原油月度调试通过
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workpc
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@ -2,7 +2,7 @@ import logging
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import os
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import os
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import logging.handlers
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import logging.handlers
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import datetime
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import datetime
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from lib.tools import MySQLDB,SQLiteHandler
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from lib.tools import MySQLDB, SQLiteHandler
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# eta 接口token
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# eta 接口token
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@ -19,57 +19,56 @@ 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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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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edbbusinessurl = 'http://10.189.2.78:8108/v1/edb/business/data/del'
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edbcodelist = ['CO1 Comdty', 'ovx index', 'C2404194834', 'C2404199738', 'dxy curncy', 'C2403128043', 'C2403150124',
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edbcodelist = ['CO1 Comdty', 'ovx index', 'C2404194834', 'C2404199738', 'dxy curncy', 'C2403128043', 'C2403150124',
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'DOESCRUD Index', 'WTRBM1 EEGC Index', 'FVHCM1 INDEX', 'doedtprd index', 'CFFDQMMN INDEX',
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'DOESCRUD Index', 'WTRBM1 EEGC Index', 'FVHCM1 INDEX', 'doedtprd index', 'CFFDQMMN INDEX',
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'C2403083739', 'C2404167878', 'C2403250571', 'lmcads03 lme comdty', 'GC1 COMB Comdty',
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'C2403083739', 'C2404167878', 'C2403250571', 'lmcads03 lme comdty', 'GC1 COMB Comdty',
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'C2404171822','C2404167855',
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'C2404171822', 'C2404167855',
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# 'W000825','W000826','G.IPE', # 美国汽柴油
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# 'W000825','W000826','G.IPE', # 美国汽柴油
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# 'S5131019','ID00135604','FSGAM1 Index','S5120408','ID00136724', # 新加坡汽柴油
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# 'S5131019','ID00135604','FSGAM1 Index','S5120408','ID00136724', # 新加坡汽柴油
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]
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]
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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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# 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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edbnamelist = [
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'ds','y',
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'ds', 'y',
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'Brent c1-c6','Brent c1-c3','Brent-WTI','美国商业原油库存',
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'Brent c1-c6', 'Brent c1-c3', 'Brent-WTI', '美国商业原油库存',
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'DFL','美国汽油裂解价差','ovx index','dxy curncy','lmcads03 lme comdty',
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'DFL', '美国汽油裂解价差', 'ovx index', 'dxy curncy', 'lmcads03 lme comdty',
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'C2403128043','C2403150124','FVHCM1 INDEX','doedtprd index','CFFDQMMN INDEX',
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'C2403128043', 'C2403150124', 'FVHCM1 INDEX', 'doedtprd index', 'CFFDQMMN INDEX',
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'C2403083739','C2404167878',
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'C2403083739', 'C2404167878',
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'GC1 COMB Comdty','C2404167855',
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'GC1 COMB Comdty', 'C2404167855',
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# 'A汽油价格','W000826','ICE柴油价格',
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# 'A汽油价格','W000826','ICE柴油价格',
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# '新加坡(含硫0.05%) 柴油现货价','柴油：10ppm：国际市场：FOB中间价：新加坡（日）','Bloomberg Commodity Fair Value Singapore Mogas 92 Swap Month 1','97#汽油FOB新加坡现货价','无铅汽油：97#：国际市场：FOB中间价：新加坡（日）'
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# '新加坡(含硫0.05%) 柴油现货价','柴油：10ppm：国际市场：FOB中间价：新加坡（日）','Bloomberg Commodity Fair Value Singapore Mogas 92 Swap Month 1','97#汽油FOB新加坡现货价','无铅汽油：97#：国际市场：FOB中间价：新加坡（日）'
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]
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]
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# eta自有数据指标编码
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# eta自有数据指标编码
