石油焦配置调整
This commit is contained in:
jingboyitiji
parent
409cbb65e7
commit
7788cfda6f
@ -137,6 +137,142 @@ data = {
|
||||
ClassifyId = 1161
|
||||
|
||||
|
||||
|
||||
# 变量定义--线上环境
|
||||
server_host = '10.200.32.39'
|
||||
login_pushreport_url = "http://10.200.32.39/jingbo-api/api/server/login"
|
||||
upload_url = "http://10.200.32.39/jingbo-api/api/analysis/reportInfo/researchUploadReportSave"
|
||||
upload_warning_url = "http://10.200.32.39/jingbo-api/api/basicBuiness/crudeOilWarning/save"
|
||||
query_data_list_item_nos_url = f"http://{server_host}/jingbo-api/api/warehouse/dwDataItem/queryDataListItemNos"
|
||||
# 上传数据项值
|
||||
push_data_value_list_url = f"http://{server_host}/jingbo-api/api/dw/dataValue/pushDataValueList"
|
||||
上传停更数据到市场信息平台
|
||||
push_waring_data_value_list_url = f"http://{server_host}:8080/jingbo-dev/api/basicBuiness/crudeOilWarning/crudeSaveOrupdate"
|
||||
获取预警数据中取消订阅指标ID
|
||||
get_waring_data_value_list_url = f"http://{server_host}:8080/jingbo-dev/api/basicBuiness/crudeOilWarning/dataList"
|
||||
|
||||
|
||||
login_data = {
|
||||
"data": {
|
||||
"account": "api_dev",
|
||||
"password": "ZTEwYWRjMzk0OWJhNTlhYmJlNTZlMDU3ZjIwZjg4M2U=",
|
||||
"tenantHashCode": "8a4577dbd919675758d57999a1e891fe",
|
||||
"terminal": "API"
|
||||
},
|
||||
"funcModule": "API",
|
||||
"funcOperation": "获取token"
|
||||
}
|
||||
|
||||
|
||||
upload_data = {
|
||||
"funcModule":'研究报告信息',
|
||||
"funcOperation":'上传聚烯烃PP价格预测报告',
|
||||
"data":{
|
||||
"groupNo":'000211' # 用户组编号
|
||||
"ownerAccount":'36541', #报告所属用户账号 36541 - 贾青雪
|
||||
"reportType":'OIL_PRICE_FORECAST', # 报告类型，固定为OIL_PRICE_FORECAST
|
||||
"fileName": '', #文件名称
|
||||
"fileBase64": '' ,#文件内容base64
|
||||
"categoryNo":'jxtjgycbg', # 研究报告分类编码
|
||||
"smartBusinessClassCode":'JXTJGYCBG', #分析报告分类编码
|
||||
"reportEmployeeCode":"E40482" ,# 报告人 E40482 - 管理员 0000027663 - 刘小朋
|
||||
"reportDeptCode" :"JXTJGYCBG", # 报告部门 - 002000621000 SH期货研究部
|
||||
"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": {
|
||||
"groupNo": "000211",
|
||||
'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
|
||||
}
|
||||
}
|
||||
|
||||
|
||||
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
|
||||
}
|
||||
]
|
||||
}
|
||||
|
||||
|
||||
push_waring_data_value_list_data = {
|
||||
"data": {
|
||||
"crudeOilWarningDtoList": [
|
||||
{
|
||||
"lastUpdateDate": "20240501",
|
||||
"updateSuspensionCycle": 1,
|
||||
"dataSource": "9",
|
||||
"frequency": "1",
|
||||
"indicatorName": "美元指数",
|
||||
"indicatorId": "myzs001",
|
||||
"warningDate": "2024-05-13"
|
||||
}
|
||||
],
|
||||
"dataSource": "9"
|
||||
},
|
||||
"funcModule": "商品数据同步",
|
||||
"funcOperation": "同步"
|
||||
}
|
||||
|
||||
|
||||
get_waring_data_value_list_data = {
|
||||
"data": "9", "funcModule": "商品数据同步", "funcOperation": "同步"}
|
||||
|
||||
|
||||
# 八大维度数据项编码
|
||||
bdwd_items = {
|
||||
'ciri': '91371600MAC3TYFN6M|LSBM00007|FORECAST_PRICE',
|
||||
'benzhou': '91371600MAC3TYFN6M|LSBM00007|FORECAST_PRICE01',
|
||||
'cizhou': '91371600MAC3TYFN6M|LSBM00007|FORECAST_PRICE02',
|
||||
