石油焦日度预测调试
This commit is contained in:
jingboyitiji
parent
313e9e229d
commit
7cf3cda87a
@ -3,7 +3,7 @@
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from lib.dataread import *
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from config_shiyoujiao_lvyong import *
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from lib.tools import SendMail, exception_logger
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from models.nerulforcastmodels import ex_Model, model_losss, model_losss_juxiting, brent_export_pdf, tansuanli_export_pdf, pp_export_pdf, model_losss_juxiting
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from models.nerulforcastmodels import model_losss, shiyoujiao_lvyong_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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@ -173,228 +173,228 @@ 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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# 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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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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df_zhibiaoshuju, df_zhibiaoliebiao = etadata.get_eta_api_shiyoujiao_lvyong_data(
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data_set=data_set, dataset=dataset) # 原始数据，未处理
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# end_time = global_config['end_time']
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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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# 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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# df_zhibiaoshuju, df_zhibiaoliebiao = etadata.get_eta_api_shiyoujiao_lvyong_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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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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# 数据处理
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df = datachuli(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(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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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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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 %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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# 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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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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# # 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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# 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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# except:
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# logger.info('最高最低价拼接失败')
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try:
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if is_weekday:
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# if True:
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logger.info('今天是周一，发送特征预警')
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# 上传预警信息到数据库
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warning_data_df = df_zhibiaoliebiao.copy()
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warning_data_df = warning_data_df[warning_data_df['停更周期'] > 3][[
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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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# 重命名列名
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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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'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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from sqlalchemy import create_engine
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import urllib
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global password
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if '@' in password:
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password = urllib.parse.quote_plus(password)
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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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engine = create_engine(
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f'mysql+pymysql://{dbusername}:{password}@{host}:{port}/{dbname}')
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warning_data_df['WARNING_DATE'] = datetime.date.today().strftime(
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"%Y-%m-%d %H:%M:%S")
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warning_data_df['TENANT_CODE'] = 'T0004'
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# 插入数据之前查询表数据然后新增id列
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existing_data = pd.read_sql(f"SELECT * FROM {table_name}", engine)
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if not existing_data.empty:
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max_id = existing_data['ID'].astype(int).max()
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warning_data_df['ID'] = range(
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max_id + 1, max_id + 1 + len(warning_data_df))
