聚烯烃月度模型执行更新
This commit is contained in:
jingboyitiji
parent
3b51565262
commit
ff7b9bf235
@ -557,8 +557,7 @@ if __name__ == '__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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global_config['end_time'] = '2025-08-01'
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global_config['db_mysql'].connect()
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global_config['end_time'] = '2025-08-04'
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predict_main()
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# global_config['end_time'] = '2025-08-01'
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@ -3,7 +3,7 @@
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from lib.dataread import *
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from config_juxiting_yuedu import *
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from lib.tools import SendMail, convert_df_to_pydantic_pp, exception_logger, find_best_models, get_modelsname
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from models.nerulforcastmodels import ex_Model, model_losss_juxiting, pp_bdwd_png, pp_export_pdf
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from models.nerulforcastmodels import ex_Model_Juxiting, model_losss_juxiting, pp_bdwd_png, pp_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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@ -285,203 +285,203 @@ def predict_main():
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返回:
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None
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"""
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# end_time = global_config['end_time']
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# signature = BinanceAPI(APPID, SECRET)
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# etadata = EtaReader(signature=signature,
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# classifylisturl=global_config['classifylisturl'],
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# classifyidlisturl=global_config['classifyidlisturl'],
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# edbcodedataurl=global_config['edbcodedataurl'],
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# edbcodelist=global_config['edbcodelist'],
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# edbdatapushurl=global_config['edbdatapushurl'],
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# edbdeleteurl=global_config['edbdeleteurl'],
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# edbbusinessurl=global_config['edbbusinessurl'],
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# classifyId=global_config['ClassifyId'],
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# )
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# # 获取数据
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# if is_eta:
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# logger.info('从eta获取数据...')
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end_time = global_config['end_time']
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signature = BinanceAPI(APPID, SECRET)
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etadata = EtaReader(signature=signature,
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classifylisturl=global_config['classifylisturl'],
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classifyidlisturl=global_config['classifyidlisturl'],
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edbcodedataurl=global_config['edbcodedataurl'],
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edbcodelist=global_config['edbcodelist'],
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edbdatapushurl=global_config['edbdatapushurl'],
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edbdeleteurl=global_config['edbdeleteurl'],
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edbbusinessurl=global_config['edbbusinessurl'],
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classifyId=global_config['ClassifyId'],
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)
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# 获取数据
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if is_eta:
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logger.info('从eta获取数据...')
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# df_zhibiaoshuju, df_zhibiaoliebiao = etadata.get_eta_api_pp_data(
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# data_set=data_set, dataset=dataset) # 原始数据，未处理
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df_zhibiaoshuju, df_zhibiaoliebiao = etadata.get_eta_api_pp_data(
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data_set=data_set, dataset=dataset) # 原始数据，未处理
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# if is_market:
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# logger.info('从市场信息平台获取数据...')
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# try:
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# # 如果是测试环境，最高价最低价取excel文档
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# if server_host == '192.168.100.53':
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# logger.info('从excel文档获取最高价最低价')
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# df_zhibiaoshuju = get_high_low_data(df_zhibiaoshuju)
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# else:
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# logger.info('从市场信息平台获取数据')
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# df_zhibiaoshuju = get_market_data(
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# end_time, df_zhibiaoshuju)
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if is_market:
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logger.info('从市场信息平台获取数据...')
