326 lines
11 KiB
Python
326 lines
11 KiB
Python
import logging
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import os
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import logging.handlers
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import datetime
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from lib.tools import MySQLDB,SQLiteHandler
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# eta 接口token
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APPID = "XNLDvxZHHugj7wJ7"
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SECRET = "iSeU4s6cKKBVbt94htVY1p0sqUMqb2xa"
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# eta 接口url
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sourcelisturl = 'http://10.189.2.78:8108/v1/edb/source/list'
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classifylisturl = 'http://10.189.2.78:8108/v1/edb/classify/list?ClassifyType='
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uniquecodedataurl = 'http://10.189.2.78:8108/v1/edb/data?UniqueCode=4991c37becba464609b409909fe4d992&StartDate=2024-02-01'
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classifyidlisturl = 'http://10.189.2.78:8108/v1/edb/list?ClassifyId='
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edbcodedataurl = 'http://10.189.2.78:8108/v1/edb/data?EdbCode='
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edbdatapushurl = 'http://10.189.2.78:8108/v1/edb/push'
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edbdeleteurl = 'http://10.189.2.78:8108/v1/edb/business/edb/del'
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edbbusinessurl = 'http://10.189.2.78:8108/v1/edb/business/data/del'
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edbcodelist = ['ID01385938','lmcads03 lme comdty',
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'GC1 COMB Comdty',
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'C2404171822',
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'dxy curncy',
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'S5443199 ',
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'S5479800',
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'S5443108',
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'H7358586',
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'LC3FM1 INDEX',
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'CNY REGN Curncy',
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's0105897',
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'M0067419',
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'M0066351',
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'S0266372',
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'S0266438',
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'S0266506',
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'ID01384463']
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# 临时写死用指定的列,与上面的edbcode对应，后面更改
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edbnamelist = [
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'ds','y',
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'LME铜价',
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'黄金连1合约',
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'Brent-WTI',
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'美元指数',
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'甲醇鲁南价格',
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'甲醇太仓港口价格',
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'山东丙烯主流价',
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'丙烷（山东）',
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'FEI丙烷 M1',
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'在岸人民币汇率',
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'南华工业品指数',
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'PVC期货主力',
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'PE期货收盘价',
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'PP连续-1月',
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'PP连续-5月',
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'PP连续-9月',
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'PP：拉丝：L5E89：出厂价：华北（第二区域）：内蒙古久泰新材料（日）'
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]
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edbcodenamedict = {
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'ID01385938':'PP：拉丝：1102K：市场价：青州：国家能源宁煤（日）',
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'ID01384463':'PP：拉丝：L5E89：出厂价：华北（第二区域）：内蒙古久泰新材料（日）',
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'lmcads03 lme comdty':'LME铜价',
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'GC1 COMB Comdty':'黄金连1合约',
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'C2404171822':'Brent-WTI',
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'dxy curncy':'美元指数',
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'S5443199 ':'甲醇鲁南价格',
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'S5479800':'甲醇太仓港口价格',
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'S5443108':'山东丙烯主流价',
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'H7358586':'丙烷（山东）',
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'LC3FM1 INDEX':'FEI丙烷 M1',
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'CNY REGN Curncy':'在岸人民币汇率',
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's0105897':'南华工业品指数',
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'M0067419':'PVC期货主力',
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'M0066351':'PE期货收盘价',
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'S0266372':'PP连续-1月',
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'S0266438':'PP连续-5月',
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'S0266506':'PP连续-9月',
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}
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# eta自有数据指标编码
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modelsindex = {
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'NHITS': 'SELF0000077',
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'Informer':'SELF0000078',
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'LSTM':'SELF0000079',
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'iTransformer':'SELF0000080',
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'TSMixer':'SELF0000081',
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'TSMixerx':'SELF0000082',
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'PatchTST':'SELF0000083',
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'RNN':'SELF0000084',
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'GRU':'SELF0000085',
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'TCN':'SELF0000086',
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'BiTCN':'SELF0000087',
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'DilatedRNN':'SELF0000088',
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'MLP':'SELF0000089',
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'DLinear':'SELF0000090',
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'NLinear':'SELF0000091',
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'TFT':'SELF0000092',
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'FEDformer':'SELF0000093',
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'StemGNN':'SELF0000094',
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'MLPMultivariate':'SELF0000095',
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'TiDE':'SELF0000096',
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'DeepNPTS':'SELF0000097'
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}
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# eta 上传预测结果的请求体，后面发起请求的时候更改 model datalist 数据
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data = {
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"IndexCode": "",
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"IndexName": "价格预测模型",
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"Unit": "无",
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"Frequency": "日度",
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"SourceName": f"价格预测",
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"Remark": 'ddd',
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"DataList": [
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{
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"Date": "2024-05-02",
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"Value": 333444
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}
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]