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else:
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warning_data_df['ID'] = range(1, 1 + len(warning_data_df))
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warning_data_df.to_sql(
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table_name, con=engine, if_exists='append', index=False)
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if is_update_warning_data:
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upload_warning_info(len(warning_data_df))
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except:
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logger.info('上传预警信息到数据库失败')
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# # 数据处理
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# df = datachuli(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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if is_corr:
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df = corr_feature(df=df)
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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(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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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...")
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row, col = df.shape
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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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now = datetime.datetime.now().strftime('%Y%m%d%H%M%S')
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ex_Model(df,
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horizon=global_config['horizon'],
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input_size=global_config['input_size'],
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train_steps=global_config['train_steps'],
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val_check_steps=global_config['val_check_steps'],
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early_stop_patience_steps=global_config['early_stop_patience_steps'],
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is_debug=global_config['is_debug'],
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dataset=global_config['dataset'],
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is_train=global_config['is_train'],
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is_fivemodels=global_config['is_fivemodels'],
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val_size=global_config['val_size'],
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test_size=global_config['test_size'],
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settings=global_config['settings'],
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now=now,
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etadata=global_config['etadata'],
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modelsindex=global_config['modelsindex'],
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data=data,
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is_eta=global_config['is_eta'],
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end_time=global_config['end_time'],
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)
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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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logger.info('模型训练完成')
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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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# # 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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# 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']}'")
|
||||
# except:
|
||||
# logger.info(f'更新accuracy表的y值失败：{row_dict}')
|
||||
# # except Exception as e:
|
||||
# # logger.info(f'更新accuracy表的y值失败：{e}')
|
||||
|
||||
# # 判断当前日期是不是周一
|
||||
# is_weekday = datetime.datetime.now().weekday() == 0
|
||||
# if is_weekday:
|
||||
# logger.info('今天是周一，更新预测模型')
|
||||
# # 计算最近60天预测残差最低的模型名称
|
||||
# model_results = sqlitedb.select_data(
|
||||
# 'trueandpredict', order_by="ds DESC", limit="60")
|
||||
# # 删除空值率为90%以上的列
|
||||
# if len(model_results) > 10:
|
||||
# model_results = model_results.dropna(
|
||||
# thresh=len(model_results)*0.1, axis=1)
|
||||
# # 删除空行
|
||||
# model_results = model_results.dropna()
|
||||
# modelnames = model_results.columns.to_list()[2:-1]
|
||||
# for col in model_results[modelnames].select_dtypes(include=['object']).columns:
|
||||
# model_results[col] = model_results[col].astype(np.float32)
|
||||
# # 计算每个预测值与真实值之间的偏差率
|
||||
# for model in modelnames:
|
||||
# model_results[f'{model}_abs_error_rate'] = abs(
|
||||
# model_results['y'] - model_results[model]) / model_results['y']
|
||||
# # 获取每行对应的最小偏差率值
|
||||
# min_abs_error_rate_values = model_results.apply(
|
||||
# lambda row: row[[f'{model}_abs_error_rate' for model in modelnames]].min(), axis=1)
|
||||
# # 获取每行对应的最小偏差率值对应的列名
|
||||
# min_abs_error_rate_column_name = model_results.apply(
|
||||
# lambda row: row[[f'{model}_abs_error_rate' for model in modelnames]].idxmin(), axis=1)
|
||||
# # 将列名索引转换为列名
|
||||
# min_abs_error_rate_column_name = min_abs_error_rate_column_name.map(
|
||||
# lambda x: x.split('_')[0])
|
||||
# # 取出现次数最多的模型名称
|
||||
