diff --git a/lib/dataread.py b/lib/dataread.py index 1291650..dd6d331 100644 --- a/lib/dataread.py +++ b/lib/dataread.py @@ -2276,60 +2276,6 @@ def get_baichuan_data(baichuanidnamedict): -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() - - # 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 get_bdwd_predict_data(): # 获取认证头部信息 token = get_head_auth_report() diff --git a/main_yuanyou.py b/main_yuanyou.py index 2f9e4ef..bdc532c 100644 --- a/main_yuanyou.py +++ b/main_yuanyou.py @@ -80,6 +80,63 @@ 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() + + # 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。 diff --git a/main_yuanyou_yuedu.py b/main_yuanyou_yuedu.py index e5ffaf3..3b470d0 100644 --- a/main_yuanyou_yuedu.py +++ b/main_yuanyou_yuedu.py @@ -3,7 +3,7 @@ from lib.dataread import * from config_jingbo_yuedu import * from lib.tools import SendMail, exception_logger -from models.nerulforcastmodels import ex_Model, model_losss +from models.nerulforcastmodels import ex_Model, model_losss,brent_export_pdf import datetime import torch torch.set_float32_matmul_precision("high") @@ -417,18 +417,18 @@ 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原油大模型月度预测--{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, 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原油大模型月度预测--{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, 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) @@ -455,13 +455,13 @@ 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('2025-1-1', '2025-3-26', freq='B'): - # try: - global_config['end_time'] = i_time.strftime('%Y-%m-%d') - predict_main() + # for i_time in pd.date_range('2025-1-1', '2025-3-26', 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() + predict_main()