133,7 +1156,7 @@ def brent_export_pdf(num_indicators=475,num_models=21, num_dayindicator=202,inpu
|
|||||||
eval_df = eval_df.T
|
eval_df = eval_df.T
|
||||||
# df重置索引
|
# df重置索引
|
||||||
eval_df = eval_df.reset_index()
|
eval_df = eval_df.reset_index()
|
||||||
# eval_df = eval_df.T
|
eval_df = eval_df.T
|
||||||
# # 添加表格
|
# # 添加表格
|
||||||
data = eval_df.values.tolist()
|
data = eval_df.values.tolist()
|
||||||
col_width = 500/len(eval_df.columns)
|
col_width = 500/len(eval_df.columns)
|
||||||
|
|||||||
Loading…
Reference in New Issue
Block a user