# most_common_model = min_abs_error_rate_column_name.value_counts().idxmax()
|
||||
# logger.info(f"最近60天预测残差最低的模型名称：{most_common_model}")
|
||||
# # 保存结果到数据库
|
||||
# if not sqlitedb.check_table_exists('most_model'):
|
||||
# sqlitedb.create_table(
|
||||
# 'most_model', columns="ds datetime, most_common_model TEXT")
|
||||
# sqlitedb.insert_data('most_model', (datetime.datetime.now().strftime(
|
||||
# '%Y-%m-%d %H:%M:%S'), most_common_model,), columns=('ds', 'most_common_model',))
|
||||
|
||||
# try:
|
||||
# if is_weekday:
|
||||
# # if True:
|
||||
# logger.info('今天是周一，发送特征预警')
|
||||
# # 上传预警信息到数据库
|
||||
# warning_data_df = df_zhibiaoliebiao.copy()
|
||||
# warning_data_df = warning_data_df[warning_data_df['停更周期'] > 3][[
|
||||
# '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']]
|
||||
# # 重命名列名
|
||||
# warning_data_df = warning_data_df.rename(columns={'指标名称': 'INDICATOR_NAME', '指标id': 'INDICATOR_ID', '频度': 'FREQUENCY',
|
||||
# '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'})
|
||||
# from sqlalchemy import create_engine
|
||||
# import urllib
|
||||
# global password
|
||||
# if '@' in password:
|
||||
# password = urllib.parse.quote_plus(password)
|
||||
|
||||
# engine = create_engine(
|
||||
# f'mysql+pymysql://{dbusername}:{password}@{host}:{port}/{dbname}')
|
||||
# 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:
|
||||
# df = corr_feature(df=df)
|
||||
|
||||
# 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(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=global_config['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')
|
||||
model_results3 = model_losss(sqlitedb, end_time=end_time)
|
||||
@ -403,15 +403,15 @@ def predict_main():
|
||||
# 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原油大模型日度预测--{end_time}.pdf' # 报告文件名
|
||||
reportname = f'石油焦铝用大模型日度预测--{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,
|
||||
shiyoujiao_lvyong_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')
|
||||
|
||||
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)
|
||||
|
||||
@ -866,7 +866,7 @@ def model_losss_yongan(sqlitedb, end_time, table_name_prefix):
|
||||
plt.text(i, j, str(j), ha='center', va='bottom')
|
||||
|
||||
# 当前日期画竖虚线
|
||||
plt.axvline(x=df['ds'].iloc[-horizon], color='r', linestyle='--')
|
||||
plt.axvline(x=df['ds'].iloc[-config.horizon], color='r', linestyle='--')
|
||||
plt.legend()
|
||||
plt.xlabel('日期')
|
||||
plt.ylabel('价格')
|
||||
@ -881,8 +881,8 @@ def model_losss_yongan(sqlitedb, end_time, table_name_prefix):
|
||||
ax.axis('off') # 关闭坐标轴
|
||||
# 数值保留2位小数
|
||||
df = df.round(2)
|
||||
df = df[-horizon:]
|
||||
df['Day'] = [f'Day_{i}' for i in range(1, horizon+1)]
|
||||
df = df[-config.horizon:]
|
||||
df['Day'] = [f'Day_{i}' for i in range(1, config.horizon+1)]
|
||||
# Day列放到最前面
|
||||
df = df[['Day'] + list(df.columns[:-1])]
|
||||
table = ax.table(cellText=df.values,
|
||||
@ -1297,7 +1297,7 @@ def model_losss(sqlitedb, end_time):
|
||||
# plt.plot(df['ds'], df[model], label=model,marker='o')
|
||||
plt.plot(df['ds'], df[most_model_name], label=model, marker='o')
|
||||
# 当前日期画竖虚线
|
||||
plt.axvline(x=df['ds'].iloc[-horizon], color='r', linestyle='--')
|
||||
plt.axvline(x=df['ds'].iloc[-config.horizon], color='r', linestyle='--')
|
||||
plt.legend()
|
||||
plt.xlabel('日期')
|
||||
# 设置横轴日期格式为年-月-日
|
||||
@ -1338,7 +1338,7 @@ def model_losss(sqlitedb, end_time):
|
||||
plt.text(i, j, str(j), ha='center', va='bottom')
|
||||
|
||||
# 当前日期画竖虚线
|
||||
plt.axvline(x=df['ds'].iloc[-horizon], color='r', linestyle='--')
|
||||
plt.axvline(x=df['ds'].iloc[-config.horizon], color='r', linestyle='--')
|
||||
plt.legend()
|
||||
plt.xlabel('日期')
|
||||
# 自动设置横轴日期显示
|
||||
@ -1357,8 +1357,8 @@ def model_losss(sqlitedb, end_time):
|
||||
ax.axis('off') # 关闭坐标轴
|
||||
# 数值保留2位小数
|
||||
df = df.round(2)
|
||||
df = df[-horizon:]
|
||||
df['Day'] = [f'Day_{i}' for i in range(1, horizon+1)]
|
||||
df = df[-config.horizon:]
|
||||
df['Day'] = [f'Day_{i}' for i in range(1, config.horizon+1)]
|
||||
# Day列放到最前面
|
||||
df = df[['Day'] + list(df.columns[:-1])]
|
||||
table = ax.table(cellText=df.values,
|
||||
@ -1388,10 +1388,10 @@ def model_losss(sqlitedb, end_time):
|
||||
bbox_inches='tight')
|
||||
plt.close()
|
||||
|
||||
# _plt_predict_ture(df_combined3)
|
||||
_plt_predict_ture(df_combined3)
|
||||
# _plt_modeltopten_predict_ture(df_combined4)
|
||||
# _plt_predict_table(df_combined3)
|
||||
# _plt_model_results3()
|
||||
_plt_predict_table(df_combined3)
|
||||
_plt_model_results3()
|
||||
|
||||
return model_results3
|
||||
|
||||
@ -2461,6 +2461,319 @@ def brent_export_pdf(num_indicators=475, num_models=21, num_dayindicator=202, in
|
||||
print(f"请求超时: {e}")
|
||||
|
||||
|
||||
@exception_logger
|
||||
def shiyoujiao_lvyong_export_pdf(num_indicators=475, num_models=21, num_dayindicator=202, inputsize=5, dataset='dataset', time='2024-07-30', reportname='report.pdf', sqlitedb='jbsh_yuanyou.db'):
|
||||
global y
|
||||
# 创建内容对应的空列表
|
||||
content = list()
|
||||
# 获取特征的近一月值
|
||||
import pandas as pd
|
||||
feature_data_df = pd.read_csv(os.path.join(
|
||||
config.dataset,'指标数据添加时间特征.csv'), parse_dates=['ds']).tail(60)
|
||||
|
||||
def draw_feature_trend(feature_data_df, features):
|
||||
# 画特征近60天的趋势图
|
||||
feature_df = feature_data_df[['ds', 'y']+features]
|
||||
# 遍历X每一列，和yy画散点图 ，
|
||||
|
||||
for i, col in enumerate(features):
|
||||
# try:
|
||||
print(f'正在绘制第{i+1}个特征{col}与价格散点图...')
