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还原原油周度执行文件

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workpc 2025-07-11 14:46:23 +08:00
parent 34770b83a3
commit 98836d7a4d
2 changed files with 87 additions and 110 deletions
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67
main_juxiting.py
View File

@ -436,42 +436,41 @@ def predict_main():
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 True:
if True: logger.info('发送特征预警')
logger.info('发送特征预警') # 获取取消订阅的指标ID
# 获取取消订阅的指标ID quxiaodingyueidlist = get_waring_data()
quxiaodingyueidlist = get_waring_data() # 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': 'indicatorName', '指标id': 'indicatorId', '频度': 'frequency',
warning_data_df = warning_data_df.rename(columns={'指标名称': 'indicatorName', '指标id': 'indicatorId', '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': 'updateSuspensionCycle'})
'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': 'updateSuspensionCycle'})
warning_data_df['warningDate'] = datetime.date.today().strftime( warning_data_df['warningDate'] = datetime.date.today().strftime(
"%Y-%m-%d %H:%M:%S") "%Y-%m-%d %H:%M:%S")
warning_data_df['dataSource'] = 9 warning_data_df['dataSource'] = 9
if len(quxiaodingyueidlist) > 0: if len(quxiaodingyueidlist) > 0:
# 去掉取消订阅的指标 # 去掉取消订阅的指标
print(warning_data_df.shape) print(warning_data_df.shape)
warning_data_df = warning_data_df[~warning_data_df['indicatorId'].isin( warning_data_df = warning_data_df[~warning_data_df['indicatorId'].isin(
quxiaodingyueidlist)] quxiaodingyueidlist)]
print(warning_data_df.shape) print(warning_data_df.shape)
warning_data = warning_data_df.to_json( warning_data = warning_data_df.to_json(
orient='records', force_ascii=False) orient='records', force_ascii=False)
warning_data = warning_data.replace('日度', '1') warning_data = warning_data.replace('日度', '1')
warning_data = warning_data.replace('周度', '2') warning_data = warning_data.replace('周度', '2')
warning_data = warning_data.replace('月度', '3') warning_data = warning_data.replace('月度', '3')
warning_data = json.loads(warning_data) warning_data = json.loads(warning_data)
push_waring_market_data( push_waring_market_data(
warning_data, dataSource=warning_data_df['dataSource'].values[0]) warning_data, dataSource=warning_data_df['dataSource'].values[0])
# 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)

