From cf8614da3f402e44216789f5d37f4df57ad89b42 Mon Sep 17 00:00:00 2001 From: workpc Date: Thu, 29 May 2025 13:51:09 +0800 Subject: [PATCH] =?UTF-8?q?=E6=B5=8B=E8=AF=95=E9=9B=86=E9=AA=8C=E8=AF=81?= =?UTF-8?q?=E9=80=BB=E8=BE=91=E8=B0=83=E6=95=B4?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- main_yuanyou.py | 28 ++++++++++++++-------------- models/nerulforcastmodels.py | 15 +++++++-------- 2 files changed, 21 insertions(+), 22 deletions(-) diff --git a/main_yuanyou.py b/main_yuanyou.py index dd7ea5d..3a9fe0d 100644 --- a/main_yuanyou.py +++ b/main_yuanyou.py @@ -451,22 +451,22 @@ def predict_main(): 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原油大模型日度预测--{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, - inputsize=global_config['horizon'], - sqlitedb=sqlitedb - ), + # # 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原油大模型日度预测--{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, + # inputsize=global_config['horizon'], + # sqlitedb=sqlitedb + # ), - logger.info('制作报告end') - logger.info('模型训练完成') + # logger.info('制作报告end') + # logger.info('模型训练完成') - push_market_value() + # push_market_value() # # LSTM 单变量模型 # ex_Lstm(df,input_seq_len=input_size,output_seq_len=horizon,is_debug=is_debug,dataset=dataset) diff --git a/models/nerulforcastmodels.py b/models/nerulforcastmodels.py index 45ed060..3b30237 100644 --- a/models/nerulforcastmodels.py +++ b/models/nerulforcastmodels.py @@ -193,8 +193,8 @@ def ex_Model(df, horizon, input_size, train_steps, val_check_steps, early_stop_p # 模型交叉验证 nf_preds = nf.cross_validation( df=df_train, val_size=val_size, test_size=test_size, n_windows=None) - nf_preds.to_csv(os.path.join( - config.dataset, "cross_validation.csv"), index=False) + # nf_preds.to_csv(os.path.join( + # config.dataset, "cross_validation.csv"), index=False) nf_preds = nf_preds.reset_index() # 保存模型 @@ -498,7 +498,6 @@ def ex_Model_Juxiting(df, horizon, input_size, train_steps, val_check_steps, ear return - # 雍安环境预测评估指数 @exception_logger def model_losss_yongan(sqlitedb, end_time, table_name_prefix): @@ -965,7 +964,7 @@ def model_losss(sqlitedb, end_time): try: df_combined = sqlitedb.select_data( 'accuracy', where_condition=f"created_dt <= '{end_time}'") - if len(df_combined) < 100: + if len(df_combined) < 10000: len(df_combined) + '' if df_combined['y'].isnull().sum() / len(df_combined) > 0.8: len(df_combined) + '' @@ -973,7 +972,7 @@ def model_losss(sqlitedb, end_time): df_combined = loadcsv(os.path.join( config.dataset, "cross_validation.csv")) df_combined = dateConvert(df_combined) - df_combined['CREAT_DATE'] = df_combined['cutoff'] + df_combined['CREAT_DATE'] = df_combined['ds'] df_combined4 = df_combined.copy() # 备份df_combined,后面画图需要 # 删除缺失值大于80%的列 config.logger.info(df_combined.shape) @@ -985,12 +984,12 @@ def model_losss(sqlitedb, end_time): # 其他列转为数值类型 df_combined = df_combined.astype({col: 'float32' for col in df_combined.columns if col not in [ 'CREAT_DATE', 'ds', 'created_dt', 'cutoff']}) - # 使用 groupby 和 transform 结合 lambda 函数来获取每个分组中 cutoff 的最小值，并创建一个新的列来存储这个最大值 + # 使用 groupby 和 transform 结合 lambda 函数来获取每个分组中 cutoff 的最大值，并创建一个新的列来存储这个最大值 df_combined['max_cutoff'] = df_combined.groupby( - 'ds')['CREAT_DATE'].transform('max') + 'ds')['cutoff'].transform('max') # 然后筛选出那些 cutoff 等于 max_cutoff 的行，这样就得到了每个分组中 cutoff 最大的行，并保留了其他列 - df_combined = df_combined[df_combined['CREAT_DATE'] + df_combined = df_combined[df_combined['cutoff'] == df_combined['max_cutoff']] # 删除模型生成的cutoff列 df_combined.drop(columns=['CREAT_DATE', 'max_cutoff', 'created_dt', 'min_within_quantile', 'max_within_quantile',