|
||||
if col not in ['ds', 'y']:
|
||||
fig, ax1 = plt.subplots(figsize=(10, 6))
|
||||
# 在第一个坐标轴上绘制数据
|
||||
sns.lineplot(data=feature_df, x='ds', y='y', ax=ax1, color='b')
|
||||
ax1.set_xlabel('日期')
|
||||
ax1.set_ylabel('y', color='b')
|
||||
ax1.tick_params('y', colors='b')
|
||||
# 在 ax1 上添加文本显示值，添加一定的偏移避免值与曲线重叠
|
||||
for j in range(1, len(feature_df), 2):
|
||||
value = feature_df['y'].iloc[j]
|
||||
date = feature_df['ds'].iloc[j]
|
||||
offset = 1.001
|
||||
ax1.text(date, value * offset, str(round(value, 2)),
|
||||
ha='center', va='bottom', color='b', fontsize=10)
|
||||
# 创建第二个坐标轴
|
||||
ax2 = ax1.twinx()
|
||||
# 在第二个坐标轴上绘制数据
|
||||
sns.lineplot(data=feature_df, x='ds', y=col, ax=ax2, color='r')
|
||||
ax2.set_ylabel(col, color='r')
|
||||
ax2.tick_params('y', colors='r')
|
||||
# 在 ax2 上添加文本显示值，添加一定的偏移避免值与曲线重叠
|
||||
for j in range(0, len(feature_df), 2):
|
||||
value = feature_df[col].iloc[j]
|
||||
date = feature_df['ds'].iloc[j]
|
||||
offset = 1.0003
|
||||
ax2.text(date, value * offset, str(round(value, 2)),
|
||||
ha='center', va='bottom', color='r', fontsize=10)
|
||||
# 添加标题
|
||||
plt.title(col)
|
||||
# 设置横坐标为日期格式并自动调整
|
||||
locator = mdates.AutoDateLocator()
|
||||
formatter = mdates.AutoDateFormatter(locator)
|
||||
ax1.xaxis.set_major_locator(locator)
|
||||
ax1.xaxis.set_major_formatter(formatter)
|
||||
# 文件名特殊字符处理
|
||||
col = col.replace('*', '-')
|
||||
col = col.replace(':', '-')
|
||||
col = col.replace(r'/', '-')
|
||||
plt.savefig(os.path.join(config.dataset, f'{col}与价格散点图.png'))
|
||||
content.append(Graphs.draw_img(
|
||||
os.path.join(config.dataset, f'{col}与价格散点图.png')))
|
||||
plt.close()
|
||||
# except Exception as e:
|
||||
# print(f'绘制第{i+1}个特征{col}与价格散点图时出错：{e}')
|
||||
|
||||
# 添加标题
|
||||
content.append(Graphs.draw_title(f'{config.y}{time}预测报告'))
|
||||
|
||||
# 预测结果
|
||||
content.append(Graphs.draw_little_title('一、预测结果：'))
|
||||
# 添加历史走势及预测价格的走势图片
|
||||
content.append(Graphs.draw_img(os.path.join(config.dataset, '历史价格-预测值.png')))
|
||||
# 波动率画图逻辑
|
||||
content.append(Graphs.draw_text('图示说明：'))
|
||||
content.append(Graphs.draw_text(
|
||||
' 确定置信区间：设置残差置信阈值，以每周最佳模型为基准，选取在置信区间的预测值作为置信区间;'))
|
||||
|
||||
|
||||
# 取df中y列为空的行
|
||||
import pandas as pd
|
||||
df = pd.read_csv(os.path.join(config.dataset, 'predict.csv'), encoding='gbk')
|
||||
df_true = pd.read_csv(os.path.join(
|
||||
config.dataset,'指标数据添加时间特征.csv'), encoding='utf-8') # 获取预测日期对应的真实值
|
||||
df_true = df_true[['ds', 'y']]
|
||||
eval_df = pd.read_csv(os.path.join(
|
||||
