周度预测调试
This commit is contained in:
jingboyitiji
parent
4b0b976d2a
commit
c78c6fc3ef
@ -174,230 +174,230 @@ def predict_main():
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返回:
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返回:
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None
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None
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"""
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"""
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# end_time = global_config['end_time']
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end_time = global_config['end_time']
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# signature = BinanceAPI(APPID, SECRET)
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signature = BinanceAPI(APPID, SECRET)
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# etadata = EtaReader(signature=signature,
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etadata = EtaReader(signature=signature,
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# classifylisturl=global_config['classifylisturl'],
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classifylisturl=global_config['classifylisturl'],
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# classifyidlisturl=global_config['classifyidlisturl'],
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classifyidlisturl=global_config['classifyidlisturl'],
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# edbcodedataurl=global_config['edbcodedataurl'],
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edbcodedataurl=global_config['edbcodedataurl'],
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# edbcodelist=global_config['edbcodelist'],
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edbcodelist=global_config['edbcodelist'],
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# edbdatapushurl=global_config['edbdatapushurl'],
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edbdatapushurl=global_config['edbdatapushurl'],
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# edbdeleteurl=global_config['edbdeleteurl'],
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edbdeleteurl=global_config['edbdeleteurl'],
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# edbbusinessurl=global_config['edbbusinessurl'],
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edbbusinessurl=global_config['edbbusinessurl'],
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# classifyId=global_config['ClassifyId'],
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classifyId=global_config['ClassifyId'],
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# )
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)
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# # 获取数据
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# 获取数据
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# if is_eta:
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if is_eta:
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# logger.info('从eta获取数据...')
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logger.info('从eta获取数据...')
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# df_zhibiaoshuju, df_zhibiaoliebiao = etadata.get_eta_api_yuanyou_data(
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df_zhibiaoshuju, df_zhibiaoliebiao = etadata.get_eta_api_yuanyou_data(
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# data_set=data_set, dataset=dataset) # 原始数据，未处理
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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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if is_market:
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# logger.info('从市场信息平台获取数据...')
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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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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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# 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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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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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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df_zhibiaoshuju = get_high_low_data(df_zhibiaoshuju)
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# else:
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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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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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df_zhibiaoshuju = get_market_data(
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# end_time, df_zhibiaoshuju)
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end_time, df_zhibiaoshuju)
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# except:
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except:
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# logger.info('最高最低价拼接失败')
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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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# 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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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_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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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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# 数据处理
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# df = datachuli(df_zhibiaoshuju, df_zhibiaoliebiao, y=y, dataset=dataset, add_kdj=add_kdj, is_timefurture=is_timefurture,
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df = datachuli(df_zhibiaoshuju, df_zhibiaoliebiao, y=y, dataset=dataset, add_kdj=add_kdj, is_timefurture=is_timefurture,
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# end_time=end_time)
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end_time=end_time)
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# else:
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else:
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# # 读取数据
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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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logger.info('读取本地数据：' + os.path.join(dataset, data_set))
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# df, df_zhibiaoliebiao = getdata(filename=os.path.join(dataset, data_set), y=y, dataset=dataset, add_kdj=add_kdj,
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df, df_zhibiaoliebiao = getdata(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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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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# 更改预测列名称
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# df.rename(columns={y: 'y'}, inplace=True)
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df.rename(columns={y: 'y'}, inplace=True)
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# if is_edbnamelist:
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if is_edbnamelist:
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# df = df[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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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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# 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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# 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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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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# 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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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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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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return None
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# # 将最新真实值保存到数据库
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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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if not sqlitedb.check_table_exists('trueandpredict'):
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# first_row.to_sql('trueandpredict', sqlitedb.connection, index=False)
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first_row.to_sql('trueandpredict', sqlitedb.connection, index=False)
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# else:
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else:
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# for row in first_row.itertuples(index=False):
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for row in first_row.itertuples(index=False):
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# row_dict = row._asdict()
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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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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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# 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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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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# 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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# try:
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# row_dict['ds'] = pd.to_datetime(
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for row in update_y.itertuples(index=False):
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# row_dict['ds']).strftime('%Y-%m-%d')
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try:
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# except:
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row_dict = row._asdict()
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# logger.warning(f"无法解析的时间格式: {row_dict['ds']}")
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yy = df[df['ds'] == row_dict['ds']]['y'].values[0]
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# # row_dict['ds'] = row_dict['ds'].strftime('%Y-%m-%d')
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LOW = df[df['ds'] == row_dict['ds']]['Brentzdj'].values[0]
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# # row_dict['ds'] = row_dict['ds'].strftime('%Y-%m-%d %H:%M:%S')
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HIGH = df[df['ds'] == row_dict['ds']]['Brentzgj'].values[0]
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# check_query = sqlitedb.select_data(
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sqlitedb.update_data(
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# 'trueandpredict', where_condition=f"ds = '{row.ds}'")
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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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# if len(check_query) > 0:
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except:
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# set_clause = ", ".join(
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logger.info(f'更新accuracy表的y值失败：{row_dict}')
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# [f"{key} = '{value}'" for key, value in row_dict.items()])
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# except Exception as e:
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# sqlitedb.update_data(
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# logger.info(f'更新accuracy表的y值失败：{e}')
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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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# 判断当前日期是不是周一
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# if not sqlitedb.check_table_exists('accuracy'):
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is_weekday = datetime.datetime.now().weekday() == 0
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# pass
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if is_weekday:
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# else:
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logger.info('今天是周一，更新预测模型')
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# update_y = sqlitedb.select_data(
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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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# 'accuracy', where_condition="y is null")
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model_results = sqlitedb.select_data(
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# if len(update_y) > 0:
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'trueandpredict', order_by="ds DESC", limit="60")
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# logger.info('更新accuracy表的y值')
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# 删除空值率为90%以上的列
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# # 找到update_y 中ds且df中的y的行
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if len(model_results) > 10:
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# update_y = update_y[update_y['ds'] <= end_time]
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model_results = model_results.dropna(
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# logger.info(f'要更新y的信息：{update_y}')
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thresh=len(model_results)*0.1, axis=1)
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# # try:
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# 删除空行
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# for row in update_y.itertuples(index=False):
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model_results = model_results.dropna()
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# try:
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modelnames = model_results.columns.to_list()[2:-2]
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# row_dict = row._asdict()
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for col in model_results[modelnames].select_dtypes(include=['object']).columns:
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# yy = df[df['ds'] == row_dict['ds']]['y'].values[0]
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model_results[col] = model_results[col].astype(np.float32)
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# LOW = df[df['ds'] == row_dict['ds']]['Brentzdj'].values[0]
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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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# HIGH = df[df['ds'] == row_dict['ds']]['Brentzgj'].values[0]
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for model in modelnames:
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# sqlitedb.update_data(
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model_results[f'{model}_abs_error_rate'] = abs(
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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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model_results['y'] - model_results[model]) / model_results['y']
