原油月度
This commit is contained in:
jingboyitiji
parent
f40d928caa
commit
dd3538a4e9
1
.gitignore
vendored
1
.gitignore
vendored
@ -13,6 +13,7 @@ build/
|
|||||||
dataset/
|
dataset/
|
||||||
yuanyoudataset/
|
yuanyoudataset/
|
||||||
yuanyouzhoududataset/
|
yuanyouzhoududataset/
|
||||||
|
yuanyouyuedudataset/
|
||||||
juxitingdataset/
|
juxitingdataset/
|
||||||
logs/
|
logs/
|
||||||
develop-eggs/
|
develop-eggs/
|
||||||
|
|||||||
268
config_jingbo_yuedu.py
Normal file
268
config_jingbo_yuedu.py
Normal file
@ -0,0 +1,268 @@
|
|||||||
|
import logging
|
||||||
|
import os
|
||||||
|
import logging.handlers
|
||||||
|
import datetime
|
||||||
|
from lib.tools import MySQLDB,SQLiteHandler
|
||||||
|
|
||||||
|
|
||||||
|
# eta 接口token
|
||||||
|
APPID = "XNLDvxZHHugj7wJ7"
|
||||||
|
SECRET = "iSeU4s6cKKBVbt94htVY1p0sqUMqb2xa"
|
||||||
|
|
||||||
|
# eta 接口url
|
||||||
|
sourcelisturl = 'http://10.189.2.78:8108/v1/edb/source/list'
|
||||||
|
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='
|
||||||
|
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'
|
||||||
|
edbcodelist = ['CO1 Comdty', 'ovx index', 'C2404194834', 'C2404199738', 'dxy curncy', 'C2403128043', 'C2403150124',
|
||||||
|
'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
|
||||||
|
]
|
||||||
|
|
||||||
|
# 临时写死用指定的列,与上面的edbcode对应，后面更改
|
||||||
|
edbnamelist = [
|
||||||
|
'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',
|
||||||
|
# '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（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）'
|
||||||
|
]
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
# eta自有数据指标编码
|
||||||
|
modelsindex = {
|
||||||
|
'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'
|
||||||
|
}
|
||||||
|
|
||||||
|
# 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 = {
|
||||||
|
"IndexCode": "",
|
||||||
|
"IndexName": "价格预测模型",
|
||||||
|
"Unit": "无",
|
||||||
|
"Frequency": "日度",
|
||||||
|
"SourceName": f"价格预测",
|
||||||
|
"Remark": 'ddd',
|
||||||
|
"DataList": [
|
||||||
|
{
|
||||||
|
"Date": "2024-05-02",
|
||||||
|
"Value": 333444
|
||||||
|
}
|
||||||
|
]
|
||||||
|
}
|
||||||
|
|
||||||
|
# eta 分类
|
||||||
|
# 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
|
||||||
|
# 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
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
############################################################################################################### 变量定义--测试环境
|
||||||
|
server_host = '192.168.100.53'
|
||||||
|
|
||||||
|
login_pushreport_url = f"http://{server_host}:8080/jingbo-dev/api/server/login"
|
||||||
|
upload_url = f"http://{server_host}:8080/jingbo-dev/api/analysis/reportInfo/researchUploadReportSave"
|
||||||
|
upload_warning_url = f"http://{server_host}:8080/jingbo-dev/api/basicBuiness/crudeOilWarning/save"
|
||||||
|
query_data_list_item_nos_url = f"http://{server_host}:8080/jingbo-dev/api/warehouse/dwDataItem/queryDataListItemNos"
|
||||||
|
|
||||||
|
login_data = {
|
||||||
|
"data": {
|
||||||
|
"account": "api_test",
|
||||||
|