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modelsindex = {
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modelsindex = {
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'NHITS': 'SELF0000001',
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'NHITS': 'SELF0000001',
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'Informer':'SELF0000057',
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'Informer': 'SELF0000057',
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'LSTM':'SELF0000058',
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'LSTM': 'SELF0000058',
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'iTransformer':'SELF0000059',
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'iTransformer': 'SELF0000059',
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'TSMixer':'SELF0000060',
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'TSMixer': 'SELF0000060',
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'TSMixerx':'SELF0000061',
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'TSMixerx': 'SELF0000061',
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'PatchTST':'SELF0000062',
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'PatchTST': 'SELF0000062',
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'RNN':'SELF0000063',
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'RNN': 'SELF0000063',
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'GRU':'SELF0000064',
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'GRU': 'SELF0000064',
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'TCN':'SELF0000065',
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'TCN': 'SELF0000065',
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'BiTCN':'SELF0000066',
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'BiTCN': 'SELF0000066',
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'DilatedRNN':'SELF0000067',
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'DilatedRNN': 'SELF0000067',
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'MLP':'SELF0000068',
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'MLP': 'SELF0000068',
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'DLinear':'SELF0000069',
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'DLinear': 'SELF0000069',
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'NLinear':'SELF0000070',
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'NLinear': 'SELF0000070',
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'TFT':'SELF0000071',
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'TFT': 'SELF0000071',
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'FEDformer':'SELF0000072',
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'FEDformer': 'SELF0000072',
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'StemGNN':'SELF0000073',
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'StemGNN': 'SELF0000073',
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'MLPMultivariate':'SELF0000074',
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'MLPMultivariate': 'SELF0000074',
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'TiDE':'SELF0000075',
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'TiDE': 'SELF0000075',
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'DeepNPTS':'SELF0000076'
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'DeepNPTS': 'SELF0000076'
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}
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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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# 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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data = {
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"IndexCode": "",
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"IndexCode": "",
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"IndexName": "价格预测模型",
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"IndexName": "价格预测模型",
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"Unit": "无",
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"Unit": "无",
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"Frequency": "日度",
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"Frequency": "日度",
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"SourceName": f"价格预测",
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"SourceName": f"价格预测",
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"Remark": 'ddd',
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"Remark": 'ddd',
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@ -79,19 +78,18 @@ data = {
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"Value": 333444
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"Value": 333444
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}
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}
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]
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]
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}
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}
