From e7cd575b883cb246e3acac7096185d1b1041d0bb Mon Sep 17 00:00:00 2001 From: workpc Date: Wed, 2 Apr 2025 16:20:50 +0800 Subject: [PATCH] =?UTF-8?q?=E8=81=9A=E7=83=AF=E7=83=83=E6=97=A5=E5=BA=A6?= =?UTF-8?q?=E9=A2=84=E6=B5=8B=EF=BC=8C=E4=B8=8A=E4=BC=A0=E5=B8=82=E5=9C=BA?= =?UTF-8?q?=E4=BF=A1=E6=81=AF=E5=B9=B3=E5=8F=B0?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- config_juxiting.py | 6 ++-- main_juxiting.py | 78 +++++++++++++++++++++++++++++++++++++++++----- 2 files changed, 74 insertions(+), 10 deletions(-) diff --git a/config_juxiting.py b/config_juxiting.py index d1cecc2..e667c33 100644 --- a/config_juxiting.py +++ b/config_juxiting.py @@ -241,16 +241,16 @@ table_name = 'v_tbl_crude_oil_warning' # 开关 -is_train = False # 是否训练 +is_train = True # 是否训练 is_debug = False # 是否调试 -is_eta = False # 是否使用eta接口 +is_eta = True # 是否使用eta接口 is_market = False # 是否通过市场信息平台获取特征 ,在is_eta 为true 的情况下生效 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 = True # 预测结果上传到eta -is_update_report = False # 是否上传报告 +is_update_report = True # 是否上传报告 is_update_warning_data = False # 是否上传预警数据 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 f766fed..a5e91c1 100644 --- a/main_juxiting.py +++ b/main_juxiting.py @@ -82,6 +82,68 @@ global_config.update({ }) + + + +def push_market_value(): + config.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 + predict_file_path = os.path.join(config.dataset, 'predict.csv') + model_eval_file_path = os.path.join(config.dataset, 'model_evaluation.csv') + try: + predictdata_df = pd.read_csv(predict_file_path) + top_models_df = pd.read_csv(model_eval_file_path) + except FileNotFoundError as e: + config.logger.error(f"文件未找到: {e}") + return + + predictdata = predictdata_df.copy() + + # 取模型前十 + top_models = top_models_df['模型(Model)'].head(10).tolist() + # 去掉FDBformer + if 'FEDformer' in top_models: + top_models.remove('FEDformer') + + # 计算前十模型的均值 + predictdata_df['top_models_mean'] = predictdata_df[top_models].mean(axis=1) + + # 打印日期和前十模型均值 + print(predictdata_df[['ds', 'top_models_mean']]) + + # 准备要推送的数据 + first_mean = predictdata_df['top_models_mean'].iloc[0] + last_mean = predictdata_df['top_models_mean'].iloc[-1] + # 保留两位小数 + first_mean = round(first_mean, 2) + last_mean = round(last_mean, 2) + + predictdata = [ + { + "dataItemNo": global_config['bdwd_items']['ciri'], + "dataDate": global_config['end_time'].replace('-',''), + "dataStatus": "add", + "dataValue": first_mean + }, + { + "dataItemNo": global_config['bdwd_items']['benzhou'], + "dataDate": global_config['end_time'].replace('-',''), + "dataStatus": "add", + "dataValue": last_mean + } + ] + + print(predictdata) + + # 推送数据到市场信息平台 + try: + push_market_data(predictdata) + except Exception as e: + config.logger.error(f"推送数据失败: {e}") + + + + def predict_main(): """ 主预测函数，用于从 ETA 获取数据、处理数据、训练模型并进行预测。 @@ -388,13 +450,15 @@ 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('2025-3-19', '2025-3-29', freq='B'): - try: - global_config['end_time'] = i_time.strftime('%Y-%m-%d') - predict_main() - except Exception as e: - logger.info(f'预测失败：{e}') - continue + # for i_time in pd.date_range('2025-3-19', '2025-3-29', freq='B'): + # try: + # global_config['end_time'] = i_time.strftime('%Y-%m-%d') + # predict_main() + # except Exception as e: + # logger.info(f'预测失败：{e}') + # continue # predict_main() + + push_market_value()