# "password": "MmVmNzNlOWI0MmY0ZDdjZGUwNzE3ZjFiMDJiZDZjZWU=", # Shihua@123456
|
||||||
|
"password": "ZTEwYWRjMzk0OWJhNTlhYmJlNTZlMDU3ZjIwZjg4M2U=", # 123456
|
||||||
|
"tenantHashCode": "8a4577dbd919675758d57999a1e891fe",
|
||||||
|
"terminal": "API"
|
||||||
|
},
|
||||||
|
"funcModule": "API",
|
||||||
|
"funcOperation": "获取token"
|
||||||
|
}
|
||||||
|
|
||||||
|
upload_data = {
|
||||||
|
"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
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
||||||
|
|
||||||
|
warning_data = {
|
||||||
|
"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':''
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
||||||
|
query_data_list_item_nos_data = {
|
||||||
|
"funcModule": "数据项",
|
||||||
|
"funcOperation": "查询",
|
||||||
|
"data": {
|
||||||
|
"dateStart":"20200101",
|
||||||
|
"dateEnd":"20241231",
|
||||||
|
"dataItemNoList":["Brentzdj","Brentzgj"] # 数据项编码,代表 brent最低价和最高价
|
||||||
|
}
|
||||||
|
}
|
||||||
|
|
||||||
|
|
||||||
|
# 北京环境数据库
|
||||||
|
host = '192.168.101.27'
|
||||||
|
port = 3306
|
||||||
|
dbusername ='root'
|
||||||
|
password = '123456'
|
||||||
|
dbname = 'jingbo_test'
|
||||||
|
table_name = 'v_tbl_crude_oil_warning'
|
||||||
|
|
||||||
|
|
||||||
|
### 开关
|
||||||
|
is_train = True # 是否训练
|
||||||
|
is_debug = False # 是否调试
|
||||||
|
is_eta = True # 是否使用eta接口
|
||||||
|
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
|
||||||
|
is_update_report = False # 是否上传报告
|
||||||
|
is_update_warning_data = False # 是否上传预警数据
|
||||||
|
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
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
# 连接到数据库
|
||||||
|
db_mysql = MySQLDB(host=host, user=dbusername, password=password, database=dbname)
|
||||||
|
db_mysql.connect()
|
||||||
|
print("数据库连接成功",host,dbname,dbusername)
|
||||||
|
|
||||||
|
|
||||||
|
# 数据截取日期
|
||||||
|
start_year = 2020 # 数据开始年份
|
||||||
|
end_time = '' # 数据截取日期
|
||||||
|
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
|
||||||
|
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
|
||||||
|
if add_kdj and is_edbnamelist:
|
||||||
|
edbnamelist = edbnamelist+['K','D','J']
|
||||||
|
|
||||||
|
### 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 =3 # 预测的步长
|
||||||
|
input_size = 9 # 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
|
||||||
|
val_check_steps = 30 # 评估频率
|
||||||
|
early_stop_patience_steps = 5 # 早停的耐心步数
|
||||||
|
# --- 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
|
||||||
|
val_size = test_size # 验证集大小,同测试集大小
|
||||||
|
|
||||||
|
### 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
|
||||||
|
|
||||||
|
### 计算准确率
|
||||||
|
weight_dict = [0.4,0.15,0.1,0.1,0.25] # 权重
|
||||||
|
|
||||||
|
|
||||||
|
### 文件
|
||||||
|
data_set = '原油指标数据.xlsx' # 数据集文件
|
||||||
|
dataset = 'yuanyouyuedudataset' # 数据集文件夹
|
||||||
|
|
||||||
|
# 数据库名称
|
||||||
|
db_name = os.path.join(dataset,'jbsh_yuanyou_yuedu.db')
|
||||||
|
sqlitedb = SQLiteHandler(db_name)
|
||||||
|
sqlitedb.connect()
|
||||||
|
|
||||||
|
settings = f'{input_size}-{horizon}-{train_steps}--{k}-{data_set}-{y}'