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# eta 分类
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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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# 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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# 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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# ParentId ":1160, 能源化工
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# ClassifyId ":1214,原油
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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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# ParentId ":1214,"，就是原油下所有的数据。
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ClassifyId = 1214
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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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server_host = '192.168.100.53'
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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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login_pushreport_url = f"http://{server_host}:8080/jingbo-dev/api/server/login"
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@ -103,7 +101,7 @@ 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_test",
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# "password": "MmVmNzNlOWI0MmY0ZDdjZGUwNzE3ZjFiMDJiZDZjZWU=", # Shihua@123456
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# "password": "MmVmNzNlOWI0MmY0ZDdjZGUwNzE3ZjFiMDJiZDZjZWU=", # Shihua@123456
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"password": "ZTEwYWRjMzk0OWJhNTlhYmJlNTZlMDU3ZjIwZjg4M2U=", # 123456
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"password": "ZTEwYWRjMzk0OWJhNTlhYmJlNTZlMDU3ZjIwZjg4M2U=", # 123456
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"tenantHashCode": "8a4577dbd919675758d57999a1e891fe",
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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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@ -112,39 +110,39 @@ login_data = {
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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": '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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"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":'YCJGYCBG', #分析报告分类编码
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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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"reportEmployeeCode": "E40116", # 报告人
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"reportDeptCode" :"D0044" ,# 报告部门
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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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"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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warning_data = {
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warning_data = {
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"funcModule":'原油特征停更预警',
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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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"funcOperation": '原油特征停更预警',
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"data":{
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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_TYPE_NAME': '特征数据停更预警',
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'WARNING_CONTENT':'',
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'WARNING_CONTENT': '',
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'WARNING_DATE':''
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'WARNING_DATE': ''
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}
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}
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}
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}
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query_data_list_item_nos_data = {
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query_data_list_item_nos_data = {
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"funcModule": "数据项",
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"funcModule": "数据项",
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"funcOperation": "查询",
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"funcOperation": "查询",
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"data": {
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"data": {
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"dateStart":"20200101",
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"dateStart": "20200101",
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"dateEnd":"20241231",
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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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"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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}
