PriceForecast/config_jingbo_yuedu.py

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import logging
import os
import logging.handlers
import datetime
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from lib.tools import MySQLDB, SQLiteHandler
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# eta 接口token
APPID = "XNLDvxZHHugj7wJ7"
SECRET = "iSeU4s6cKKBVbt94htVY1p0sqUMqb2xa"
# 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='
uniquecodedataurl = 'http://10.189.2.78:8108/v1/edb/data?UniqueCode=4991c37becba464609b409909fe4d992&StartDate=2024-02-01'
classifyidlisturl = 'http://10.189.2.78:8108/v1/edb/list?ClassifyId='
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'
edbdeleteurl = 'http://10.189.2.78:8108/v1/edb/business/edb/del'
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',
'C2403083739', 'C2404167878', 'C2403250571', 'lmcads03 lme comdty', 'GC1 COMB Comdty',
'C2404171822', 'C2404167855',
# 'W000825','W000826','G.IPE', # 美国汽柴油
# 'S5131019','ID00135604','FSGAM1 Index','S5120408','ID00136724', # 新加坡汽柴油
]
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# 临时写死用指定的列,与上面的edbcode对应后面更改
edbnamelist = [
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'ds', 'y',
'Brent c1-c6', 'Brent c1-c3', 'Brent-WTI', '美国商业原油库存',
'DFL', '美国汽油裂解价差', 'ovx index', 'dxy curncy', 'lmcads03 lme comdty',
'C2403128043', 'C2403150124', 'FVHCM1 INDEX', 'doedtprd index', 'CFFDQMMN INDEX',
'C2403083739', 'C2404167878',
'GC1 COMB Comdty', 'C2404167855',
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# 'A汽油价格','W000826','ICE柴油价格',
# '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','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'
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]
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# eta自有数据指标编码
modelsindex = {
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'NHITS': 'SELF0000001',
'Informer': 'SELF0000057',
'LSTM': 'SELF0000058',
'iTransformer': 'SELF0000059',
'TSMixer': 'SELF0000060',
'TSMixerx': 'SELF0000061',
'PatchTST': 'SELF0000062',
'RNN': 'SELF0000063',
'GRU': 'SELF0000064',
'TCN': 'SELF0000065',
'BiTCN': 'SELF0000066',
'DilatedRNN': 'SELF0000067',
'MLP': 'SELF0000068',
'DLinear': 'SELF0000069',
'NLinear': 'SELF0000070',
'TFT': 'SELF0000071',
'FEDformer': 'SELF0000072',
'StemGNN': 'SELF0000073',
'MLPMultivariate': 'SELF0000074',
'TiDE': 'SELF0000075',
'DeepNPTS': 'SELF0000076'
}
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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
data = {
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"IndexCode": "",
"IndexName": "价格预测模型",
"Unit": "",
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"Frequency": "日度",
"SourceName": f"价格预测",
"Remark": 'ddd',
"DataList": [
{
"Date": "2024-05-02",
"Value": 333444
}
]
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}
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# eta 分类
# level3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'
# ParentId ":1160, 能源化工
# ClassifyId ":1214,原油
# ParentId ":1214,",就是原油下所有的数据。
ClassifyId = 1214
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# 变量定义--测试环境
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server_host = '192.168.100.53:8080' # 内网
# server_host = '183.242.74.28' # 外网
login_pushreport_url = f"http://{server_host}/jingbo-dev/api/server/login"
upload_url = f"http://{server_host}/jingbo-dev/api/analysis/reportInfo/researchUploadReportSave"
upload_warning_url = f"http://{server_host}/jingbo-dev/api/basicBuiness/crudeOilWarning/save"
query_data_list_item_nos_url = f"http://{server_host}/jingbo-dev/api/warehouse/dwDataItem/queryDataListItemNos"
# 上传数据项值
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push_data_value_list_url = f"http://{server_host}/jingbo-dev/api/dw/dataValue/pushDataValueList"
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login_data = {
"data": {
"account": "api_test",
# "password": "MmVmNzNlOWI0MmY0ZDdjZGUwNzE3ZjFiMDJiZDZjZWU=", # Shihua@123456
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"password": "ZTEwYWRjMzk0OWJhNTlhYmJlNTZlMDU3ZjIwZjg4M2U=", # 123456
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"tenantHashCode": "8a4577dbd919675758d57999a1e891fe",
"terminal": "API"
},
"funcModule": "API",
"funcOperation": "获取token"
}
upload_data = {
"groupNo": '', # 用户组id
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"funcModule": '研究报告信息',
"funcOperation": '上传原油价格预测报告',
"data": {
"ownerAccount": 'arui', # 报告所属用户账号
"reportType": 'OIL_PRICE_FORECAST', # 报告类型固定为OIL_PRICE_FORECAST
"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