|
||||||
|
# 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') # 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 = reportname.replace(':', '-') # 替换冒号
|
||||||
|
if end_time == '':
|
||||||
|
end_time = now
|
||||||
|
### 邮件配置
|
||||||
|
username='1321340118@qq.com'
|
||||||
|
passwd='wgczgyhtyyyyjghi'
|
||||||
|
# recv=['liurui_test@163.com','52585119@qq.com']
|
||||||
|
recv=['liurui_test@163.com','jin.wang@chambroad.com']
|
||||||
|
# recv=['liurui_test@163.com']
|
||||||
|
title='reportname'
|
||||||
|
content='brent价格预测报告请看附件'
|
||||||
|
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')
|
||||||
|
ssl=True
|
||||||
|
|
||||||
|
|
||||||
|
### 日志配置
|
||||||
|
|
||||||
|
# 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
|
||||||
|
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'))
|
||||||
|
|
||||||
|
# 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)
|
||||||
|
|
||||||
298
main_yuanyou_yuedu.py
Normal file
298
main_yuanyou_yuedu.py
Normal file
@ -0,0 +1,298 @@
|
|||||||
|
# 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
|
||||||
|
import torch
|
||||||
|
torch.set_float32_matmul_precision("high")
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
def predict_main():
|
||||||
|
"""
|
||||||
|
主预测函数，用于从 ETA 获取数据、处理数据、训练模型并进行预测。
|
||||||
|
|
||||||
|
参数:
|
||||||
|
signature (BinanceAPI): Binance API 实例。
|
||||||
|
etadata (EtaReader): ETA 数据读取器实例。
|
||||||
|
is_eta (bool): 是否从 ETA 获取数据。
|
||||||
|
data_set (str): 数据集名称。
|
||||||
|
dataset (str): 数据集路径。
|
||||||
|
add_kdj (bool): 是否添加 KDJ 指标。
|
||||||
|
is_timefurture (bool): 是否添加时间衍生特征。
|
||||||
|
end_time (str): 结束时间。
|
||||||
|
is_edbnamelist (bool): 是否使用 EDB 名称列表。
|
||||||
|
edbnamelist (list): EDB 名称列表。
|
||||||
|
y (str): 预测目标列名。
|
||||||
|
sqlitedb (SQLiteDB): SQLite 数据库实例。
|
||||||
|
is_corr (bool): 是否进行相关性分析。
|
||||||
|
horizon (int): 预测时域。
|
||||||
|
input_size (int): 输入数据大小。
|
||||||
|
train_steps (int): 训练步数。
|
||||||
|
val_check_steps (int): 验证检查步数。
|
||||||
|
early_stop_patience_steps (int): 早停耐心步数。
|
||||||
|
is_debug (bool): 是否调试模式。
|
||||||
|
dataset (str): 数据集名称。
|
||||||
|
is_train (bool): 是否训练模型。
|
||||||
|
is_fivemodels (bool): 是否使用五个模型。
|
||||||
|
val_size (float): 验证集大小。
|
||||||
|
test_size (float): 测试集大小。
|
||||||
|
settings (dict): 模型设置。
|
||||||
|
now (str): 当前时间。
|
||||||
|
etadata (EtaReader): ETA 数据读取器实例。
|
||||||
|
modelsindex (list): 模型索引列表。
|
||||||
|
data (str): 数据类型。
|
||||||
|
is_eta (bool): 是否从 ETA 获取数据。
|
||||||
|
|
||||||
|
返回:
|
||||||
|
None
|
||||||
|
"""
|
||||||
|
global end_time
|
||||||
|
signature = BinanceAPI(APPID, SECRET)
|
||||||
|
etadata = EtaReader(signature=signature,
|
||||||
|
classifylisturl=classifylisturl,
|
||||||
|
classifyidlisturl=classifyidlisturl,
|
||||||
|
edbcodedataurl=edbcodedataurl,
|
||||||
|
edbcodelist=edbcodelist,
|
||||||
|
edbdatapushurl=edbdatapushurl,
|
||||||
|
edbdeleteurl=edbdeleteurl,
|
||||||
|
edbbusinessurl=edbbusinessurl
|
||||||
|
)
|
||||||
|
# 获取数据
|
||||||
|
if is_eta:
|
||||||
|
logger.info('从eta获取数据...')