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}
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@ -152,96 +150,96 @@ query_data_list_item_nos_data = {
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# 北京环境数据库
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# 北京环境数据库
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host = '192.168.101.27'
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host = '192.168.101.27'
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port = 3306
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port = 3306
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dbusername ='root'
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dbusername = 'root'
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password = '123456'
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password = '123456'
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dbname = 'jingbo_test'
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dbname = 'jingbo_test'
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table_name = 'v_tbl_crude_oil_warning'
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table_name = 'v_tbl_crude_oil_warning'
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### 开关
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# 开关
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is_train = False # 是否训练
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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_debug = True # 是否调试
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is_eta = False # 是否使用eta接口
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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_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_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_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_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_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_eta = False # 预测结果上传到eta
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is_update_report = False # 是否上传报告
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is_update_report = 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_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_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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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,
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password=password, database=dbname)
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db_mysql.connect()
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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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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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# 数据截取日期
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start_year = 1993 # 数据开始年份
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start_year = 2005 # 数据开始年份
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end_time = '' # 数据截取日期
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end_time = '' # 数据截取日期
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freq = 'M' # 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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freq = 'M' # 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 "WW" 自定义周
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delweekenday = True if freq == 'B' else False # 是否删除周末数据
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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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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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add_kdj = False # 是否添加kdj指标
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if add_kdj and is_edbnamelist:
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if add_kdj and is_edbnamelist:
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edbnamelist = edbnamelist+['K','D','J']
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edbnamelist = edbnamelist+['K', 'D', 'J']
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### 模型参数
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# 模型参数
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y = 'Brent连1合约价格' # 原油指标数据的目标变量 Brent连1合约价格 Brent活跃合约
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y = 'Brent连1合约价格' # 原油指标数据的目标变量 Brent连1合约价格 Brent活跃合约
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horizon =3 # 预测的步长
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horizon = 4 # 预测的步长
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input_size = 9 # 输入序列长度
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input_size = 16 # 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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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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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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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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# --- 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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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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val_size = test_size # 验证集大小,同测试集大小
