From 61ba82f1776b0299d8720f43cf92d34201aab5d6 Mon Sep 17 00:00:00 2001 From: workpc Date: Tue, 12 Aug 2025 17:10:07 +0800 Subject: [PATCH] =?UTF-8?q?=E8=81=9A=E7=83=AF=E7=83=83=E6=97=A5=E5=BA=A6?= =?UTF-8?q?=E8=B0=83=E8=AF=95?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- config_juxiting.py | 23 +++++++++++++---------- main_juxiting.py | 31 ++++++++++++++++++++----------- main_juxiting_yuedu.py | 10 +++++----- models/nerulforcastmodels.py | 10 ++++++++-- 4 files changed, 46 insertions(+), 28 deletions(-) diff --git a/config_juxiting.py b/config_juxiting.py index ead3469..2f4b972 100644 --- a/config_juxiting.py +++ b/config_juxiting.py @@ -257,14 +257,17 @@ ClassifyId = 1161 # # 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', +# 'ciri': '251889263|FORECAST|PRICE|T01', +# 'cierri': '251889263|FORECAST|PRICE|T02', +# 'cisanri': '251889263|FORECAST|PRICE|T03', +# 'cisiri': '251889263|FORECAST|PRICE|T04', +# 'benzhou': '251889263|FORECAST|PRICE|T05', +# 'cizhou': '251889263|FORECAST|PRICE|W_01', +# 'gezhou': '251889263|FORECAST|PRICE|W_02', +# 'ciyue': '251889263|FORECAST|PRICE|M_01', +# 'cieryue': '251889263|FORECAST|PRICE|M_02', +# 'cisanyue': '251889263|FORECAST|PRICE|M_03', +# 'cisiyue': '251889263|FORECAST|PRICE|M_04', # } @@ -440,9 +443,9 @@ 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_eta = True # 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_update_warning_data = True # 是否上传预警数据 is_update_predict_value = True # 是否上传预测值到市场信息平台 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 diff --git a/main_juxiting.py b/main_juxiting.py index b0989d8..0657cf4 100644 --- a/main_juxiting.py +++ b/main_juxiting.py @@ -521,8 +521,17 @@ def predict_main(): # logger.info('制作报告end') - push_market_value() - sql_inset_predict(global_config) + try: + push_market_value() + logger.info('推送市场值完成') + except Exception as e: + logger.info(f'推送市场值失败：{e}') + + try: + sql_inset_predict(global_config) + logger.info('插入预测数据完成') + except Exception as e: + logger.info(f'插入预测数据失败：{e}') # # LSTM 单变量模型 # ex_Lstm(df,input_seq_len=input_size,output_seq_len=horizon,is_debug=is_debug,dataset=dataset) @@ -549,16 +558,16 @@ def predict_main(): 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('2025-6-2', '2025-7-23', freq='B'): - # try: - # global_config['end_time'] = i_time.strftime('%Y-%m-%d') - # global_config['db_mysql'].connect() - # predict_main() - # except Exception as e: - # logger.info(f'预测失败：{e}') - # continue + for i_time in pd.date_range('2025-8-1', '2025-8-11', freq='B'): + try: + global_config['end_time'] = i_time.strftime('%Y-%m-%d') + global_config['db_mysql'].connect() + predict_main() + except Exception as e: + logger.info(f'预测失败：{e}') + continue # global_config['end_time'] = '2025-08-05' - predict_main() + # predict_main() # global_config['end_time'] = '2025-08-01' # push_market_value() diff --git a/main_juxiting_yuedu.py b/main_juxiting_yuedu.py index 697ce15..9c756a4 100644 --- a/main_juxiting_yuedu.py +++ b/main_juxiting_yuedu.py @@ -552,12 +552,12 @@ if __name__ == '__main__': # continue # global_config['end_time'] = '2025-07-25' - predict_main() + # predict_main() # push_market_value() # sql_inset_predict(global_config) # 图片报告 - # global_config['end_time'] = '2025-08-05' - # logger.info('图片报告ing') - # pp_bdwd_png(global_config=global_config) - # logger.info('图片报告end') + global_config['end_time'] = '2025-08-12' + logger.info('图片报告ing') + pp_bdwd_png(global_config=global_config) + logger.info('图片报告end') diff --git a/models/nerulforcastmodels.py b/models/nerulforcastmodels.py index f279fe5..0dd5e35 100644 --- a/models/nerulforcastmodels.py +++ b/models/nerulforcastmodels.py @@ -321,6 +321,8 @@ def ex_Model_Juxiting(df, horizon, input_size, train_steps, val_check_steps, ear df['ds'] = pd.to_datetime(df['ds'], errors='coerce') # df 数值列转为 float32 for col in df.select_dtypes(include=['int']).columns: + if col == 'ds': + continue df[col] = df[col].astype(np.float32) # 设置中文字体 @@ -351,8 +353,12 @@ def ex_Model_Juxiting(df, horizon, input_size, train_steps, val_check_steps, ear # 特征重要度 X_train = df_train.drop(columns=['y', 'ds']) - if 'yearmonthweeks' in df_train.columns: - X_train = df_train.drop(columns=['yearmonthweeks']) + if 'yearmonthweeks' in X_train.columns: + X_train = X_train.drop(columns=['yearmonthweeks']) + # 自动检测并删除所有datetime类型列 + datetime_cols = X_train.select_dtypes(include=['datetime64']).columns + if not datetime_cols.empty: + X_train = X_train.drop(columns=datetime_cols) y_train = df_train['y'] feature_importance(X_train=X_train, y_train=y_train)