|
||||||
|
signature = BinanceAPI(APPID, SECRET)
|
||||||
|
etadata = EtaReader(signature=signature,
|
||||||
|
classifylisturl=classifylisturl,
|
||||||
|
classifyidlisturl=classifyidlisturl,
|
||||||
|
edbcodedataurl=edbcodedataurl,
|
||||||
|
edbcodelist=edbcodelist,
|
||||||
|
edbdatapushurl=edbdatapushurl,
|
||||||
|
edbdeleteurl=edbdeleteurl,
|
||||||
|
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
|
||||||
|
|
||||||
|
if is_market:
|
||||||
|
logger.info('从市场信息平台获取数据...')
|
||||||
|
try:
|
||||||
|
# 如果是测试环境，最高价最低价取excel文档
|
||||||
|
if server_host == '192.168.100.53':
|
||||||
|
logger.info('从excel文档获取最高价最低价')
|
||||||
|
df_zhibiaoshuju = get_high_low_data(df_zhibiaoshuju)
|
||||||
|
else:
|
||||||
|
logger.info('从市场信息平台获取数据')
|
||||||
|
df_zhibiaoshuju = get_market_data(end_time,df_zhibiaoshuju)
|
||||||
|
|
||||||
|
except :
|
||||||
|
logger.info('最高最低价拼接失败')
|
||||||
|
|
||||||
|
# 保存到xlsx文件的sheet表
|
||||||
|
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_zhibiaoliebiao.to_excel(file, sheet_name='指标列表', index=False)
|
||||||
|
|
||||||
|
|
||||||
|
# 数据处理
|
||||||
|
df = datachuli(df_zhibiaoshuju, df_zhibiaoliebiao, y=y, dataset=dataset, add_kdj=add_kdj, is_timefurture=is_timefurture,
|
||||||
|
end_time=end_time)
|
||||||
|
|
||||||
|
else:
|
||||||
|
# 读取数据
|
||||||
|
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,
|
||||||
|
is_timefurture=is_timefurture, end_time=end_time) # 原始数据，未处理
|
||||||
|
|
||||||
|
# 更改预测列名称
|
||||||
|
df.rename(columns={y: 'y'}, inplace=True)
|
||||||
|
|
||||||
|
if is_edbnamelist:
|
||||||
|
df = df[edbnamelist]
|
||||||
|
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
|
||||||
|
# 取第一行数据存储到数据库中
|
||||||
|
first_row = df[['ds', 'y']].tail(1)
|
||||||
|
# 判断y的类型是否为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')
|
||||||
|
return None
|
||||||
|
|
||||||
|
# 将最新真实值保存到数据库
|
||||||
|
if not sqlitedb.check_table_exists('trueandpredict'):
|
||||||
|
first_row.to_sql('trueandpredict', sqlitedb.connection, index=False)
|
||||||
|
else:
|
||||||
|
for row in first_row.itertuples(index=False):
|
||||||
|
row_dict = row._asdict()
|
||||||
|
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:
|
||||||
|
set_clause = ", ".join([f"{key} = '{value}'" for key, value in row_dict.items()])
|
||||||
|
sqlitedb.update_data('trueandpredict', set_clause, where_condition=f"ds = '{row.ds}'")
|
||||||
|
continue
|
||||||
|
sqlitedb.insert_data('trueandpredict', tuple(row_dict.values()), columns=row_dict.keys())
|
||||||
|
|
||||||
|
# 更新accuracy表的y值
|
||||||
|
if not sqlitedb.check_table_exists('accuracy'):
|
||||||
|
pass
|
||||||
|
else:
|
||||||
|
update_y = sqlitedb.select_data('accuracy',where_condition="y is null")
|
||||||
|
if len(update_y) > 0:
|
||||||
|
logger.info('更新accuracy表的y值')
|
||||||
|
# 找到update_y 中ds且df中的y的行
|
||||||
|
update_y = update_y[update_y['ds']<=end_time]
|
||||||
|
logger.info(f'要更新y的信息：{update_y}')