|
||||||
|
|
||||||
### 特征筛选用到的参数
|
# 特征筛选用到的参数
|
||||||
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
|
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
|
||||||
corr_threshold = 0.6 # 相关性大于0.6的特征
|
corr_threshold = 0.6 # 相关性大于0.6的特征
|
||||||
rote = 0.06 # 绘图上下界阈值
|
rote = 0.06 # 绘图上下界阈值
|
||||||
|
|
||||||
### 计算准确率
|
# 计算准确率
|
||||||
weight_dict = [0.4,0.15,0.1,0.1,0.25] # 权重
|
weight_dict = [0.4, 0.15, 0.1, 0.1, 0.25] # 权重
|
||||||
|
|
||||||
|
|
||||||
### 文件
|
# 文件
|
||||||
data_set = '原油指标数据.xlsx' # 数据集文件
|
data_set = '原油指标数据.xlsx' # 数据集文件
|
||||||
dataset = 'yuanyouyuedudataset' # 数据集文件夹
|
dataset = 'yuanyouyuedudataset' # 数据集文件夹
|
||||||
|
|
||||||
# 数据库名称
|
# 数据库名称
|
||||||
db_name = os.path.join(dataset,'jbsh_yuanyou_yuedu.db')
|
db_name = os.path.join(dataset, 'jbsh_yuanyou_yuedu.db')
|
||||||
sqlitedb = SQLiteHandler(db_name)
|
sqlitedb = SQLiteHandler(db_name)
|
||||||
sqlitedb.connect()
|
sqlitedb.connect()
|
||||||
|
|
||||||
settings = f'{input_size}-{horizon}-{train_steps}--{k}-{data_set}-{y}'
|
settings = f'{input_size}-{horizon}-{train_steps}--{k}-{data_set}-{y}'
|
||||||
# 获取日期时间
|
# 获取日期时间
|
||||||
# now = datetime.datetime.now().strftime('%Y%m%d%H%M%S') # 获取当前日期时间
|
# now = datetime.datetime.now().strftime('%Y%m%d%H%M%S') # 获取当前日期时间
|
||||||
now = datetime.datetime.now().strftime('%Y-%m-%d') # 获取当前日期时间
|
now = datetime.datetime.now().strftime('%Y-%m-%d') # 获取当前日期时间
|
||||||
reportname = f'Brent原油大模型预测--{end_time}.pdf' # 报告文件名
|
reportname = f'Brent原油大模型月度预测--{end_time}.pdf' # 报告文件名
|
||||||
reportname = reportname.replace(':', '-') # 替换冒号
|
reportname = reportname.replace(':', '-') # 替换冒号
|
||||||
if end_time == '':
|
if end_time == '':
|
||||||
end_time = now
|
end_time = now
|
||||||
### 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','jin.wang@chambroad.com']
|
recv = ['liurui_test@163.com', 'jin.wang@chambroad.com']
|
||||||
# recv=['liurui_test@163.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')
|
||||||
# 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')
|
# 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')
|
||||||
ssl=True
|
ssl = True
|
||||||
|
|
||||||
|
|
||||||
### 日志配置
|
# 日志配置
|
||||||
|
|
||||||
# 创建日志目录（如果不存在）
|
# 创建日志目录（如果不存在）
|
||||||
log_dir = 'logs'
|
log_dir = 'logs'
|
||||||
@ -253,8 +251,10 @@ logger = logging.getLogger('my_logger')
|
|||||||
logger.setLevel(logging.INFO)
|
logger.setLevel(logging.INFO)
|
||||||
|
|
||||||
# 配置文件处理器，将日志记录到文件
|
# 配置文件处理器，将日志记录到文件
|
||||||
file_handler = logging.handlers.RotatingFileHandler(os.path.join(log_dir, 'pricepredict.log'), maxBytes=1024 * 1024, backupCount=5)
|
file_handler = logging.handlers.RotatingFileHandler(os.path.join(
|
||||||
file_handler.setFormatter(logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s'))
|
log_dir, 'pricepredict.log'), maxBytes=1024 * 1024, backupCount=5)
|
||||||
|
file_handler.setFormatter(logging.Formatter(
|
||||||
|
'%(asctime)s - %(name)s - %(levelname)s - %(message)s'))
|
||||||
|
|
||||||
# 配置控制台处理器，将日志打印到控制台
|
# 配置控制台处理器，将日志打印到控制台
|
||||||
console_handler = logging.StreamHandler()
|
console_handler = logging.StreamHandler()
|
||||||
@ -265,4 +265,3 @@ logger.addHandler(file_handler)
|
|||||||
logger.addHandler(console_handler)
|
logger.addHandler(console_handler)
|
||||||
|
|
||||||
# logger.info('当前配置：'+settings)
|
# logger.info('当前配置：'+settings)
|
||||||
|
|
||||||
|
|||||||
@ -838,7 +838,9 @@ def datachuli(df_zhibiaoshuju, df_zhibiaoliebiao, datecol='date', end_time='', y
|
|||||||
df = df.resample('W', on='ds').mean().reset_index()
|
df = df.resample('W', on='ds').mean().reset_index()
|
||||||
elif config.freq == 'M':
|
elif config.freq == 'M':
|
||||||
# 按月取样
|
# 按月取样
|
||||||
df = df.resample('M', on='ds').mean().reset_index()
|
if 'yearmonthweeks' in df.columns:
|
||||||
|
df.drop('yearmonthweeks', axis=1, inplace=True)
|
||||||
|
df = df.resample('ME', on='ds').mean().reset_index()
|
||||||
# 删除预测列空值的行
|
# 删除预测列空值的行
|
||||||
''' 工作日缺失，如果删除，会影响预测结果，导致统计准确率出错 '''
|
''' 工作日缺失，如果删除，会影响预测结果，导致统计准确率出错 '''
|
||||||
# df = df.dropna(subset=['y'])
|
# df = df.dropna(subset=['y'])
|
||||||
|
|||||||
@ -1,12 +1,66 @@
|
|||||||
# 读取配置
|
# 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
|
from lib.dataread import *
|
||||||
|
from config_jingbo_yuedu 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 datetime
|
||||||
import torch
|
import torch
|
||||||
torch.set_float32_matmul_precision("high")
|
torch.set_float32_matmul_precision("high")
|
||||||
|
|
||||||
|
global_config.update({
|
||||||
|
# 核心参数
|
||||||
|
'logger': logger,
|
||||||
|
'dataset': dataset,
|
||||||
|
'y': y,
|
||||||
|
'is_debug': is_debug,
|
||||||
|
'is_train': is_train,
|
||||||
|
'is_fivemodels': is_fivemodels,
|
||||||
|
'settings': settings,
|
||||||
|
|