130
main_yuanyou_zhoudu.py
View File

@ -1,9 +1,9 @@
# 读取配置 # 读取配置
from lib.dataread import * from lib.dataread import *
from config_juxiting_zhoudu import * from config_jingbo_zhoudu import *
from lib.tools import SendMail, exception_logger, convert_df_to_pydantic, exception_logger, get_modelsname from lib.tools import SendMail, convert_df_to_pydantic, exception_logger, get_modelsname
from models.nerulforcastmodels import ex_Model_Juxiting, model_losss_juxiting, pp_export_pdf from models.nerulforcastmodels import ex_Model, model_losss, brent_export_pdf
import datetime import datetime
import torch import torch
torch.set_float32_matmul_precision("high") torch.set_float32_matmul_precision("high")
@ -13,10 +13,6 @@ global_config.update({
'logger': logger, 'logger': logger,
'dataset': dataset, 'dataset': dataset,
'y': y, 'y': y,
# 'offsite_col': offsite_col,
# 'avg_cols': avg_cols,
# 'offsite': offsite,
'edbcodenamedict': edbcodenamedict,
'is_debug': is_debug, 'is_debug': is_debug,
'is_train': is_train, 'is_train': is_train,
'is_fivemodels': is_fivemodels, 'is_fivemodels': is_fivemodels,
@ -67,14 +63,6 @@ global_config.update({
'push_data_value_list_url': push_data_value_list_url, 'push_data_value_list_url': push_data_value_list_url,
'push_data_value_list_data': push_data_value_list_data, 'push_data_value_list_data': push_data_value_list_data,
# 上传预警数据
'push_waring_data_value_list_url': push_waring_data_value_list_url,
'push_waring_data_value_list_data': push_waring_data_value_list_data,
# 获取取消订阅的数据
'get_waring_data_value_list_url': get_waring_data_value_list_url,
'get_waring_data_value_list_data': get_waring_data_value_list_data,
# eta 配置 # eta 配置
'APPID': APPID, 'APPID': APPID,
'SECRET': SECRET, 'SECRET': SECRET,
@ -91,7 +79,6 @@ global_config.update({
# 数据库配置 # 数据库配置
'sqlitedb': sqlitedb, 'sqlitedb': sqlitedb,
'bdwd_items': bdwd_items,
'is_bdwd': is_bdwd, 'is_bdwd': is_bdwd,
'db_mysql': db_mysql, 'db_mysql': db_mysql,
'DEFAULT_CONFIG': DEFAULT_CONFIG, 'DEFAULT_CONFIG': DEFAULT_CONFIG,
@ -99,7 +86,7 @@ global_config.update({
def push_market_value(): def push_market_value():
config.logger.info('发送预测结果到市场信息平台') 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 # 读取预测数据和模型评估数据
predict_file_path = os.path.join(config.dataset, 'predict.csv') predict_file_path = os.path.join(config.dataset, 'predict.csv')
model_eval_file_path = os.path.join(config.dataset, 'model_evaluation.csv') model_eval_file_path = os.path.join(config.dataset, 'model_evaluation.csv')
@ -107,7 +94,7 @@ def push_market_value():
predictdata_df = pd.read_csv(predict_file_path) predictdata_df = pd.read_csv(predict_file_path)
top_models_df = pd.read_csv(model_eval_file_path) top_models_df = pd.read_csv(model_eval_file_path)
except FileNotFoundError as e: except FileNotFoundError as e:
config.logger.error(f"文件未找到: {e}") logger.error(f"文件未找到: {e}")
return return
predictdata = predictdata_df.copy() predictdata = predictdata_df.copy()
@ -151,7 +138,7 @@ def push_market_value():
try: try:
push_market_data(predictdata) push_market_data(predictdata)
except Exception as e: except Exception as e:
config.logger.error(f"推送数据失败: {e}") logger.error(f"推送数据失败: {e}")
def sql_inset_predict(global_config): def sql_inset_predict(global_config):
@ -167,7 +154,7 @@ def sql_inset_predict(global_config):
model_name_list, model_id_name_dict = get_modelsname(df, global_config) model_name_list, model_id_name_dict = get_modelsname(df, global_config)
PRICE_COLUMNS = [ PRICE_COLUMNS = [
'day_price', 'week_price', 'second_week_price', 'next_week_price', 'day_price', 'week_price', 'second_week_price', 'next_week_price',
'next_month_price', 'next_february_price', 'next_march_price', 'next_april_price' 'next_month_price', 'next_february_price', 'next_march_price', 'next_april_price'
] ]
@ -217,8 +204,8 @@ def sql_inset_predict(global_config):
params = ( params = (
result.feature_factor_frequency, result.feature_factor_frequency,
result.strategy_id, result.strategy_id,
global_config['DEFAULT_CONFIG']['oil_code'], result.oil_code,
global_config['DEFAULT_CONFIG']['oil_name'], result.oil_name,
next_day_df['created_dt'].values[0], next_day_df['created_dt'].values[0],
result.market_price, result.market_price,
*price_values, *price_values,
@ -279,6 +266,7 @@ def predict_main():
None None
""" """
end_time = global_config['end_time'] end_time = global_config['end_time']
signature = BinanceAPI(APPID, SECRET) signature = BinanceAPI(APPID, SECRET)
etadata = EtaReader(signature=signature, etadata = EtaReader(signature=signature,
classifylisturl=global_config['classifylisturl'], classifylisturl=global_config['classifylisturl'],
@ -294,7 +282,7 @@ def predict_main():
if is_eta: if is_eta:
logger.info('从eta获取数据...') logger.info('从eta获取数据...')
df_zhibiaoshuju, df_zhibiaoliebiao = etadata.get_eta_api_pp_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:
@ -302,7 +290,7 @@ def predict_main():
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文档获取市场信息平台指标') 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')
@ -310,7 +298,7 @@ def predict_main():
end_time, df_zhibiaoshuju) end_time, df_zhibiaoshuju)
except: except:
logger.info('市场信息平台数据项-eta数据项 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:
@ -318,14 +306,14 @@ def predict_main():
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_juxiting(df_zhibiaoshuju, df_zhibiaoliebiao, y=global_config['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_zhoudu_juxiting(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)
@ -395,8 +383,8 @@ def predict_main():
# 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 预测目标周度许转换，暂注释
is_weekday = datetime.datetime.now().weekday() == 0 # is_weekday = datetime.datetime.strptime(global_config['end_time'], "%Y-%m-%d").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
@ -442,41 +430,45 @@ def predict_main():
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_Juxiting(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=etadata, etadata=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')
logger.info('训练数据绘图ing') logger.info('训练数据绘图ing')
model_results3 = model_losss_juxiting( model_results3 = model_losss(sqlitedb, end_time=end_time)
sqlitedb, end_time=global_config['end_time'], is_fivemodels=global_config['is_fivemodels'])
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'聚烯烃PP大模型周度预测--{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
pp_export_pdf(dataset=dataset, num_models=5 if is_fivemodels else 22, time=end_time, brent_export_pdf(dataset=dataset,
reportname=reportname, sqlitedb=sqlitedb), num_models=5 if is_fivemodels else 22,
time=end_time,
reportname=reportname,
inputsize=global_config['horizon'],
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')
@ -484,15 +476,6 @@ def predict_main():
push_market_value() push_market_value()
sql_inset_predict(global_config) sql_inset_predict(global_config)
# # 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 # 发送邮件
# m = SendMail( # m = SendMail(
# username=username, # username=username,
@ -509,15 +492,10 @@ def predict_main():
if __name__ == '__main__': if __name__ == '__main__':
# global end_time # global end_time
# 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('2025-4-14', '2025-4-15', freq='B'): for i_time in pd.date_range('2025-6-23', '2025-6-30', freq='B'):
# try: global_config['end_time'] = i_time.strftime('%Y-%m-%d')
# global_config['end_time'] = i_time.strftime('%Y-%m-%d') global_config['db_mysql'].connect()
# predict_main() predict_main()
# except Exception as e:
# logger.info(f'预测失败：{e}')
# continue
predict_main() # predict_main()
# sql_inset_predict(global_config=global_config)
# push_market_value()
# sql_inset_predict(global_config)