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# except:
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# 获取每行对应的最小偏差率值
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# logger.info(f'更新accuracy表的y值失败：{row_dict}')
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min_abs_error_rate_values = model_results.apply(
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# # except Exception as e:
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lambda row: row[[f'{model}_abs_error_rate' for model in modelnames]].min(), axis=1)
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# # logger.info(f'更新accuracy表的y值失败：{e}')
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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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try:
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# is_weekday = datetime.datetime.now().weekday() == 0
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if is_weekday:
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# if is_weekday:
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# if True:
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# logger.info('今天是周一，更新预测模型')
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logger.info('今天是周一，发送特征预警')
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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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# 上传预警信息到数据库
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# model_results = sqlitedb.select_data(
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warning_data_df = df_zhibiaoliebiao.copy()
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# 'trueandpredict', order_by="ds DESC", limit="60")
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warning_data_df = warning_data_df[warning_data_df['停更周期'] > 3][[
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# # 删除空值率为90%以上的列
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'指标名称', '指标id', '频度', '更新周期', '指标来源', '最后更新时间', '停更周期']]
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# if len(model_results) > 10:
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# 重命名列名
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# model_results = model_results.dropna(
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warning_data_df = warning_data_df.rename(columns={'指标名称': 'INDICATOR_NAME', '指标id': 'INDICATOR_ID', '频度': 'FREQUENCY',
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# thresh=len(model_results)*0.1, axis=1)
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'更新周期': 'UPDATE_FREQUENCY', '指标来源': 'DATA_SOURCE', '最后更新时间': 'LAST_UPDATE_DATE', '停更周期': 'UPDATE_SUSPENSION_CYCLE'})
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# # 删除空行
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from sqlalchemy import create_engine
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# model_results = model_results.dropna()
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import urllib
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# modelnames = model_results.columns.to_list()[2:-2]
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global password
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# for col in model_results[modelnames].select_dtypes(include=['object']).columns:
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if '@' in password:
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# model_results[col] = model_results[col].astype(np.float32)
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password = urllib.parse.quote_plus(password)
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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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# try:
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engine = create_engine(
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# if is_weekday:
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f'mysql+pymysql://{dbusername}:{password}@{host}:{port}/{dbname}')
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# # if True:
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warning_data_df['WARNING_DATE'] = datetime.date.today().strftime(
|
||||||
# logger.info('今天是周一，发送特征预警')
|
"%Y-%m-%d %H:%M:%S")
|
||||||
# # 上传预警信息到数据库
|
warning_data_df['TENANT_CODE'] = 'T0004'
|
||||||
# warning_data_df = df_zhibiaoliebiao.copy()
|
# 插入数据之前查询表数据然后新增id列
|
||||||
# warning_data_df = warning_data_df[warning_data_df['停更周期'] > 3][[
|
existing_data = pd.read_sql(f"SELECT * FROM {table_name}", engine)
|
||||||
# '指标名称', '指标id', '频度', '更新周期', '指标来源', '最后更新时间', '停更周期']]
|
if not existing_data.empty:
|
||||||
# # 重命名列名
|
max_id = existing_data['ID'].astype(int).max()
|
||||||
# warning_data_df = warning_data_df.rename(columns={'指标名称': 'INDICATOR_NAME', '指标id': 'INDICATOR_ID', '频度': 'FREQUENCY',
|
warning_data_df['ID'] = range(
|
||||||
# '更新周期': 'UPDATE_FREQUENCY', '指标来源': 'DATA_SOURCE', '最后更新时间': 'LAST_UPDATE_DATE', '停更周期': 'UPDATE_SUSPENSION_CYCLE'})
|
max_id + 1, max_id + 1 + len(warning_data_df))
|
||||||
# from sqlalchemy import create_engine
|
else:
|
||||||
# import urllib
|
warning_data_df['ID'] = range(1, 1 + len(warning_data_df))
|
||||||
# global password
|
warning_data_df.to_sql(
|
||||||
# if '@' in password:
|
table_name, con=engine, if_exists='append', index=False)
|
||||||
# password = urllib.parse.quote_plus(password)
|
if is_update_warning_data:
|
||||||
|
upload_warning_info(len(warning_data_df))
|
||||||
|
except:
|
||||||
|
logger.info('上传预警信息到数据库失败')
|
||||||
|
|
||||||
# engine = create_engine(
|
if is_corr:
|
||||||
# f'mysql+pymysql://{dbusername}:{password}@{host}:{port}/{dbname}')
|
df = corr_feature(df=df)
|
||||||
# warning_data_df['WARNING_DATE'] = datetime.date.today().strftime(
|
|
||||||
# "%Y-%m-%d %H:%M:%S")
|
|
||||||
# warning_data_df['TENANT_CODE'] = 'T0004'
|
|
||||||
# # 插入数据之前查询表数据然后新增id列
|
|
||||||
# existing_data = pd.read_sql(f"SELECT * FROM {table_name}", engine)
|
|
||||||
# if not existing_data.empty:
|
|
||||||
# max_id = existing_data['ID'].astype(int).max()
|
|
||||||
# warning_data_df['ID'] = range(
|
|
||||||
# max_id + 1, max_id + 1 + len(warning_data_df))
|
|
||||||
# else:
|
|
||||||
# warning_data_df['ID'] = range(1, 1 + len(warning_data_df))
|
|
||||||
# warning_data_df.to_sql(
|
|
||||||
# table_name, con=engine, if_exists='append', index=False)
|
|
||||||
# if is_update_warning_data:
|
|
||||||
# upload_warning_info(len(warning_data_df))
|
|
||||||
# except:
|
|
||||||
# logger.info('上传预警信息到数据库失败')
|
|
||||||
|
|
||||||
# if is_corr:
|
df1 = df.copy() # 备份一下，后面特征筛选完之后加入ds y 列用
|
||||||
# df = corr_feature(df=df)
|
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
|
now = datetime.datetime.now().strftime('%Y%m%d%H%M%S')
|
||||||
# logger.info(f"开始训练模型...")
|
ex_Model(df,
|
||||||
# row, col = df.shape
|
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')
|
logger.info('模型训练完成')
|
||||||
# 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('训练数据绘图ing')
|
logger.info('训练数据绘图ing')
|
||||||
model_results3 = model_losss(sqlitedb, end_time=end_time)
|
model_results3 = model_losss(sqlitedb, end_time=end_time)
|
||||||
@ -415,14 +415,6 @@ def predict_main():
|
|||||||
logger.info('模型训练完成')
|
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)
|
|
||||||
|
|
||||||
# # lstm 多变量模型
|
|
||||||
# ex_Lstm_M(df,n_days=input_size,out_days=horizon,is_debug=is_debug,datasetpath=dataset)
|
|
||||||
|
|
||||||
# # GRU 模型
|
|
||||||
# # ex_GRU(df)
|
|
||||||
|
|
||||||
# 发送邮件
|
# 发送邮件
|
||||||
# m = SendMail(
|
# m = SendMail(
|
||||||
|
|||||||
Loading…
Reference in New Issue
Block a user