原油日度预测上传市场信息平台数据
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jingboyitiji
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2fbd6c04f9
commit
046e6dcc7b
@ -2276,60 +2276,6 @@ def get_baichuan_data(baichuanidnamedict):
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def push_market_value():
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config.logger.info('发送预测结果到市场信息平台')
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# 读取预测数据和模型评估数据
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predict_file_path = os.path.join(config.dataset, 'predict.csv')
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model_eval_file_path = os.path.join(config.dataset, 'model_evaluation.csv')
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try:
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predictdata_df = pd.read_csv(predict_file_path)
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top_models_df = pd.read_csv(model_eval_file_path)
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except FileNotFoundError as e:
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config.logger.error(f"文件未找到: {e}")
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return
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predictdata = predictdata_df.copy()
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# 取模型前十
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top_models = top_models_df['模型(Model)'].head(10).tolist()
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# 计算前十模型的均值
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predictdata_df['top_models_mean'] = predictdata_df[top_models].mean(axis=1)
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# 打印日期和前十模型均值
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print(predictdata_df[['ds', 'top_models_mean']])
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# 准备要推送的数据
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first_mean = predictdata_df['top_models_mean'].iloc[0]
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last_mean = predictdata_df['top_models_mean'].iloc[-1]
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# 保留两位小数
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first_mean = round(first_mean, 2)
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last_mean = round(last_mean, 2)
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predictdata = [
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{
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"dataItemNo": global_config['bdwd_items']['ciri'],
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"dataDate": global_config['end_time'].replace('-',''),
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"dataStatus": "add",
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"dataValue": first_mean
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},
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{
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"dataItemNo": global_config['bdwd_items']['benzhou'],
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"dataDate": global_config['end_time'].replace('-',''),
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"dataStatus": "add",
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"dataValue": last_mean
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}
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]
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print(predictdata)
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# 推送数据到市场信息平台
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try:
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push_market_data(predictdata)
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except Exception as e:
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config.logger.error(f"推送数据失败: {e}")
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def get_bdwd_predict_data():
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# 获取认证头部信息
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token = get_head_auth_report()
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@ -80,6 +80,63 @@ global_config.update({
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})
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def push_market_value():
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config.logger.info('发送预测结果到市场信息平台')
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# 读取预测数据和模型评估数据
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predict_file_path = os.path.join(config.dataset, 'predict.csv')
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model_eval_file_path = os.path.join(config.dataset, 'model_evaluation.csv')
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try:
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predictdata_df = pd.read_csv(predict_file_path)
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top_models_df = pd.read_csv(model_eval_file_path)
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except FileNotFoundError as e:
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config.logger.error(f"文件未找到: {e}")
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return
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predictdata = predictdata_df.copy()
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# 取模型前十
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top_models = top_models_df['模型(Model)'].head(10).tolist()
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# 计算前十模型的均值
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predictdata_df['top_models_mean'] = predictdata_df[top_models].mean(axis=1)
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# 打印日期和前十模型均值
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print(predictdata_df[['ds', 'top_models_mean']])
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# 准备要推送的数据
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first_mean = predictdata_df['top_models_mean'].iloc[0]
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last_mean = predictdata_df['top_models_mean'].iloc[-1]
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# 保留两位小数
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first_mean = round(first_mean, 2)
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last_mean = round(last_mean, 2)
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predictdata = [
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{
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"dataItemNo": global_config['bdwd_items']['ciri'],
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"dataDate": global_config['end_time'].replace('-',''),
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"dataStatus": "add",
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"dataValue": first_mean
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},
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{
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"dataItemNo": global_config['bdwd_items']['benzhou'],
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"dataDate": global_config['end_time'].replace('-',''),
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"dataStatus": "add",
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"dataValue": last_mean
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}
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]
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print(predictdata)
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# 推送数据到市场信息平台
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try:
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push_market_data(predictdata)
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except Exception as e:
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config.logger.error(f"推送数据失败: {e}")
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def predict_main():
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"""
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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。
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@ -3,7 +3,7 @@
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from lib.dataread import *
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from config_jingbo_yuedu import *
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from lib.tools import SendMail, exception_logger
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from models.nerulforcastmodels import ex_Model, model_losss
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from models.nerulforcastmodels import ex_Model, model_losss,brent_export_pdf
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import datetime
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import torch
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torch.set_float32_matmul_precision("high")
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@ -417,18 +417,18 @@ def predict_main():
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model_results3 = model_losss(sqlitedb, end_time=end_time)
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logger.info('训练数据绘图end')
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# # 模型报告
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# logger.info('制作报告ing')
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# title = f'{settings}--{end_time}-预测报告' # 报告标题
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# reportname = f'Brent原油大模型月度预测--{end_time}.pdf' # 报告文件名
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# reportname = reportname.replace(':', '-') # 替换冒号
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# brent_export_pdf(dataset=dataset, num_models=5 if is_fivemodels else 22, time=end_time,
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# reportname=reportname, sqlitedb=sqlitedb),
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# 模型报告
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logger.info('制作报告ing')
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title = f'{settings}--{end_time}-预测报告' # 报告标题
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reportname = f'Brent原油大模型月度预测--{end_time}.pdf' # 报告文件名
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reportname = reportname.replace(':', '-') # 替换冒号
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brent_export_pdf(dataset=dataset, num_models=5 if is_fivemodels else 22, time=end_time,
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reportname=reportname, sqlitedb=sqlitedb),
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# logger.info('制作报告end')
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# logger.info('模型训练完成')
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logger.info('制作报告end')
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logger.info('模型训练完成')
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# push_market_value()
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push_market_value()
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# # LSTM 单变量模型
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# ex_Lstm(df,input_seq_len=input_size,output_seq_len=horizon,is_debug=is_debug,dataset=dataset)
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@ -455,13 +455,13 @@ def predict_main():
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if __name__ == '__main__':
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# global end_time
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# 遍历2024-11-25 到 2024-12-3 之间的工作日日期
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for i_time in pd.date_range('2025-1-1', '2025-3-26', freq='B'):
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# try:
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global_config['end_time'] = i_time.strftime('%Y-%m-%d')
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predict_main()
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# for i_time in pd.date_range('2025-1-1', '2025-3-26', freq='B'):
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# # try:
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# global_config['end_time'] = i_time.strftime('%Y-%m-%d')
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# predict_main()
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# except Exception as e:
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# logger.info(f'预测失败：{e}')
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# continue
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# predict_main()
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predict_main()
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