||||||
|
|
||||||
|
# 模型参数
|
||||||
|
'data_set': data_set,
|
||||||
|
'input_size': input_size,
|
||||||
|
'horizon': horizon,
|
||||||
|
'train_steps': train_steps,
|
||||||
|
'val_check_steps': val_check_steps,
|
||||||
|
'val_size': val_size,
|
||||||
|
'test_size': test_size,
|
||||||
|
'modelsindex': modelsindex,
|
||||||
|
'rote': rote,
|
||||||
|
|
||||||
|
# 特征工程开关
|
||||||
|
'is_del_corr': is_del_corr,
|
||||||
|
'is_del_tow_month': is_del_tow_month,
|
||||||
|
'is_eta': is_eta,
|
||||||
|
'is_update_eta': is_update_eta,
|
||||||
|
'early_stop_patience_steps': early_stop_patience_steps,
|
||||||
|
|
||||||
|
# 时间参数
|
||||||
|
'start_year': start_year,
|
||||||
|
'end_time': end_time or datetime.datetime.now().strftime("%Y-%m-%d"),
|
||||||
|
'freq': freq, # 保持列表结构
|
||||||
|
|
||||||
|
# 接口配置
|
||||||
|
'login_pushreport_url': login_pushreport_url,
|
||||||
|
'login_data': login_data,
|
||||||
|
'upload_url': upload_url,
|
||||||
|
'upload_warning_url': upload_warning_url,
|
||||||
|
'warning_data': warning_data,
|
||||||
|
|
||||||
|
# 查询接口
|
||||||
|
'query_data_list_item_nos_url': query_data_list_item_nos_url,
|
||||||
|
'query_data_list_item_nos_data': query_data_list_item_nos_data,
|
||||||
|
|
||||||
|
# eta 配置
|
||||||
|
'APPID': APPID,
|
||||||
|
'SECRET': SECRET,
|
||||||
|
'etadata': data,
|
||||||
|
|
||||||
|
# 数据库配置
|
||||||
|
'sqlitedb': sqlitedb,
|
||||||
|
})
|
||||||
|
|
||||||
|
|
||||||
def predict_main():
|
def predict_main():
|
||||||
@ -72,7 +126,8 @@ def predict_main():
|
|||||||
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...')
|
||||||
@ -83,26 +138,26 @@ def predict_main():
|
|||||||
df_zhibiaoshuju = get_high_low_data(df_zhibiaoshuju)
|
df_zhibiaoshuju = get_high_low_data(df_zhibiaoshuju)
|
||||||
else:
|
else:
|
||||||
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')
|
||||||
df_zhibiaoshuju = get_market_data(end_time,df_zhibiaoshuju)
|
df_zhibiaoshuju = get_market_data(
|
||||||
|
end_time, df_zhibiaoshuju)
|
||||||
|
|
||||||
except :
|
except:
|
||||||
logger.info('最高最低价拼接失败')
|
logger.info('最高最低价拼接失败')
|
||||||
|
|
||||||
# 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)
|
||||||
@ -124,48 +179,65 @@ def predict_main():
|
|||||||
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')
|
config.logger.info(f'要保存的真实值：{row_dict}')
|
||||||
check_query = sqlitedb.select_data('trueandpredict', where_condition=f"ds = '{row.ds}'")
|
# 判断ds是否为字符串类型,如果不是则转换为字符串类型
|
||||||
|
if isinstance(row_dict['ds'], (pd.Timestamp, datetime.datetime)):
|
||||||
|
row_dict['ds'] = row_dict['ds'].strftime('%Y-%m-%d')
|
||||||
|
elif not isinstance(row_dict['ds'], str):
|
||||||
|
try:
|
||||||
|
row_dict['ds'] = pd.to_datetime(
|
||||||
|
row_dict['ds']).strftime('%Y-%m-%d')
|
||||||
|
except:
|
||||||
|
logger.warning(f"无法解析的时间格式: {row_dict['ds']}")
|
||||||
|
# row_dict['ds'] = row_dict['ds'].strftime('%Y-%m-%d')
|
||||||
|
# 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:
|
if len(check_query) > 0:
|
||||||
set_clause = ", ".join([f"{key} = '{value}'" for key, value in row_dict.items()])
|
set_clause = ", ".join(
|
||||||
sqlitedb.update_data('trueandpredict', set_clause, where_condition=f"ds = '{row.ds}'")
|
[f"{key} = '{value}'" for key, value in row_dict.items()])
|
||||||
|
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):
|
||||||
try:
|
try:
|
||||||
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:
|
except:
|
||||||
logger.info(f'更新accuracy表的y值失败：{row_dict}')
|
logger.info(f'更新accuracy表的y值失败：{row_dict}')
|
||||||
# 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
|
|
||||||
# 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90%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)*0.1,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:-1]
|
modelnames = model_results.columns.to_list()[2:-1]
|
||||||
@ -173,47 +245,59 @@ def predict_main():
|
|||||||
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(
|
||||||
sqlitedb.insert_data('most_model', (datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S'), most_common_model,), columns=('ds', 'most_common_model',))
|
'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:
|
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(
|
||||||
warning_data_df['WARNING_DATE'] = datetime.date.today().strftime("%Y-%m-%d %H:%M:%S")
|
f'mysql+pymysql://{dbusername}:{password}@{host}:{port}/{dbname}')
|
||||||
warning_data_df['TENANT_CODE'] = 'T0004'
|
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
|
# 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:
|
||||||
@ -228,43 +312,42 @@ def predict_main():
|
|||||||
|
|
||||||
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=global_config['horizon'],
|
||||||
input_size=input_size,
|
input_size=global_config['input_size'],
|
||||||
train_steps=train_steps,
|
train_steps=global_config['train_steps'],
|
||||||
val_check_steps=val_check_steps,
|
val_check_steps=global_config['val_check_steps'],
|
||||||
early_stop_patience_steps=early_stop_patience_steps,
|
early_stop_patience_steps=global_config['early_stop_patience_steps'],
|
||||||
is_debug=is_debug,
|
is_debug=global_config['is_debug'],
|
