配置分离
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workpc
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211
config_jingbo.py
211
config_jingbo.py
@ -105,216 +105,39 @@ data = {
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ClassifyId = 1214
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################################################################################################################ 变量定义--线上环境
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# server_host = '10.200.32.39'
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# login_pushreport_url = "http://10.200.32.39/jingbo-api/api/server/login"
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# upload_url = "http://10.200.32.39/jingbo-api/api/analysis/reportInfo/researchUploadReportSave"
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# upload_warning_url = "http://10.200.32.39/jingbo-api/api/basicBuiness/crudeOilWarning/save"
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# query_data_list_item_nos_url = "http://10.200.32.39/jingbo-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": "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":'27663', #报告所属用户账号 27663 - 刘小朋
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# "reportType":'OIL_PRICE_FORECAST', # 报告类型，固定为OIL_PRICE_FORECAST
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# "fileName": '', #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":"E40482" ,# 报告人 E40482 - 管理员 0000027663 - 刘小朋
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# "reportDeptCode" :"002000621000", # 报告部门 - 002000621000 SH期货研究部
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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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# ## 生产环境数据库
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# # host = 'rm-2zehj3r1n60ttz9x5.mysql.rds.aliyuncs.com'
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# # port = 3306
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# # dbusername ='jingbo'
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# # password = 'shihua@123'
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# # dbname = 'jingbo'
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# # table_name = 'v_tbl_crude_oil_warning'
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# ## 预生产环境
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# host = 'rm-2zehj3r1n60ttz9x5ko.mysql.rds.aliyuncs.com'
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# port = 3306
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# dbusername ='jingbo'
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# password = 'shihua@123'
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# dbname = 'jingbo-test'
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# table_name = 'v_tbl_crude_oil_warning'
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# # 线上开关备份
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# is_train = True # 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_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 = True # 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 if datetime.datetime.now().weekday() == 1 else 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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################################################################################################################ 变量定义--测试环境
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# server_host = '192.168.100.53'
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# login_pushreport_url = f"http://{server_host}:8080/jingbo-dev/api/server/login"
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# upload_url = f"http://{server_host}:8080/jingbo-dev/api/analysis/reportInfo/researchUploadReportSave"
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# upload_warning_url = f"http://{server_host}:8080/jingbo-dev/api/basicBuiness/crudeOilWarning/save"
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# query_data_list_item_nos_url = f"http://{server_host}: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 = 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 = 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_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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############################################################################################################### 变量定义--测试环境
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server_host = '192.168.100.53'
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login_pushreport_url = f"http://{server_host}:8080/jingbo-dev/api/server/login"
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upload_url = f"http://{server_host}:8080/jingbo-dev/api/analysis/reportInfo/researchUploadReportSave"
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upload_warning_url = f"http://{server_host}:8080/jingbo-dev/api/basicBuiness/crudeOilWarning/save"
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query_data_list_item_nos_url = f"http://{server_host}:8080/jingbo-dev/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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"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":'rui.liu', #报告所属用户账号
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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":"U270018", # 报告人
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"reportDeptCode" :"D270001" ,# 报告部门
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# "reportDeptCode" :"000001" ,# 报告部门
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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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@ -361,7 +184,7 @@ 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_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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@ -424,8 +247,8 @@ if end_time == '':
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username='1321340118@qq.com'
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passwd='wgczgyhtyyyyjghi'
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# recv=['liurui_test@163.com','52585119@qq.com']
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# recv=['liurui_test@163.com','jin.wang@chambroad.com']
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recv=['liurui_test@163.com']
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recv=['liurui_test@163.com','jin.wang@chambroad.com']
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# recv=['liurui_test@163.com']
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title='reportname'
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content='brent价格预测报告请看附件'
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file=os.path.join(dataset,'reportname')
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@ -168,73 +168,6 @@ is_update_warning_data = True if datetime.datetime.now().weekday() == 1 else Fa
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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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################################################################################################################ 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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# # 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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# 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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# # 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 = True # 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_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 = False # 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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# 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.connect()
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1733
lib/dataread_jingbo_pro.py
Normal file
1733
lib/dataread_jingbo_pro.py
Normal file
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Load Diff
@ -284,7 +284,7 @@ def predict_main():
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file=max(glob.glob(os.path.join(dataset,'*.pdf')), key=os.path.getctime),
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ssl=ssl,
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)
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# m.send_mail()
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m.send_mail()
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if __name__ == '__main__':
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@ -489,6 +489,7 @@ def model_losss(sqlitedb,end_time):
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accuracy_rote = 0
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for i,group in df3.groupby('CREAT_DATE'):
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accuracy_rote += (group['ACCURACY'].sum()/len(group))*weight_dict[len(group)-1]
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accuracy_rote = round(accuracy_rote,2)
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df4 = pd.DataFrame(columns=['开始日期','结束日期','准确率'])
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df4.loc[len(df4)] = {'开始日期':ds_dates[0],'结束日期':ds_dates[-1],'准确率':accuracy_rote}
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df4.to_sql("accuracy_rote", con=sqlitedb.connection, if_exists='append', index=False)
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Loading…
Reference in New Issue
Block a user