'gezhou': '91371600MAC3TYFN6M|LSBM00007|FORECAST_PRICE03',
|
||||
'ciyue': '91371600MAC3TYFN6M|LSBM00007|FORECAST_PRICE04',
|
||||
'cieryue': '91371600MAC3TYFN6M|LSBM00007|FORECAST_PRICE05',
|
||||
'cisanyue': '91371600MAC3TYFN6M|LSBM00007|FORECAST_PRICE06',
|
||||
'cisiyue': '91371600MAC3TYFN6M|LSBM00007|FORECAST_PRICE07',
|
||||
}
|
||||
|
||||
|
||||
# 生产环境数据库
|
||||
host = 'rm-2zehj3r1n60ttz9x5.mysql.rds.aliyuncs.com'
|
||||
port = 3306
|
||||
dbusername ='jingbo'
|
||||
password = 'shihua@123'
|
||||
dbname = 'jingbo'
|
||||
table_name = 'v_tbl_crude_oil_warning'
|
||||
|
||||
|
||||
|
||||
# 变量定义--测试环境
|
||||
server_host = '192.168.100.53' # 内网
|
||||
# server_host = '183.242.74.28' # 外网
|
||||
|
||||
@ -295,16 +295,17 @@ push_data_value_list_data = {
|
||||
}
|
||||
]
|
||||
}
|
||||
|
||||
# 八大维度数据项编码
|
||||
bdwd_items = {
|
||||
'ciri': 'syjlyycbdwdcr',
|
||||
'benzhou': 'syjlyycbdwdbz',
|
||||
'cizhou': 'syjlyycbdwdcz',
|
||||
'gezhou': 'syjlyycbdwdgz',
|
||||
'ciyue': 'syjlyycbdwdcy',
|
||||
'cieryue': 'syjlyycbdwdcey',
|
||||
'cisanyue': 'syjlyycbdwdcsy',
|
||||
'cisiyue': 'syjlyycbdwdcsiy',
|
||||
'cizhou': 'syj|nextweek|price',
|
||||
'gezhou': 'syj|next-one-week|price',
|
||||
'ciyue': 'syj|next-month|price',
|
||||
'cieryue': 'syj|next-one-month|price',
|
||||
'cisanyue': 'yj|next-two-month|price',
|
||||
'cisiyue': 'yj|next-three-month|price',
|
||||
}
|
||||
|
||||
# 北京环境数据库
|
||||
|
||||
@ -466,21 +466,23 @@ push_data_value_list_data = {
|
||||
}
|
||||
]
|
||||
}
|
||||
|
||||
# 八大维度数据项编码
|
||||
bdwd_items = {
|
||||
'ciri': 'syjlyycbdwdcr',
|
||||
'benzhou': 'syjlyycbdwdbz',
|
||||
'cizhou': 'syjlyycbdwdcz',
|
||||
'gezhou': 'syjlyycbdwdgz',
|
||||
'ciyue': 'syjlyycbdwdcy',
|
||||
'cieryue': 'syjlyycbdwdcey',
|
||||
'cisanyue': 'syjlyycbdwdcsy',
|
||||
'cisiyue': 'syjlyycbdwdcsiy',
|
||||
'cizhou': 'syj|nextweek|price',
|
||||
'gezhou': 'syj|next-one-week|price',
|
||||
'ciyue': 'syj|next-month|price',
|
||||
'cieryue': 'syj|next-one-month|price',
|
||||
'cisanyue': 'yj|next-two-month|price',
|
||||
'cisiyue': 'yj|next-three-month|price',
|
||||
}
|
||||
|
||||
# 报告中八大维度数据项重命名
|
||||
columnsrename={'syjlyycbdwdbz': '本周', 'syjlyycbdwdcey': '次二月', 'syjlyycbdwdcr': '次日', 'syjlyycbdwdcsiy': '次四月',
|
||||
'syjlyycbdwdcsy': '次三月', 'syjlyycbdwdcy': '次月', 'syjlyycbdwdcz': '次周', 'syjlyycbdwdgz': '隔周', }
|
||||
columnsrename={ 'syjlyycbdwdcr': '次日', 'syjlyycbdwdbz': '本周',
|
||||
'syj|nextweek|price': '次周', 'syj|next-one-week|price': '隔周',
|
||||
'syj|next-month|price': '次月', 'syj|next-one-month|price': '次二月', 'yj|next-two-month|price': '次三月', 'yj|next-three-month|price': '次四月'}
|
||||
# 北京环境数据库
|
||||
host = '192.168.101.27'
|
||||
port = 3306
|
||||
@ -542,7 +544,7 @@ avg_cols = [
|
||||
offsite = 80
|
||||
offsite_col = []
|
||||
horizon = 4 # 预测的步长
|
||||
input_size = 16 # 输入序列长度
|
||||
input_size = 8 # 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
|
||||
|
||||
@ -326,12 +326,12 @@ push_data_value_list_data = {
|
||||
bdwd_items = {
|
||||
'ciri': 'syjlyycbdwdcr',
|
||||
'benzhou': 'syjlyycbdwdbz',
|
||||
'cizhou': 'syjlyycbdwdcz',