|
||||||
|
# try:
|
||||||
|
for row in update_y.itertuples(index=False):
|
||||||
|
try:
|
||||||
|
row_dict = row._asdict()
|
||||||
|
yy = df[df['ds']==row_dict['ds']]['y'].values[0]
|
||||||
|
LOW = df[df['ds']==row_dict['ds']]['Brentzdj'].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']}'")
|
||||||
|
except:
|
||||||
|
logger.info(f'更新accuracy表的y值失败：{row_dict}')
|
||||||
|
# except Exception as 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
|
||||||
|
is_weekday = datetime.datetime.now().weekday() == 0
|
||||||
|
if is_weekday:
|
||||||
|
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
|
||||||
|
model_results = sqlitedb.select_data('trueandpredict', order_by="ds DESC", limit="60")
|
||||||
|
# 删除空值率为90%以上的列
|
||||||
|
if len(model_results) > 10:
|
||||||
|
model_results = model_results.dropna(thresh=len(model_results)*0.1,axis=1)
|
||||||
|
# 删除空行
|
||||||
|
model_results = model_results.dropna()
|
||||||
|
modelnames = model_results.columns.to_list()[2:-1]
|
||||||
|
for col in model_results[modelnames].select_dtypes(include=['object']).columns:
|
||||||
|
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
|
||||||
|
for model in modelnames:
|
||||||
|
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
|
||||||
|
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
|
||||||
|
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
|
||||||
|
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
|
||||||
|
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}")
|
||||||
|
# 保存结果到数据库
|
||||||
|
if not sqlitedb.check_table_exists('most_model'):
|
||||||
|
sqlitedb.create_table('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:
|
||||||
|
if is_weekday:
|
||||||
|
# if True:
|
||||||
|
logger.info('今天是周一，发送特征预警')
|
||||||
|
# 上传预警信息到数据库
|
||||||
|
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']]
|
||||||
|
# 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'})
|
||||||
|
from sqlalchemy import create_engine
|
||||||
|
import urllib
|
||||||
|
global password
|
||||||
|
if '@' in password:
|
||||||
|
password = urllib.parse.quote_plus(password)
|
||||||
|
|
||||||
|
engine = create_engine(f'mysql+pymysql://{dbusername}:{password}@{host}:{port}/{dbname}')
|
||||||
|
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
|
||||||
|
existing_data = pd.read_sql(f"SELECT * FROM {table_name}", engine)
|
||||||
|
if not existing_data.empty:
|
||||||
|
max_id = existing_data['ID'].astype(int).max()
|
||||||
|
warning_data_df['ID'] = range(max_id + 1, max_id + 1 + len(warning_data_df))
|
||||||
|
else:
|
||||||
|
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)
|
||||||
|
if is_update_warning_data:
|
||||||
|
upload_warning_info(len(warning_data_df))
|
||||||
|
except:
|
||||||
|
logger.info('上传预警信息到数据库失败')
|
||||||
|
|
||||||
|
if is_corr:
|
||||||
|
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
|
||||||
|
logger.info(f"开始训练模型...")