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try:
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# 如果是测试环境，最高价最低价取excel文档
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if server_host == '192.168.100.53':
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logger.info('从excel文档获取最高价最低价')
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df_zhibiaoshuju = get_high_low_data(df_zhibiaoshuju)
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else:
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logger.info('从市场信息平台获取数据')
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df_zhibiaoshuju = get_market_data(
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end_time, df_zhibiaoshuju)
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# except:
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# logger.info('最高最低价拼接失败')
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except:
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logger.info('最高最低价拼接失败')
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# # 保存到xlsx文件的sheet表
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# with pd.ExcelWriter(os.path.join(dataset, data_set)) as file:
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# df_zhibiaoshuju.to_excel(file, sheet_name='指标数据', index=False)
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# df_zhibiaoliebiao.to_excel(file, sheet_name='指标列表', index=False)
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# 保存到xlsx文件的sheet表
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with pd.ExcelWriter(os.path.join(dataset, data_set)) as file:
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df_zhibiaoshuju.to_excel(file, sheet_name='指标数据', index=False)
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df_zhibiaoliebiao.to_excel(file, sheet_name='指标列表', index=False)
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# # 数据处理
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# df = datachuli_juxiting(df_zhibiaoshuju, df_zhibiaoliebiao, y=global_config['y'], dataset=dataset, add_kdj=add_kdj, is_timefurture=is_timefurture,
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# end_time=end_time)
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# 数据处理
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df = datachuli_juxiting(df_zhibiaoshuju, df_zhibiaoliebiao, y=global_config['y'], dataset=dataset, add_kdj=add_kdj, is_timefurture=is_timefurture,
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end_time=end_time)
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# else:
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# # 读取数据
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# logger.info('读取本地数据：' + os.path.join(dataset, data_set))
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# df, df_zhibiaoliebiao = getdata_zhoudu_juxiting(filename=os.path.join(dataset, data_set), y=y, dataset=dataset, add_kdj=add_kdj,
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# is_timefurture=is_timefurture, end_time=end_time) # 原始数据，未处理
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else:
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# 读取数据
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logger.info('读取本地数据：' + os.path.join(dataset, data_set))
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df, df_zhibiaoliebiao = getdata_zhoudu_juxiting(filename=os.path.join(dataset, data_set), y=y, dataset=dataset, add_kdj=add_kdj,
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is_timefurture=is_timefurture, end_time=end_time) # 原始数据，未处理
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# # 更改预测列名称
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# df.rename(columns={y: 'y'}, inplace=True)
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# 更改预测列名称
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df.rename(columns={y: 'y'}, inplace=True)
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# if is_edbnamelist:
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# df = df[edbnamelist]
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# df.to_csv(os.path.join(dataset, '指标数据.csv'), index=False)
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# # 保存最新日期的y值到数据库
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# # 取第一行数据存储到数据库中
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# first_row = df[['ds', 'y']].tail(1)
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# # 判断y的类型是否为float
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# if not isinstance(first_row['y'].values[0], float):
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# logger.info(f'{end_time}预测目标数据为空，跳过')
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# return None
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if is_edbnamelist:
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df = df[edbnamelist]
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df.to_csv(os.path.join(dataset, '指标数据.csv'), index=False)
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# 保存最新日期的y值到数据库
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# 取第一行数据存储到数据库中
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first_row = df[['ds', 'y']].tail(1)
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# 判断y的类型是否为float
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if not isinstance(first_row['y'].values[0], float):
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logger.info(f'{end_time}预测目标数据为空，跳过')
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return None
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# # 将最新真实值保存到数据库
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# if not sqlitedb.check_table_exists('trueandpredict'):
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# first_row.to_sql('trueandpredict', sqlitedb.connection, index=False)
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# else:
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# for row in first_row.itertuples(index=False):
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# row_dict = row._asdict()
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# config.logger.info(f'要保存的真实值：{row_dict}')
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# # 判断ds是否为字符串类型,如果不是则转换为字符串类型
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# if isinstance(row_dict['ds'], (pd.Timestamp, datetime.datetime)):
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# 将最新真实值保存到数据库
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if not sqlitedb.check_table_exists('trueandpredict'):
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first_row.to_sql('trueandpredict', sqlitedb.connection, index=False)
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else:
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for row in first_row.itertuples(index=False):
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row_dict = row._asdict()
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config.logger.info(f'要保存的真实值：{row_dict}')
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# 判断ds是否为字符串类型,如果不是则转换为字符串类型
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if isinstance(row_dict['ds'], (pd.Timestamp, datetime.datetime)):
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row_dict['ds'] = row_dict['ds'].strftime('%Y-%m-%d')
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elif not isinstance(row_dict['ds'], str):
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try:
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row_dict['ds'] = pd.to_datetime(
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row_dict['ds']).strftime('%Y-%m-%d')
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except:
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logger.warning(f"无法解析的时间格式: {row_dict['ds']}")
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# row_dict['ds'] = row_dict['ds'].strftime('%Y-%m-%d')
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# elif not isinstance(row_dict['ds'], str):
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# row_dict['ds'] = row_dict['ds'].strftime('%Y-%m-%d %H:%M:%S')
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check_query = sqlitedb.select_data(
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'trueandpredict', where_condition=f"ds = '{row.ds}'")
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if len(check_query) > 0:
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set_clause = ", ".join(
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[f"{key} = '{value}'" for key, value in row_dict.items()])
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sqlitedb.update_data(
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'trueandpredict', set_clause, where_condition=f"ds = '{row.ds}'")
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continue
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sqlitedb.insert_data('trueandpredict', tuple(
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row_dict.values()), columns=row_dict.keys())
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# 更新accuracy表的y值
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if not sqlitedb.check_table_exists('accuracy'):
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pass
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else:
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update_y = sqlitedb.select_data(
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'accuracy', where_condition="y is null")
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if len(update_y) > 0:
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logger.info('更新accuracy表的y值')
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# 找到update_y 中ds且df中的y的行
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update_y = update_y[update_y['ds'] <= end_time]
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logger.info(f'要更新y的信息：{update_y}')
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# try:
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# row_dict['ds'] = pd.to_datetime(
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# row_dict['ds']).strftime('%Y-%m-%d')
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# except:
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# logger.warning(f"无法解析的时间格式: {row_dict['ds']}")
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# # row_dict['ds'] = row_dict['ds'].strftime('%Y-%m-%d')
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# # row_dict['ds'] = row_dict['ds'].strftime('%Y-%m-%d %H:%M:%S')
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# check_query = sqlitedb.select_data(
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# 'trueandpredict', where_condition=f"ds = '{row.ds}'")
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# if len(check_query) > 0:
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# set_clause = ", ".join(
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# [f"{key} = '{value}'" for key, value in row_dict.items()])
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# sqlitedb.update_data(
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# 'trueandpredict', set_clause, where_condition=f"ds = '{row.ds}'")
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# continue
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# sqlitedb.insert_data('trueandpredict', tuple(
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# row_dict.values()), columns=row_dict.keys())
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for row in update_y.itertuples(index=False):
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try:
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row_dict = row._asdict()
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yy = df[df['ds'] == row_dict['ds']]['y'].values[0]
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LOW = df[df['ds'] == row_dict['ds']]['Brentzdj'].values[0]
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HIGH = df[df['ds'] == row_dict['ds']]['Brentzgj'].values[0]
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sqlitedb.update_data(
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'accuracy', f"y = {yy},LOW_PRICE = {LOW},HIGH_PRICE = {HIGH}", where_condition=f"ds = '{row_dict['ds']}'")
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except:
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logger.info(f'更新accuracy表的y值失败：{row_dict}')
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# except Exception as e:
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# logger.info(f'更新accuracy表的y值失败：{e}')
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# # 更新accuracy表的y值
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# if not sqlitedb.check_table_exists('accuracy'):
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# pass
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# else:
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# update_y = sqlitedb.select_data(
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# 'accuracy', where_condition="y is null")
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# if len(update_y) > 0:
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# logger.info('更新accuracy表的y值')
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# # 找到update_y 中ds且df中的y的行
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# update_y = update_y[update_y['ds'] <= end_time]
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# logger.info(f'要更新y的信息：{update_y}')
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# # try:
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# for row in update_y.itertuples(index=False):
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# try:
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# row_dict = row._asdict()
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# yy = df[df['ds'] == row_dict['ds']]['y'].values[0]
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# LOW = df[df['ds'] == row_dict['ds']]['Brentzdj'].values[0]
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# HIGH = df[df['ds'] == row_dict['ds']]['Brentzgj'].values[0]
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# sqlitedb.update_data(
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# 'accuracy', f"y = {yy},LOW_PRICE = {LOW},HIGH_PRICE = {HIGH}", where_condition=f"ds = '{row_dict['ds']}'")
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# except:
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# logger.info(f'更新accuracy表的y值失败：{row_dict}')
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# # except Exception as e:
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# # logger.info(f'更新accuracy表的y值失败：{e}')
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# 判断当前日期是不是周一
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is_weekday = datetime.datetime.now().weekday() == 0
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if is_weekday:
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logger.info('今天是周一，更新预测模型')
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# 计算最近60天预测残差最低的模型名称
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model_results = sqlitedb.select_data(
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'trueandpredict', order_by="ds DESC", limit="60")
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# 删除空值率为90%以上的列
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if len(model_results) > 10:
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model_results = model_results.dropna(
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thresh=len(model_results)*0.1, axis=1)
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# 删除空行
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model_results = model_results.dropna()
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modelnames = model_results.columns.to_list()[2:-1]
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for col in model_results[modelnames].select_dtypes(include=['object']).columns:
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model_results[col] = model_results[col].astype(np.float32)
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# 计算每个预测值与真实值之间的偏差率
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for model in modelnames:
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model_results[f'{model}_abs_error_rate'] = abs(
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model_results['y'] - model_results[model]) / model_results['y']
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# 获取每行对应的最小偏差率值
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min_abs_error_rate_values = model_results.apply(
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lambda row: row[[f'{model}_abs_error_rate' for model in modelnames]].min(), axis=1)
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# 获取每行对应的最小偏差率值对应的列名
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min_abs_error_rate_column_name = model_results.apply(
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lambda row: row[[f'{model}_abs_error_rate' for model in modelnames]].idxmin(), axis=1)
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# 将列名索引转换为列名
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min_abs_error_rate_column_name = min_abs_error_rate_column_name.map(
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lambda x: x.split('_')[0])
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# 取出现次数最多的模型名称
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most_common_model = min_abs_error_rate_column_name.value_counts().idxmax()
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logger.info(f"最近60天预测残差最低的模型名称：{most_common_model}")
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# 保存结果到数据库
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if not sqlitedb.check_table_exists('most_model'):
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sqlitedb.create_table(
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'most_model', columns="ds datetime, most_common_model TEXT")
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sqlitedb.insert_data('most_model', (datetime.datetime.now().strftime(
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'%Y-%m-%d %H:%M:%S'), most_common_model,), columns=('ds', 'most_common_model',))
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# # 判断当前日期是不是周一
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# is_weekday = datetime.datetime.now().weekday() == 0
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# if is_weekday:
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# logger.info('今天是周一，更新预测模型')
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# # 计算最近60天预测残差最低的模型名称
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# model_results = sqlitedb.select_data(
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# 'trueandpredict', order_by="ds DESC", limit="60")
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# # 删除空值率为90%以上的列
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# if len(model_results) > 10:
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# model_results = model_results.dropna(
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# thresh=len(model_results)*0.1, axis=1)
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# # 删除空行
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# model_results = model_results.dropna()
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# modelnames = model_results.columns.to_list()[2:-1]
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# for col in model_results[modelnames].select_dtypes(include=['object']).columns:
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# model_results[col] = model_results[col].astype(np.float32)
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# # 计算每个预测值与真实值之间的偏差率
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# for model in modelnames:
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# model_results[f'{model}_abs_error_rate'] = abs(
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# model_results['y'] - model_results[model]) / model_results['y']
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# # 获取每行对应的最小偏差率值
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# min_abs_error_rate_values = model_results.apply(
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# lambda row: row[[f'{model}_abs_error_rate' for model in modelnames]].min(), axis=1)
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# # 获取每行对应的最小偏差率值对应的列名
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# min_abs_error_rate_column_name = model_results.apply(
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# lambda row: row[[f'{model}_abs_error_rate' for model in modelnames]].idxmin(), axis=1)
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# # 将列名索引转换为列名
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# min_abs_error_rate_column_name = min_abs_error_rate_column_name.map(
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# lambda x: x.split('_')[0])
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# # 取出现次数最多的模型名称
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# most_common_model = min_abs_error_rate_column_name.value_counts().idxmax()
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# logger.info(f"最近60天预测残差最低的模型名称：{most_common_model}")
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# # 保存结果到数据库
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# if not sqlitedb.check_table_exists('most_model'):
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# sqlitedb.create_table(
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# 'most_model', columns="ds datetime, most_common_model TEXT")
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# sqlitedb.insert_data('most_model', (datetime.datetime.now().strftime(
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# '%Y-%m-%d %H:%M:%S'), most_common_model,), columns=('ds', 'most_common_model',))
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if is_corr:
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df = corr_feature(df=df)
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# if is_corr:
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# df = corr_feature(df=df)
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df1 = df.copy() # 备份一下，后面特征筛选完之后加入ds y 列用
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logger.info(f"开始训练模型...")