|
||||
'gezhou': 'syjlyycbdwdgz',
|
||||
'ciyue': 'syjlyycbdwdcy',
|
||||
'cieryue': 'syjlyycbdwdcey',
|
||||
'cisanyue': 'syjlyycbdwdcsy',
|
||||
'cisiyue': 'syjlyycbdwdcsiy',
|
||||
'cizhou': 'syj|nextweek|price',
|
||||
'gezhou': 'syj|next-one-week|price',
|
||||
'ciyue': 'syj|next-month|price',
|
||||
'cieryue': 'syj|next-one-month|price',
|
||||
'cisanyue': 'yj|next-two-month|price',
|
||||
'cisiyue': 'yj|next-three-month|price',
|
||||
}
|
||||
|
||||
# 北京环境数据库
|
||||
|
||||
@ -79,6 +79,9 @@ global_config = {
|
||||
'upload_warning_url': None, # 预警数据上传地址
|
||||
'upload_warning_data': None, # 预警数据结构
|
||||
|
||||
# 报告上传
|
||||
'upload_data': None, # 报告数据结构
|
||||
|
||||
# 查询接口
|
||||
'query_data_list_item_nos_url': None, # 数据项查询地址
|
||||
'query_data_list_item_nos_data': None, # 数据项查询参数
|
||||
|
||||
442
main_shiyoujiao_lvyong_yuedu.py
Normal file
442
main_shiyoujiao_lvyong_yuedu.py
Normal file
@ -0,0 +1,442 @@
|
||||
# 读取配置
|
||||
|
||||
from lib.dataread import *
|
||||
from config_shiyoujiao_lvyong_yuedu import *
|
||||
from lib.tools import SendMail, exception_logger
|
||||
from models.nerulforcastmodels import ex_Model, model_losss, shiyoujiao_lvyong_export_pdf
|
||||
import datetime
|
||||
import torch
|
||||
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,
|
||||
'is_update_report': is_update_report,
|
||||
'settings': settings,
|
||||
'weight_dict': weight_dict,
|
||||
'baichuanidnamedict': baichuanidnamedict,
|
||||
'bdwdname': bdwdname,
|
||||
|
||||
|
||||
# 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,
|
||||
'bdwd_items': bdwd_items,
|
||||
'baichuanidnamedict': baichuanidnamedict,
|
||||
|
||||
# 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,
|
||||
'is_fivemodels': is_fivemodels,
|
||||
'is_update_predict_value': is_update_predict_value,
|
||||
'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_data': upload_data,
|
||||
'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,
|
||||
|
||||
# 上传数据项
|
||||
'push_data_value_list_url': push_data_value_list_url,
|
||||
'push_data_value_list_data': push_data_value_list_data,
|
||||
|
||||
# eta 配置
|
||||
'APPID': APPID,
|
||||
'SECRET': SECRET,
|
||||
'etadata': data,
|
||||
'edbcodelist': edbcodelist,
|
||||
'ClassifyId': ClassifyId,
|
||||
'edbcodedataurl': edbcodedataurl,
|
||||
'classifyidlisturl': classifyidlisturl,
|
||||
'edbdatapushurl': edbdatapushurl,
|
||||
'edbdeleteurl': edbdeleteurl,
|
||||
'edbbusinessurl': edbbusinessurl,
|
||||
'edbcodenamedict': edbcodenamedict,
|
||||
'ClassifyId': ClassifyId,
|
||||
'classifylisturl': classifylisturl,
|
||||
|
||||
# 数据库配置
|
||||
'sqlitedb': sqlitedb,
|
||||
'is_bdwd': is_bdwd,
|
||||
'columnsrename':columnsrename,
|
||||
'db_mysql': db_mysql,
|
||||
'baichuan_table_name': baichuan_table_name,
|
||||
})
|
||||
|
||||
|
||||
def push_market_value():
|
||||
logger.info('发送预测结果到市场信息平台')
|
||||
# 读取预测数据和模型评估数据
|
||||
predict_file_path = os.path.join(config.dataset, 'predict.csv')
|
||||
model_eval_file_path = os.path.join(config.dataset, 'model_evaluation.csv')
|
||||
try:
|
||||
