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原油测试集预测停止，不生成报告，不评估模型

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workpc 2025-07-07 17:16:44 +08:00
parent 4c5a1d1c6e
commit 91387a7241
2 changed files with 21 additions and 21 deletions
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32
main_yuanyou_yuedu.py
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@ -543,25 +543,25 @@ def predict_main():
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(sqlitedb, end_time=end_time) # model_results3 = model_losss(sqlitedb, end_time=end_time)
logger.info('训练数据绘图end') # logger.info('训练数据绘图end')
push_market_value() # push_market_value()
# 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'Brent原油大模型月度预测--{end_time}.pdf' # 报告文件名 # 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 # reportname = reportname.replace(':', '-') # 替换冒号
brent_export_pdf(dataset=dataset, num_models=5 if is_fivemodels else 22, time=end_time, # brent_export_pdf(dataset=dataset, num_models=5 if is_fivemodels else 22, time=end_time,
reportname=reportname, # reportname=reportname,
inputsize=global_config['horizon'], # inputsize=global_config['horizon'],
sqlitedb=sqlitedb # 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')
sql_inset_predict(global_config) sql_inset_predict(global_config)

10
models/nerulforcastmodels.py
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@ -212,11 +212,11 @@ def ex_Model(df, horizon, input_size, train_steps, val_check_steps, early_stop_p
config.logger.info('读取模型：' + filename) config.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( # nf_test_preds = nf.cross_validation(
df=df_test, val_size=val_size, test_size=test_size, n_windows=None) # 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( # nf_test_preds.to_csv(os.path.join(
config.dataset, "cross_validation.csv"), index=False) # config.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')