|
||||
row, col = df.shape
|
||||
|
||||
# df1 = df.copy() # 备份一下，后面特征筛选完之后加入ds y 列用
|
||||
# logger.info(f"开始训练模型...")
|
||||
# row, col = df.shape
|
||||
now = datetime.datetime.now().strftime('%Y%m%d%H%M%S')
|
||||
ex_Model_Juxiting(df,
|
||||
horizon=global_config['horizon'],
|
||||
input_size=global_config['input_size'],
|
||||
train_steps=global_config['train_steps'],
|
||||
val_check_steps=global_config['val_check_steps'],
|
||||
early_stop_patience_steps=global_config['early_stop_patience_steps'],
|
||||
is_debug=global_config['is_debug'],
|
||||
dataset=global_config['dataset'],
|
||||
is_train=global_config['is_train'],
|
||||
is_fivemodels=global_config['is_fivemodels'],
|
||||
val_size=global_config['val_size'],
|
||||
test_size=global_config['test_size'],
|
||||
settings=global_config['settings'],
|
||||
now=now,
|
||||
etadata=etadata,
|
||||
modelsindex=global_config['modelsindex'],
|
||||
data=data,
|
||||
is_eta=global_config['is_eta'],
|
||||
end_time=global_config['end_time'],
|
||||
)
|
||||
|
||||
# now = datetime.datetime.now().strftime('%Y%m%d%H%M%S')
|
||||
# ex_Model(df,
|
||||
# horizon=global_config['horizon'],
|
||||
# input_size=global_config['input_size'],
|
||||
# train_steps=global_config['train_steps'],
|
||||
# val_check_steps=global_config['val_check_steps'],
|
||||
# early_stop_patience_steps=global_config['early_stop_patience_steps'],
|
||||
# is_debug=global_config['is_debug'],
|
||||
# dataset=global_config['dataset'],
|
||||
# is_train=global_config['is_train'],
|
||||
# is_fivemodels=global_config['is_fivemodels'],
|
||||
# val_size=global_config['val_size'],
|
||||
# test_size=global_config['test_size'],
|
||||
# settings=global_config['settings'],
|
||||
# now=now,
|
||||
# etadata=etadata,
|
||||
# modelsindex=global_config['modelsindex'],
|
||||
# data=data,
|
||||
# is_eta=global_config['is_eta'],
|
||||
# end_time=global_config['end_time'],
|
||||
# )
|
||||
logger.info('模型训练完成')
|
||||
|
||||
# logger.info('模型训练完成')
|
||||
logger.info('训练数据绘图ing')
|
||||
model_results3 = model_losss_juxiting(
|
||||
sqlitedb, end_time=global_config['end_time'], is_fivemodels=global_config['is_fivemodels'])
|
||||
logger.info('训练数据绘图end')
|
||||
|
||||
# logger.info('训练数据绘图ing')
|
||||
# model_results3 = model_losss_juxiting(
|
||||
# sqlitedb, end_time=global_config['end_time'], is_fivemodels=global_config['is_fivemodels'])
|
||||
# logger.info('训练数据绘图end')
|
||||
push_market_value()
|
||||
|
||||
# push_market_value()
|
||||
sql_inset_predict(global_config)
|
||||
|
||||
# sql_inset_predict(global_config)
|
||||
|
||||
# # 模型报告
|
||||
# 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'聚烯烃PP大模型月度预测--{end_time}.pdf' # 报告文件名
|
||||
@ -530,7 +530,7 @@ if __name__ == '__main__':
|
||||
# logger.info(f'预测失败：{e}')
|
||||
# continue
|
||||
|
||||
global_config['end_time'] = '2025-08-01'
|
||||
global_config['end_time'] = '2025-08-04'
|
||||
predict_main()
|
||||
# push_market_value()
|
||||
|
||||
|
||||
@ -513,8 +513,7 @@ if __name__ == '__main__':
|
||||
# continue
|
||||
|
||||
|
||||
global_config['end_time'] = '2025-08-01'
|
||||
global_config['db_mysql'].connect()
|
||||
global_config['end_time'] = '2025-08-04'
|
||||
predict_main()
|
||||
|
||||
|
||||
|
||||
@ -457,12 +457,14 @@ def ex_Model_Juxiting(df, horizon, input_size, train_steps, val_check_steps, ear
|
||||
# 处理非有限值（NA 或 inf），将其替换为 0
|
||||
df_predict = df_predict.fillna(0)
|
||||
df_predict = df_predict.replace([np.inf, -np.inf], 0)
|
||||
df_predict.astype(
|
||||
{col: 'int' for col in df_predict.columns if col not in ['ds']})
|
||||
|
||||
# 添加预测时间
|
||||
df_predict['created_dt'] = end_time
|
||||
|
||||
# 预测结果保留整数（先四舍五入再转换为整数类型）
|
||||
df_predict = df_predict.round().astype({col: 'int' for col in df_predict.columns if col not in ['ds', 'created_dt']})
|
||||
|
||||
|
||||
# 保存预测值
|
||||
df_predict.to_csv(os.path.join(config.dataset, "predict.csv"), index=False)
|
||||
|
||||
|
||||
Loading…
Reference in New Issue
Block a user