config.dataset,'model_evaluation.csv'), encoding='utf-8')
|
||||
# 按评估指标排序，取前五
|
||||
fivemodels_list = eval_df['模型(Model)'].values # 列表形式，后面当作列名索引使用
|
||||
# 取 fivemodels_list 和 ds 列
|
||||
df = df[['ds'] + fivemodels_list.tolist()]
|
||||
# 拼接预测日期对应的真实值
|
||||
df = pd.merge(df, df_true, on='ds', how='left')
|
||||
# 删除全部为nan的列
|
||||
df = df.dropna(how='all', axis=1)
|
||||
# 选择除 'ds' 列外的数值列，并进行类型转换和四舍五入
|
||||
num_cols = [col for col in df.columns if col !=
|
||||
'ds' and pd.api.types.is_numeric_dtype(df[col])]
|
||||
for col in num_cols:
|
||||
df[col] = df[col].astype(float).round(2)
|
||||
# 添加最大值、最小值、平均值三列
|
||||
df['平均值'] = df[num_cols].mean(axis=1).round(2)
|
||||
df['最大值'] = df[num_cols].max(axis=1)
|
||||
df['最小值'] = df[num_cols].min(axis=1)
|
||||
# df转置
|
||||
df = df.T
|
||||
# df重置索引
|
||||
df = df.reset_index()
|
||||
# 添加预测值表格
|
||||
data = df.values.tolist()
|
||||
col_width = 500/len(df.columns)
|
||||
content.append(Graphs.draw_table(col_width, *data))
|
||||
content.append(Graphs.draw_little_title('二、上一预测周期偏差率分析：'))
|
||||
df = pd.read_csv(os.path.join(
|
||||
config.dataset,'testandpredict_groupby.csv'), encoding='utf-8')
|
||||
df4 = df.copy() # 计算偏差率使用
|
||||
# 去掉created_dt 列
|
||||
df4 = df4.drop(columns=['created_dt'])
|
||||
# 计算模型偏差率
|
||||
# 计算各列对于y列的差值百分比
|
||||
df3 = pd.DataFrame() # 存储偏差率
|
||||
|
||||
# 删除有null的行
|
||||
df4 = df4.dropna()
|
||||
df3['ds'] = df4['ds']
|
||||
for col in fivemodels_list:
|
||||
df3[col] = round(abs(df4[col] - df4['y']) / df4['y'] * 100, 2)
|
||||
# 找出决定系数前五的偏差率
|
||||
df3 = df3[['ds']+fivemodels_list.tolist()][-inputsize:]
|
||||
# 找出上一预测区间的时间
|
||||
stime = df3['ds'].iloc[0]
|
||||
etime = df3['ds'].iloc[-1]
|
||||
# 添加偏差率表格
|
||||
fivemodels = '、'.join(eval_df['模型(Model)'].values[:5]) # 字符串形式，后面写入字符串使用
|
||||
content.append(Graphs.draw_text(
|
||||
f'预测使用了{num_models}个模型进行训练，使用评估结果MAE前五的模型分别是 {fivemodels} ，模型上一预测区间 {stime} -- {etime}的偏差率(%)分别是：'))
|
||||
# # 添加偏差率表格
|
||||
df3 = df3.T
|
||||
df3 = df3.reset_index()
|
||||
data = df3.values.tolist()
|
||||
col_width = 500/len(df3.columns)
|
||||
content.append(Graphs.draw_table(col_width, *data))
|
||||
|
||||
content.append(Graphs.draw_little_title('上一周预测准确率：'))
|
||||
df4 = sqlitedb.select_data('accuracy_rote', order_by='结束日期 desc', limit=1)
|
||||
df4 = df4.T
|
||||
df4 = df4.reset_index()
|
||||
df4 = df4.T
|
||||
data = df4.values.tolist()
|
||||
col_width = 500/len(df4.columns)
|
||||
content.append(Graphs.draw_table(col_width, *data))
|
||||
|
||||