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}
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# eta 分类
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# level：3才可以获取到数据，所以需要人工把能源化工下所有的level3级都找到
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# url = 'http://10.189.2.78:8108/v1/edb/list?ClassifyId=1214'
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#ParentId ":1160, 能源化工
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# ClassifyId ":1214,原油
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#ParentId ":1214,"，就是原油下所有的数据。
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ClassifyId = 1161
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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 = f"http://{server_host}/jingbo-api/api/server/login"
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upload_url = f"http://{server_host}/jingbo-api/api/analysis/reportInfo/researchUploadReportSave"
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upload_warning_url = f"http://{server_host}/jingbo-api/api/basicBuiness/crudeOilWarning/save"
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query_data_list_item_nos_url = f"http://{server_host}/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":'上传聚烯烃价格预测报告',
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"data":{
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"ownerAccount":'36541', #报告所属用户账号 36541 - 贾青雪
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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":'jxtjgycbg', # 研究报告分类编码
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"smartBusinessClassCode":'JXTJGYCBG', #分析报告分类编码
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"reportEmployeeCode":"305000050000" ,# 报告人 E40482 - 管理员 305000050000 - 贾青雪
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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
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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_market = False # 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 = 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 = 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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db_mysql = MySQLDB(host=host, user=dbusername, password=password, database=dbname)
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db_mysql.connect()
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print("数据库连接成功",host,dbname,dbusername)
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# 数据截取日期
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start_year = 2020 # 数据开始年份
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end_time = '' # 数据截取日期
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freq = 'B' # 时间频率，"D": 天 "W": 周"M": 月"Q": 季度"A": 年 "H": 小时 "T": 分钟 "S": 秒 "B": 工作日
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delweekenday = True if freq == 'B' else False # 是否删除周末数据
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is_corr = False # 特征是否参与滞后领先提升相关系数
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add_kdj = False # 是否添加kdj指标
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if add_kdj and is_edbnamelist:
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edbnamelist = edbnamelist+['K','D','J']
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### 模型参数
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y = 'AVG-金能大唐久泰青州'
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avg_cols = [
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'PP：拉丝：1102K：出厂价：青州：国家能源宁煤（日）',
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'PP：拉丝：L5E89：出厂价：华北（第二区域）：内蒙古久泰新材料（日）',
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'PP：拉丝：L5E89：出厂价：河北、鲁北：大唐内蒙多伦（日）',
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'PP：拉丝：HP550J：市场价：青岛：金能化学（日）'
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]
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offsite = 80
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offsite_col = ['PP：拉丝：HP550J：市场价：青岛：金能化学（日）']
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horizon =5 # 预测的步长
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input_size = 40 # 输入序列长度
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train_steps = 50 if is_debug else 1000 # 训练步数,用来限定epoch次数
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val_check_steps = 30 # 评估频率
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early_stop_patience_steps = 5 # 早停的耐心步数
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# --- 交叉验证用的参数
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test_size = 100 # 测试集大小，定义100，后面使用的时候重新赋值
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val_size = test_size # 验证集大小,同测试集大小
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### 特征筛选用到的参数
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k = 100 # 特征筛选数量，如果是0或者值比特征数量大，代表全部特征
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corr_threshold = 0.6 # 相关性大于0.6的特征
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rote = 0.06 # 绘图上下界阈值
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### 计算准确率
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weight_dict = [0.4,0.15,0.1,0.1,0.25] # 权重
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### 文件
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data_set = 'PP指标数据.xlsx' # 数据集文件
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dataset = 'juxitingdataset' # 数据集文件夹
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# 数据库名称
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db_name = os.path.join(dataset,'jbsh_juxiting.db')
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sqlitedb = SQLiteHandler(db_name)
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sqlitedb.connect()
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settings = f'{input_size}-{horizon}-{train_steps}--{k}-{data_set}-{y}'
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# 获取日期时间
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# now = datetime.datetime.now().strftime('%Y%m%d%H%M%S') # 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'PP大模型预测报告--{end_time}.pdf' # 报告文件名
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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
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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']
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# recv=['liurui_test@163.com']
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title='reportname'
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content=y+'预测报告请看附件'
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file=os.path.join(dataset,'reportname')
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# file=os.path.join(dataset,'14-7-50--100-原油指标数据.xlsx-Brent连1合约价格--20240731175936-预测报告.pdf')
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ssl=True
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### 日志配置
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# 创建日志目录（如果不存在）
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log_dir = 'logs'
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if not os.path.exists(log_dir):
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os.makedirs(log_dir)
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# 配置日志记录器
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logger = logging.getLogger('my_logger')
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logger.setLevel(logging.INFO)
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# 配置文件处理器，将日志记录到文件
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file_handler = logging.handlers.RotatingFileHandler(os.path.join(log_dir, 'pricepredict.log'), maxBytes=1024 * 1024, backupCount=5)
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file_handler.setFormatter(logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s'))
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# 配置控制台处理器，将日志打印到控制台
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console_handler = logging.StreamHandler()
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console_handler.setFormatter(logging.Formatter('%(message)s'))
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# 将处理器添加到日志记录器
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logger.addHandler(file_handler)
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logger.addHandler(console_handler)
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# logger.info('当前配置：'+settings)
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