268 lines
10 KiB
Python
268 lines
10 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 = ['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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'C2403083739', 'C2404167878', 'C2403250571', 'lmcads03 lme comdty', 'GC1 COMB Comdty',
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'C2404171822', 'C2404167855',
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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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]
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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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'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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'C2403128043', 'C2403150124', 'FVHCM1 INDEX', 'doedtprd index', 'CFFDQMMN INDEX',
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'C2403083739', 'C2404167878',
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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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# '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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# eta自有数据指标编码
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modelsindex = {
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'NHITS': 'SELF0000001',
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'Informer': 'SELF0000057',
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'LSTM': 'SELF0000058',
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'iTransformer': 'SELF0000059',
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'TSMixer': 'SELF0000060',
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'TSMixerx': 'SELF0000061',
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'PatchTST': 'SELF0000062',
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'RNN': 'SELF0000063',
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'GRU': 'SELF0000064',
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'TCN': 'SELF0000065',
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'BiTCN': 'SELF0000066',
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'DilatedRNN': 'SELF0000067',
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'MLP': 'SELF0000068',
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'DLinear': 'SELF0000069',
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'NLinear': 'SELF0000070',
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'TFT': 'SELF0000071',
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'FEDformer': 'SELF0000072',
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'StemGNN': 'SELF0000073',
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'MLPMultivariate': 'SELF0000074',
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'TiDE': 'SELF0000075',
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'DeepNPTS': 'SELF0000076'
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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 = 1214
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# 变量定义--测试环境
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server_host = '192.168.100.53'
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login_pushreport_url = f"http://{server_host}:8080/jingbo-dev/api/server/login"
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upload_url = f"http://{server_host}:8080/jingbo-dev/api/analysis/reportInfo/researchUploadReportSave"
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upload_warning_url = f"http://{server_host}:8080/jingbo-dev/api/basicBuiness/crudeOilWarning/save"
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query_data_list_item_nos_url = f"http://{server_host}:8080/jingbo-dev/api/warehouse/dwDataItem/queryDataListItemNos"
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login_data = {
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"data": {
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"account": "api_test",
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# "password": "MmVmNzNlOWI0MmY0ZDdjZGUwNzE3ZjFiMDJiZDZjZWU=", # Shihua@123456
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"password": "ZTEwYWRjMzk0OWJhNTlhYmJlNTZlMDU3ZjIwZjg4M2U=", # 123456
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"tenantHashCode": "8a4577dbd919675758d57999a1e891fe",
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"terminal": "API"
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},
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"funcModule": "API",
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"funcOperation": "获取token"
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}
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upload_data = {
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"funcModule": '研究报告信息',
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"funcOperation": '上传原油价格预测报告',
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"data": {
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"ownerAccount": 'arui', # 报告所属用户账号
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"reportType": 'OIL_PRICE_FORECAST', # 报告类型，固定为OIL_PRICE_FORECAST
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"fileName": '2000-40-5-50--100-原油指标数据.xlsx-Brent活跃合约--2024-09-06-15-01-29-预测报告.pdf', # 文件名称
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"fileBase64": '', # 文件内容base64
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"categoryNo": 'yyjgycbg', # 研究报告分类编码
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"smartBusinessClassCode": 'YCJGYCBG', # 分析报告分类编码
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"reportEmployeeCode": "E40116", # 报告人
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"reportDeptCode": "D0044", # 报告部门
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"productGroupCode": "RAW_MATERIAL" # 商品分类
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}
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}
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warning_data = {
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"funcModule": '原油特征停更预警',
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"funcOperation": '原油特征停更预警',
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"data": {
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'WARNING_TYPE_NAME': '特征数据停更预警',
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'WARNING_CONTENT': '',
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'WARNING_DATE': ''
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}
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}
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query_data_list_item_nos_data = {
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"funcModule": "数据项",
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"funcOperation": "查询",
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"data": {
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"dateStart": "20200101",
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"dateEnd": "20241231",
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"dataItemNoList": ["Brentzdj", "Brentzgj"] # 数据项编码,代表 brent最低价和最高价
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}
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}
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# 北京环境数据库
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host = '192.168.101.27'
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port = 3306
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dbusername = 'root'
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password = '123456'
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dbname = 'jingbo_test'
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table_name = 'v_tbl_crude_oil_warning'
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# 开关
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is_train = False # 是否训练
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is_debug = False # 是否调试
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is_eta = True # 是否使用eta接口
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is_market = True # 是否通过市场信息平台获取特征 ,在is_eta 为true 的情况下生效
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is_timefurture = True # 是否使用时间特征
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is_fivemodels = False # 是否使用之前保存的最佳的5个模型
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is_edbcode = False # 特征使用edbcoding列表中的
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is_edbnamelist = False # 自定义特征，对应上面的edbnamelist
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is_update_eta = False # 预测结果上传到eta
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is_update_report = False # 是否上传报告
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is_update_warning_data = False # 是否上传预警数据
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is_del_corr = 0.6 # 是否删除相关性高的特征,取值为 0-1 ，0 为不删除，0.6 表示删除相关性小于0.6的特征
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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,
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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 = 2000 # 数据开始年份
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end_time = '2023-3-1' # 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 "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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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 = '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 = 4 # 预测的步长
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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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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 = '原油指标数据.xlsx' # 数据集文件
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dataset = 'yuanyouyuedudataset' # 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_yuanyou_yuedu.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'Brent原油大模型月度预测--{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', 'jin.wang@chambroad.com']
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# recv=['liurui_test@163.com']
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title = 'reportname'
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content = 'brent价格预测报告请看附件'
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file = os.path.join(dataset, 'reportname')
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# 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(
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log_dir, 'pricepredict.log'), maxBytes=1024 * 1024, backupCount=5)
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file_handler.setFormatter(logging.Formatter(
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'%(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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