|
||||||
|
row, col = df.shape
|
||||||
|
|
||||||
|
now = datetime.datetime.now().strftime('%Y%m%d%H%M%S')
|
||||||
|
ex_Model(df,
|
||||||
|
horizon=horizon,
|
||||||
|
input_size=input_size,
|
||||||
|
train_steps=train_steps,
|
||||||
|
val_check_steps=val_check_steps,
|
||||||
|
early_stop_patience_steps=early_stop_patience_steps,
|
||||||
|
is_debug=is_debug,
|
||||||
|
dataset=dataset,
|
||||||
|
is_train=is_train,
|
||||||
|
is_fivemodels=is_fivemodels,
|
||||||
|
val_size=val_size,
|
||||||
|
test_size=test_size,
|
||||||
|
settings=settings,
|
||||||
|
now=now,
|
||||||
|
etadata=etadata,
|
||||||
|
modelsindex=modelsindex,
|
||||||
|
data=data,
|
||||||
|
is_eta=is_eta,
|
||||||
|
end_time=end_time,
|
||||||
|
)
|
||||||
|
|
||||||
|
|
||||||
|
logger.info('模型训练完成')
|
||||||
|
|
||||||
|
logger.info('训练数据绘图ing')
|
||||||
|
model_results3 = model_losss(sqlitedb,end_time=end_time)
|
||||||
|
logger.info('训练数据绘图end')
|
||||||
|
|
||||||
|
# 模型报告
|
||||||
|
logger.info('制作报告ing')
|
||||||
|
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 = reportname.replace(':', '-') # 替换冒号
|
||||||
|
brent_export_pdf(dataset=dataset,num_models = 5 if is_fivemodels else 22,time=end_time,
|
||||||
|
reportname=reportname,sqlitedb=sqlitedb),
|
||||||
|
|
||||||
|
logger.info('制作报告end')
|
||||||
|
logger.info('模型训练完成')
|
||||||
|
|
||||||
|
# # LSTM 单变量模型
|
||||||
|
# ex_Lstm(df,input_seq_len=input_size,output_seq_len=horizon,is_debug=is_debug,dataset=dataset)
|
||||||
|
|
||||||
|
# # lstm 多变量模型
|
||||||
|
# ex_Lstm_M(df,n_days=input_size,out_days=horizon,is_debug=is_debug,datasetpath=dataset)
|
||||||
|
|
||||||
|
# # GRU 模型
|
||||||
|
# # ex_GRU(df)
|
||||||
|
|
||||||
|
# 发送邮件
|
||||||
|
m = SendMail(
|
||||||
|
username=username,
|
||||||
|
passwd=passwd,
|
||||||
|
recv=recv,
|
||||||
|
title=title,
|
||||||
|
content=content,
|
||||||
|
file=max(glob.glob(os.path.join(dataset,'*.pdf')), key=os.path.getctime),
|
||||||
|
ssl=ssl,
|
||||||
|
)
|
||||||
|
# m.send_mail()
|
||||||
|
|
||||||
|
|
||||||
|
if __name__ == '__main__':
|
||||||
|
global end_time
|
||||||
|
is_on = True
|
||||||
|
# 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', '2025-2-1', freq='ME'):
|
||||||
|
end_time = i_time.strftime('%Y-%m-%d')
|
||||||
|
predict_main()
|
||||||
|
|
||||||
|
# predict_main()
|
||||||
@ -275,15 +275,15 @@ 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()
|
||||||
|
|
||||||
|
|
||||||
|
|||||||
@ -187,10 +187,10 @@ def ex_Model(df,horizon,input_size,train_steps,val_check_steps,early_stop_patien
|
|||||||
filename = max(glob.glob(os.path.join(dataset,'*.joblib')), key=os.path.getctime)
|
filename = max(glob.glob(os.path.join(dataset,'*.joblib')), key=os.path.getctime)
|
||||||
logger.info('读取模型：'+ filename)
|
logger.info('读取模型：'+ filename)
|
||||||
nf = load(filename)
|
nf = load(filename)
|
||||||
# # 测试集预测
|
# 测试集预测
|
||||||
# nf_test_preds = nf.cross_validation(df=df_test, val_size=val_size, test_size=test_size, n_windows=None)
|
nf_test_preds = nf.cross_validation(df=df_test, val_size=val_size, test_size=test_size, n_windows=None)
|
||||||
# # 测试集预测结果保存
|
# 测试集预测结果保存
|
||||||
# nf_test_preds.to_csv(os.path.join(dataset,"cross_validation.csv"),index=False)
|
nf_test_preds.to_csv(os.path.join(dataset,"cross_validation.csv"),index=False)
|
||||||
|
|
||||||
df_test['ds'] = pd.to_datetime(df_test['ds'], errors='coerce')
|
df_test['ds'] = pd.to_datetime(df_test['ds'], errors='coerce')
|
||||||
|
|
||||||
|
|||||||
Loading…
Reference in New Issue
Block a user