||||||
dataset=dataset,
|
dataset=global_config['dataset'],
|
||||||
is_train=is_train,
|
is_train=global_config['is_train'],
|
||||||
is_fivemodels=is_fivemodels,
|
is_fivemodels=global_config['is_fivemodels'],
|
||||||
val_size=val_size,
|
val_size=global_config['val_size'],
|
||||||
test_size=test_size,
|
test_size=global_config['test_size'],
|
||||||
settings=settings,
|
settings=global_config['settings'],
|
||||||
now=now,
|
now=now,
|
||||||
etadata=etadata,
|
etadata=global_config['etadata'],
|
||||||
modelsindex=modelsindex,
|
modelsindex=global_config['modelsindex'],
|
||||||
data=data,
|
data=data,
|
||||||
is_eta=is_eta,
|
is_eta=global_config['is_eta'],
|
||||||
end_time=end_time,
|
end_time=global_config['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')
|
||||||
# title = f'{settings}--{end_time}-预测报告' # 报告标题
|
title = f'{settings}--{end_time}-预测报告' # 报告标题
|
||||||
# reportname = f'Brent原油大模型月度预测--{end_time}.pdf' # 报告文件名
|
reportname = f'Brent原油大模型周度预测--{end_time}.pdf' # 报告文件名
|
||||||
# reportname = reportname.replace(':', '-') # 替换冒号
|
reportname = reportname.replace(':', '-') # 替换冒号
|
||||||
# brent_export_pdf(dataset=dataset,num_models = 5 if is_fivemodels else 22,time=end_time,
|
brent_export_pdf(dataset=dataset, num_models=5 if is_fivemodels else 22, time=end_time,
|
||||||
# reportname=reportname,sqlitedb=sqlitedb),
|
reportname=reportname, sqlitedb=sqlitedb),
|
||||||
|
|
||||||
# logger.info('制作报告end')
|
logger.info('制作报告end')
|
||||||
# logger.info('模型训练完成')
|
logger.info('模型训练完成')
|
||||||
|
|
||||||
# # LSTM 单变量模型
|
# # LSTM 单变量模型
|
||||||
# ex_Lstm(df,input_seq_len=input_size,output_seq_len=horizon,is_debug=is_debug,dataset=dataset)
|
# ex_Lstm(df,input_seq_len=input_size,output_seq_len=horizon,is_debug=is_debug,dataset=dataset)
|
||||||
@ -276,24 +359,23 @@ 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
|
# # 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('2024-12-1', '2025-2-26', freq='W'):
|
||||||
for i_time in pd.date_range('2022-6-1', '2025-3-1', freq='ME'):
|
# end_time = i_time.strftime('%Y-%m-%d')
|
||||||
end_time = i_time.strftime('%Y-%m-%d')
|
# predict_main()
|
||||||
predict_main()
|
|
||||||
|
|
||||||
# predict_main()
|
predict_main()
|
||||||
|
|||||||
@ -102,235 +102,235 @@ 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(
|
df_zhibiaoshuju, df_zhibiaoliebiao = etadata.get_eta_api_yuanyou_data(
|
||||||
# data_set=data_set, dataset=dataset) # 原始数据，未处理
|
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:
|
||||||
# # 如果是测试环境，最高价最低价取excel文档
|
# 如果是测试环境，最高价最低价取excel文档
|
||||||
# if server_host == '192.168.100.53':
|
if server_host == '192.168.100.53':
|
||||||
# logger.info('从excel文档获取最高价最低价')
|
logger.info('从excel文档获取最高价最低价')
|
||||||
# df_zhibiaoshuju = get_high_low_data(df_zhibiaoshuju)
|
df_zhibiaoshuju = get_high_low_data(df_zhibiaoshuju)
|
||||||
# else:
|
else:
|
||||||
# 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')
|
||||||
# df_zhibiaoshuju = get_market_data(
|
df_zhibiaoshuju = get_market_data(
|
||||||
# end_time, df_zhibiaoshuju)
|
end_time, df_zhibiaoshuju)
|
||||||
|
|
||||||
# except:
|
except:
|
||||||
# logger.info('最高最低价拼接失败')
|
logger.info('最高最低价拼接失败')
|
||||||
|
|
||||||
# # 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)
|
||||||
# # 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()
|
||||||
# config.logger.info(f'要保存的真实值：{row_dict}')
|
config.logger.info(f'要保存的真实值：{row_dict}')
|
||||||
# # 判断ds是否为字符串类型,如果不是则转换为字符串类型
|
# 判断ds是否为字符串类型,如果不是则转换为字符串类型
|
||||||
# if isinstance(row_dict['ds'], (pd.Timestamp, datetime.datetime)):
|
if isinstance(row_dict['ds'], (pd.Timestamp, datetime.datetime)):
|
||||||
# row_dict['ds'] = row_dict['ds'].strftime('%Y-%m-%d')
|
row_dict['ds'] = row_dict['ds'].strftime('%Y-%m-%d')
|
||||||
# elif not isinstance(row_dict['ds'], str):
|
elif not isinstance(row_dict['ds'], str):
|
||||||
# try:
|
try:
|
||||||
# row_dict['ds'] = pd.to_datetime(
|
row_dict['ds'] = pd.to_datetime(
|
||||||
# row_dict['ds']).strftime('%Y-%m-%d')
|
row_dict['ds']).strftime('%Y-%m-%d')
|
||||||
# except:
|
except:
|
||||||
# logger.warning(f"无法解析的时间格式: {row_dict['ds']}")
|
logger.warning(f"无法解析的时间格式: {row_dict['ds']}")
|
||||||
# # row_dict['ds'] = row_dict['ds'].strftime('%Y-%m-%d')
|
# row_dict['ds'] = row_dict['ds'].strftime('%Y-%m-%d')
|
||||||
# # 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(
|
check_query = sqlitedb.select_data(
|
||||||
# 'trueandpredict', where_condition=f"ds = '{row.ds}'")
|
'trueandpredict', where_condition=f"ds = '{row.ds}'")
|
||||||
# if len(check_query) > 0:
|
if len(check_query) > 0:
|
||||||
# set_clause = ", ".join(
|
set_clause = ", ".join(
|
||||||
# [f"{key} = '{value}'" for key, value in row_dict.items()])
|
[f"{key} = '{value}'" for key, value in row_dict.items()])
|
||||||
# sqlitedb.update_data(
|
sqlitedb.update_data(
|
||||||
# 'trueandpredict', set_clause, where_condition=f"ds = '{row.ds}'")
|
'trueandpredict', set_clause, where_condition=f"ds = '{row.ds}'")
|
||||||
# continue
|
continue
|
||||||
# sqlitedb.insert_data('trueandpredict', tuple(
|
sqlitedb.insert_data('trueandpredict', tuple(
|
||||||
