diff --git a/config_jingbo.py b/config_jingbo.py index 0c7f70b..d2c6cbd 100644 --- a/config_jingbo.py +++ b/config_jingbo.py @@ -223,7 +223,7 @@ table_name = 'v_tbl_crude_oil_warning' ### 开关 -is_train = True # 是否训练 +is_train = False # 是否训练 is_debug = False # 是否调试 is_eta = False # 是否使用eta接口 is_timefurture = True # 是否使用时间特征 @@ -243,7 +243,7 @@ print("数据库连接成功",host,dbname,dbusername) # 数据截取日期 start_year = 2018 # 数据开始年份 -end_time = '' # 数据截取日期 +end_time = '2024-11-29' # 数据截取日期 freq = 'B' # 时间频率，"D": 天 "W": 周"M": 月"Q": 季度"A": 年 "H": 小时 "T": 分钟 "S": 秒 "B": 工作日 delweekenday = True if freq == 'B' else False # 是否删除周末数据 is_corr = False # 特征是否参与滞后领先提升相关系数 diff --git a/main_yuanyou.py b/main_yuanyou.py index 2abeabf..4ce188b 100644 --- a/main_yuanyou.py +++ b/main_yuanyou.py @@ -178,25 +178,25 @@ def predict_main(): row, col = df.shape now = datetime.datetime.now().strftime('%Y%m%d%H%M%S') - # ex_Model(df, - # horizon=horizon, - # input_size=input_size, - # train_steps=train_steps, - # val_check_steps=val_check_steps, - # early_stop_patience_steps=early_stop_patience_steps, - # is_debug=is_debug, - # dataset=dataset, - # is_train=is_train, - # is_fivemodels=is_fivemodels, - # val_size=val_size, - # test_size=test_size, - # settings=settings, - # now=now, - # etadata=etadata, - # modelsindex=modelsindex, - # data=data, - # is_eta=is_eta, - # ) + ex_Model(df, + horizon=horizon, + input_size=input_size, + train_steps=train_steps, + val_check_steps=val_check_steps, + early_stop_patience_steps=early_stop_patience_steps, + is_debug=is_debug, + dataset=dataset, + is_train=is_train, + is_fivemodels=is_fivemodels, + val_size=val_size, + test_size=test_size, + settings=settings, + now=now, + etadata=etadata, + modelsindex=modelsindex, + data=data, + is_eta=is_eta, + ) logger.info('模型训练完成') @@ -234,7 +234,7 @@ def predict_main(): file=max(glob.glob(os.path.join(dataset,'*.pdf')), key=os.path.getctime), ssl=ssl, ) - m.send_mail() + # m.send_mail() if __name__ == '__main__': diff --git a/models/nerulforcastmodels.py b/models/nerulforcastmodels.py index 91ba459..e00e4ad 100644 --- a/models/nerulforcastmodels.py +++ b/models/nerulforcastmodels.py @@ -179,17 +179,17 @@ def ex_Model(df,horizon,input_size,train_steps,val_check_steps,early_stop_patien filename = f'{settings}--{now}.joblib' #文件名去掉冒号 filename = filename.replace(':', '-') # 替换冒号 - # dump(nf, os.path.join(dataset,filename)) + dump(nf, os.path.join(dataset,filename)) else: # glob获取dataset下最新的joblib文件 import glob filename = max(glob.glob(os.path.join(dataset,'*.joblib')), key=os.path.getctime) - # logger.info('读取模型：'+ filename) + logger.info('读取模型：'+ filename) nf = load(filename) - # # 测试集预测 - nf_test_preds = nf.cross_validation(df=df_test, val_size=val_size, test_size=test_size, n_windows=None) - # 测试集预测结果保存 - nf_test_preds.to_csv(os.path.join(dataset,"cross_validation.csv"),index=False) + # # # 测试集预测 + # nf_test_preds = nf.cross_validation(df=df_test, val_size=val_size, test_size=test_size, n_windows=None) + # # 测试集预测结果保存 + # nf_test_preds.to_csv(os.path.join(dataset,"cross_validation.csv"),index=False) df_test['ds'] = pd.to_datetime(df_test['ds'], errors='coerce') @@ -217,7 +217,8 @@ def ex_Model(df,horizon,input_size,train_steps,val_check_steps,early_stop_patien etadata.push_data(data) - return nf_test_preds + # return nf_test_preds + return # 原油计算预测评估指数 @@ -399,12 +400,7 @@ def model_losss(sqlitedb): columns = ','.join(df_combined3.columns.to_list()+['id','CREAT_DATE']) sqlitedb.create_table('accuracy',columns=columns) existing_data = sqlitedb.select_data(table_name = "accuracy") - update_y = sqlitedb.select_data(table_name = "accuracy",where_condition='y is null') - df_combined4 = df_combined3[(df_combined3['ds'].isin(update_y['ds'])) & (df_combined3['y'].notnull())] - if len(df_combined4) > 0: - for index, row in df_combined4.iterrows(): - sqlitedb.update_data('accuracy',f"y = {row['y']}",f"ds = '{row['ds']}'") - print(df_combined4) + if not existing_data.empty: max_id = existing_data['id'].astype(int).max() df_predict2['id'] = range(max_id + 1, max_id + 1 + len(df_predict2)) @@ -418,7 +414,16 @@ def model_losss(sqlitedb): # df_predict2 = df_predict2[['id','PREDICT_DATE','CREAT_DATE','MIN_PRICE','MAX_PRICE']] df_predict2.to_sql("accuracy", con=sqlitedb.connection, if_exists='append', index=False) - + update_y = sqlitedb.select_data(table_name = "accuracy",where_condition='y is null') + if len(update_y) > 0: + df_combined4 = df_combined3[(df_combined3['ds'].isin(update_y['ds'])) & (df_combined3['y'].notnull())] + if len(df_combined4) > 0: + for index, row in df_combined4.iterrows(): + try: + sqlitedb.update_data('accuracy',f"y = {row['y']}",f"ds = '{row['ds']}'") + except: + print(row) + print(df_combined4) def _add_abs_error_rate():