"fileBase64": '', # 文件内容base64
"categoryNo": 'yyjgycbg', # 研究报告分类编码
"smartBusinessClassCode": 'YCJGYCBG', # 分析报告分类编码
"reportEmployeeCode": "E40116", # 报告人
"reportDeptCode": "D0044", # 报告部门
"productGroupCode": "RAW_MATERIAL" # 商品分类
}
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}
warning_data = {
"groupNo": '', # 用户组id
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"funcModule": '原油特征停更预警',
"funcOperation": '原油特征停更预警',
"data": {
'WARNING_TYPE_NAME': '特征数据停更预警',
'WARNING_CONTENT': '',
'WARNING_DATE': ''
}
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}
query_data_list_item_nos_data = {
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"funcModule": "数据项",
"funcOperation": "查询",
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"data": {
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"dateStart": "20200101",
"dateEnd": "20241231",
"dataItemNoList": ["Brentzdj", "Brentzgj"] # 数据项编码,代表 brent最低价和最高价
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}
}
push_data_value_list_data = {
"funcModule": "数据表信息列表",
"funcOperation": "新增",
"data": [
{"dataItemNo": "91230600716676129",
"dataDate": "20230113",
"dataStatus": "add",
"dataValue": 100.11
},
{"dataItemNo": "91230600716676129P|ETHYL_BEN|CAPACITY",
"dataDate": "20230113",
"dataStatus": "add",
"dataValue": 100.55
},
{"dataItemNo": "91230600716676129P|ETHYL_BEN|CAPACITY",
"dataDate": "20230113",
"dataStatus": "add",
"dataValue": 100.55
}
]
}
# 八大维度数据项编码
bdwd_items = {
'ciri': 'yyycbdwdcr',
'benzhou': 'yyycbdwdbz',
'cizhou': 'yyycbdwdcz',
'gezhou': 'yyycbdwdgz',
'ciyue': 'yyycbdwdcy',
'cieryue': 'yyycbdwdcey',
'cisanyue': 'yyycbdwdcsy',
'cisiyue': 'yyycbdwdcsiy',
}
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# 北京环境数据库
host = '192.168.101.27'
port = 3306
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dbusername = 'root'
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password = '123456'
dbname = 'jingbo_test'
table_name = 'v_tbl_crude_oil_warning'
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# 开关
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is_train = True # 是否训练
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is_debug = False # 是否调试
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
is_timefurture = True # 是否使用时间特征
is_fivemodels = False # 是否使用之前保存的最佳的5个模型
is_edbcode = False # 特征使用edbcoding列表中的
is_edbnamelist = False # 自定义特征对应上面的edbnamelist
is_update_eta = False # 预测结果上传到eta
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is_update_report = True # 是否上传报告
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is_update_warning_data = False # 是否上传预警数据
is_update_predict_value = True # 是否上传预测值到市场信息平台
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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
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 = 2000 # 数据开始年份
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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 "WW" 自定义周
delweekenday = True if freq == 'B' else False # 是否删除周末数据
is_corr = False # 特征是否参与滞后领先提升相关系数
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
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
horizon = 4 # 预测的步长
input_size = 16 # 输入序列长度
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
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
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
rote = 0.06 # 绘图上下界阈值
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# 计算准确率
weight_dict = [0.4, 0.15, 0.1, 0.1, 0.25] # 权重
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# 文件
data_set = '原油指标数据.xlsx' # 数据集文件
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')
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
# 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
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
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if end_time == '':
end_time = now
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# 邮件配置
username = '1321340118@qq.com'
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'
content = 'brent价格预测报告请看附件'
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)
log_dir = 'logs'
if not os.path.exists(log_dir):
os.makedirs(log_dir)
# 配置日志记录器
logger = logging.getLogger('my_logger')
logger.setLevel(logging.INFO)
# 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)
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
console_handler = logging.StreamHandler()
console_handler.setFormatter(logging.Formatter('%(message)s'))
# 将处理器添加到日志记录器
logger.addHandler(file_handler)
logger.addHandler(console_handler)
# logger.info('当前配置:'+settings)