predictdata_df = pd.read_csv(predict_file_path)
|
||||
top_models_df = pd.read_csv(model_eval_file_path)
|
||||
except FileNotFoundError as e:
|
||||
logger.error(f"文件未找到: {e}")
|
||||
return
|
||||
|
||||
predictdata = predictdata_df.copy()
|
||||
|
||||
# 取模型前十
|
||||
top_models = top_models_df['模型(Model)'].head(10).tolist()
|
||||
# 去掉FDBformer
|
||||
if 'FEDformer' in top_models:
|
||||
top_models.remove('FEDformer')
|
||||
# 计算前十模型的均值
|
||||
predictdata_df['top_models_mean'] = predictdata_df[top_models].mean(axis=1)
|
||||
|
||||
# 打印日期和前十模型均值
|
||||
print(predictdata_df[['ds', 'top_models_mean']])
|
||||
|
||||
# 准备要推送的数据
|
||||
ciyue_mean = predictdata_df['top_models_mean'].iloc[0]
|
||||
cieryue_mean = predictdata_df['top_models_mean'].iloc[1]
|
||||
cisanyue_mean = predictdata_df['top_models_mean'].iloc[2]
|
||||
cisieryue_mean = predictdata_df['top_models_mean'].iloc[3]
|
||||
# 保留两位小数
|
||||
ciyue_mean = round(ciyue_mean, 2)
|
||||
cieryue_mean = round(cieryue_mean, 2)
|
||||
cisanyue_mean = round(cisanyue_mean, 2)
|
||||
cisieryue_mean = round(cisieryue_mean, 2)
|
||||
|
||||
predictdata = [
|
||||
{
|
||||
"dataItemNo": global_config['bdwd_items']['ciyue'],
|
||||
"dataDate": global_config['end_time'].replace('-', ''),
|
||||
"dataStatus": "add",
|
||||
"dataValue": ciyue_mean
|
||||
},
|
||||
{
|
||||
"dataItemNo": global_config['bdwd_items']['cieryue'],
|
||||
"dataDate": global_config['end_time'].replace('-', ''),
|
||||
"dataStatus": "add",
|
||||
"dataValue": cieryue_mean
|
||||
},
|
||||
{
|
||||
"dataItemNo": global_config['bdwd_items']['cisanyue'],
|
||||
"dataDate": global_config['end_time'].replace('-', ''),
|
||||
"dataStatus": "add",
|
||||
"dataValue": cisanyue_mean
|
||||
},
|
||||
{
|
||||
"dataItemNo": global_config['bdwd_items']['cisiyue'],
|
||||
"dataDate": global_config['end_time'].replace('-', ''),
|
||||
"dataStatus": "add",
|
||||
"dataValue": cisieryue_mean
|
||||
}
|
||||
]
|
||||
|
||||
print(predictdata)
|
||||
|
||||
# 推送数据到市场信息平台
|
||||
try:
|
||||
push_market_data(predictdata)
|
||||
except Exception as e:
|
||||
logger.error(f"推送数据失败: {e}")
|
||||
|
||||
|
||||
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
|
||||
"""
|
||||
|
||||
end_time = global_config['end_time']
|
||||
# 获取数据
|
||||
if is_eta:
|
||||
logger.info('从eta获取数据...')
|
||||
signature = BinanceAPI(APPID, SECRET)
|
||||
etadata = EtaReader(signature=signature,
|
||||
classifylisturl=global_config['classifylisturl'],
|
||||
classifyidlisturl=global_config['classifyidlisturl'],
|
||||
edbcodedataurl=global_config['edbcodedataurl'],
|
||||
edbcodelist=global_config['edbcodelist'],
|
||||
edbdatapushurl=global_config['edbdatapushurl'],
|
||||
edbdeleteurl=global_config['edbdeleteurl'],
|
||||
edbbusinessurl=global_config['edbbusinessurl'],
|
||||
classifyId=global_config['ClassifyId'],
|
||||
)
|
||||
df_zhibiaoshuju, df_zhibiaoliebiao = etadata.get_eta_api_shiyoujiao_lvyong_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')
|
||||
|
||||
if len(global_config['baichuanidnamedict']) > 0:
|
||||
logger.info('从市场数据库获取百川数据...')