# row_dict.values()), columns=row_dict.keys())
|
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(
|
update_y = sqlitedb.select_data(
|
||||||
# 'accuracy', where_condition="y is null")
|
'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):
|
||||||
# try:
|
try:
|
||||||
# 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(
|
sqlitedb.update_data(
|
||||||
# 'accuracy', f"y = {yy},LOW_PRICE = {LOW},HIGH_PRICE = {HIGH}", where_condition=f"ds = '{row_dict['ds']}'")
|
'accuracy', f"y = {yy},LOW_PRICE = {LOW},HIGH_PRICE = {HIGH}", where_condition=f"ds = '{row_dict['ds']}'")
|
||||||
# except:
|
except:
|
||||||
# logger.info(f'更新accuracy表的y值失败：{row_dict}')
|
logger.info(f'更新accuracy表的y值失败：{row_dict}')
|
||||||
# # 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}')
|
||||||
|
|
||||||
# # 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(
|
model_results = sqlitedb.select_data(
|
||||||
# 'trueandpredict', order_by="ds DESC", limit="60")
|
'trueandpredict', order_by="ds DESC", limit="60")
|
||||||
# # 删除空值率为90%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(
|
model_results = model_results.dropna(
|
||||||
# thresh=len(model_results)*0.1, axis=1)
|
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:-1]
|
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[f'{model}_abs_error_rate'] = abs(
|
||||||
# model_results['y'] - model_results[model]) / model_results['y']
|
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(
|
min_abs_error_rate_values = model_results.apply(
|
||||||
# lambda row: row[[f'{model}_abs_error_rate' for model in modelnames]].min(), axis=1)
|
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(
|
min_abs_error_rate_column_name = model_results.apply(
|
||||||
# lambda row: row[[f'{model}_abs_error_rate' for model in modelnames]].idxmin(), axis=1)
|
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(
|
min_abs_error_rate_column_name = min_abs_error_rate_column_name.map(
|
||||||
# lambda x: x.split('_')[0])
|
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(
|
sqlitedb.create_table(
|
||||||
# 'most_model', columns="ds datetime, most_common_model TEXT")
|
'most_model', columns="ds datetime, most_common_model TEXT")
|
||||||
# sqlitedb.insert_data('most_model', (datetime.datetime.now().strftime(
|
sqlitedb.insert_data('most_model', (datetime.datetime.now().strftime(
|
||||||
# '%Y-%m-%d %H:%M:%S'), most_common_model,), columns=('ds', 'most_common_model',))
|
'%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][[
|
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']]
|
'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',
|
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'})
|
'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(
|
engine = create_engine(
|
||||||
# f'mysql+pymysql://{dbusername}:{password}@{host}:{port}/{dbname}')
|
f'mysql+pymysql://{dbusername}:{password}@{host}:{port}/{dbname}')
|
||||||
# warning_data_df['WARNING_DATE'] = datetime.date.today().strftime(
|
warning_data_df['WARNING_DATE'] = datetime.date.today().strftime(
|
||||||
# "%Y-%m-%d %H:%M:%S")
|
"%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(
|
warning_data_df['ID'] = range(
|
||||||
# max_id + 1, max_id + 1 + len(warning_data_df))
|
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(
|
warning_data_df.to_sql(
|
||||||
# table_name, con=engine, if_exists='append', index=False)
|
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=global_config['horizon'],
|
horizon=global_config['horizon'],
|
||||||
# input_size=global_config['input_size'],
|
input_size=global_config['input_size'],
|
||||||
# train_steps=global_config['train_steps'],
|
train_steps=global_config['train_steps'],
|
||||||
# val_check_steps=global_config['val_check_steps'],
|
val_check_steps=global_config['val_check_steps'],
|
||||||
# early_stop_patience_steps=global_config['early_stop_patience_steps'],
|
early_stop_patience_steps=global_config['early_stop_patience_steps'],
|
||||||
# is_debug=global_config['is_debug'],
|
is_debug=global_config['is_debug'],
|
||||||
# dataset=global_config['dataset'],
|
dataset=global_config['dataset'],
|
||||||
# is_train=global_config['is_train'],
|
is_train=global_config['is_train'],
|
||||||
# is_fivemodels=global_config['is_fivemodels'],
|
is_fivemodels=global_config['is_fivemodels'],
|
||||||
# val_size=global_config['val_size'],
|
val_size=global_config['val_size'],
|
||||||
# test_size=global_config['test_size'],
|
test_size=global_config['test_size'],
|
||||||
# settings=global_config['settings'],
|
settings=global_config['settings'],
|
||||||
# now=now,
|
now=now,
|
||||||
# etadata=global_config['etadata'],
|
etadata=global_config['etadata'],
|
||||||
# modelsindex=global_config['modelsindex'],
|
modelsindex=global_config['modelsindex'],
|
||||||
# data=data,
|
data=data,
|
||||||
# is_eta=global_config['is_eta'],
|
is_eta=global_config['is_eta'],
|
||||||
# end_time=global_config['end_time'],
|
end_time=global_config['end_time'],
|
||||||
# )
|
)
|
||||||
|
|
||||||
# logger.info('模型训练完成')
|
# logger.info('模型训练完成')
|
||||||
|
|
||||||
|
|||||||
Loading…
Reference in New Issue
Block a user