|
||||
baichuandf = get_baichuan_data(global_config['baichuanidnamedict'])
|
||||
df_zhibiaoshuju = pd.merge(
|
||||
df_zhibiaoshuju, baichuandf, on='date', how='outer')
|
||||
# 指标列表添加百川数据
|
||||
df_baichuanliebiao = pd.DataFrame(
|
||||
global_config['baichuanidnamedict'].items(), columns=['指标id', '指标名称'])
|
||||
df_baichuanliebiao['指标分类'] = '石油焦对标炼厂价格'
|
||||
df_baichuanliebiao['频度'] = '其他'
|
||||
df_zhibiaoliebiao = pd.concat(
|
||||
[df_zhibiaoliebiao, df_baichuanliebiao], axis=0)
|
||||
|
||||
# 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 = zhoududatachuli(df_zhibiaoshuju, df_zhibiaoliebiao, y=global_config['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()
|
||||
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
|
||||
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:
|
||||
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}')
|
||||
|
||||
# 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:-2]
|
||||
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',))
|
||||
|
||||
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=global_config['horizon'],
|
||||
input_size=global_config['input_size'],
|
||||
train_steps=global_config['train_steps'],
|
||||
val_check_steps=global_config['val_check_steps'],
|
||||
early_stop_patience_steps=global_config['early_stop_patience_steps'],
|
||||
is_debug=global_config['is_debug'],
|
||||
dataset=global_config['dataset'],
|
||||
is_train=global_config['is_train'],
|
||||
is_fivemodels=global_config['is_fivemodels'],
|
||||
val_size=global_config['val_size'],
|
||||
test_size=global_config['test_size'],
|
||||
settings=global_config['settings'],
|
||||
now=now,
|
||||
etadata=etadata,
|
||||
modelsindex=global_config['modelsindex'],
|
||||
data=data,
|
||||
is_eta=global_config['is_eta'],
|
||||
end_time=global_config['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 = '石油焦大模型铝用渠道.pdf' # 报告文件名
|
||||
# reportname = f'石油焦铝用大模型月度预测--{end_time}.pdf' # 报告文件名
|
||||
# reportname = reportname.replace(':', '-') # 替换冒号
|
||||
shiyoujiao_lvyong_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')
|
||||
|
||||
push_market_value()
|
||||
|
||||
# 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
|
||||
# # 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'):
|
||||
# end_time = i_time.strftime('%Y-%m-%d')
|
||||
# predict_main()
|
||||
|
||||
predict_main()
|
||||
@ -54,6 +54,7 @@ global_config.update({
|
||||
'login_pushreport_url': login_pushreport_url,
|
||||
'login_data': login_data,
|
||||
'upload_url': upload_url,
|
||||
'upload_data': upload_data,
|
||||
'upload_warning_url': upload_warning_url,
|
||||
'warning_data': warning_data,
|
||||
|
||||
@ -373,7 +374,7 @@ def predict_main():
|
||||
test_size=global_config['test_size'],
|
||||
settings=global_config['settings'],
|
||||
now=now,
|
||||
etadata=global_config['etadata'],
|
||||
etadata=etadata,
|
||||
modelsindex=global_config['modelsindex'],
|
||||
data=data,
|
||||
is_eta=global_config['is_eta'],
|
||||
|
||||
@ -2851,10 +2851,10 @@ def shiyoujiao_lvyong_export_pdf(num_indicators=475, num_models=21, num_dayindic
|
||||
if config.is_update_report:
|
||||
with open(os.path.join(config.dataset, reportname), 'rb') as f:
|
||||
base64_data = base64.b64encode(f.read()).decode('utf-8')
|
||||
upload_data["data"]["fileBase64"] = base64_data
|
||||
upload_data["data"]["fileName"] = reportname
|
||||
config.upload_data["data"]["fileBase64"] = base64_data
|
||||
config.upload_data["data"]["fileName"] = reportname
|
||||
token = get_head_auth_report()
|
||||
upload_report_data(token, upload_data)
|
||||
upload_report_data(token, config.upload_data)
|
||||
except TimeoutError as e:
|
||||
print(f"请求超时: {e}")
|
||||
|
||||
|
||||
Loading…
Reference in New Issue
Block a user