content.append(Graphs.draw_little_title('三、预测过程解析：'))
|
||||
# 特征、模型、参数配置
|
||||
content.append(Graphs.draw_little_title('模型选择：'))
|
||||
content.append(Graphs.draw_text(
|
||||
f'本次预测使用了一个专门收集时间序列的NeuralForecast库中的{num_models}个模型：'))
|
||||
content.append(Graphs.draw_text(f'使用40天的数据预测未来{inputsize}天的数据。'))
|
||||
content.append(Graphs.draw_little_title('指标情况：'))
|
||||
with open(os.path.join(config.dataset, '特征频度统计.txt'), encoding='utf-8') as f:
|
||||
for line in f.readlines():
|
||||
content.append(Graphs.draw_text(line))
|
||||
|
||||
data = pd.read_csv(os.path.join(config.dataset, '指标数据添加时间特征.csv'),
|
||||
encoding='utf-8') # 计算相关系数用
|
||||
df_zhibiaofenlei = loadcsv(os.path.join(
|
||||
config.dataset,'特征处理后的指标名称及分类.csv')) # 气泡图用
|
||||
df_zhibiaoshuju = data.copy() # 气泡图用
|
||||
|
||||
# 绘制特征相关气泡图
|
||||
|
||||
grouped = df_zhibiaofenlei.groupby('指标分类')
|
||||
grouped_corr = pd.DataFrame(columns=['指标分类', '指标数量', '相关性总和'])
|
||||
|
||||
content.append(Graphs.draw_little_title('按指标分类分别与预测目标进行皮尔逊相关系数分析：'))
|
||||
content.append(Graphs.draw_text('''皮尔逊相关系数说明：'''))
|
||||
content.append(Graphs.draw_text('''衡量两个特征之间的线性相关性。'''))
|
||||
content.append(Graphs.draw_text('''
|
||||
相关系数为1：表示两个变量之间存在完全正向的线性关系，即当一个变量增加时，另一个变量也相应增加，且变化是完全一致的。'''))
|
||||
content.append(Graphs.draw_text(
|
||||
'''相关系数为-1：表示两个变量之间存在完全负向的线性关系，即当一个变量增加时，另一个变量会相应减少，且变化是完全相反的'''))
|
||||
content.append(Graphs.draw_text(
|
||||
'''相关系数接近0：表示两个变量之间不存在线性关系，即它们的变化不会随着对方的变化而变化。'''))
|
||||
for name, group in grouped:
|
||||
cols = group['指标名称'].tolist()
|
||||
config.logger.info(f'开始绘制{name}类指标的相关性直方图')
|
||||
cols_subset = cols
|
||||
feature_names = ['y'] + cols_subset
|
||||
correlation_matrix = df_zhibiaoshuju[feature_names].corr()['y']
|
||||
|
||||
# 绘制特征相关性直方分布图
|
||||
plt.figure(figsize=(10, 8))
|
||||
sns.histplot(correlation_matrix.values.flatten(),
|
||||
bins=20, kde=True, color='skyblue')
|
||||
plt.title(f'{name}类指标(共{len(cols_subset)}个)相关性直方分布图')
|
||||
plt.xlabel('相关系数')
|
||||
plt.ylabel('频数')
|
||||
plt.savefig(os.path.join(
|
||||
config.dataset,f'{name}类指标相关性直方分布图.png'), bbox_inches='tight')
|
||||
plt.close()
|
||||
content.append(Graphs.draw_img(
|
||||
os.path.join(config.dataset, f'{name}类指标相关性直方分布图.png')))
|
||||
content.append(Graphs.draw_text(
|
||||
f'{name}类指标(共{len(cols_subset)}个)的相关性直方分布图如上所示。'))
|
||||
# 相关性大于0的特征
|
||||
positive_corr_features = correlation_matrix[correlation_matrix > 0].sort_values(
|
||||
ascending=False).index.tolist()[1:]
|
||||
|
||||
print(f'{name}下正相关的特征值有：', positive_corr_features)
|
||||
if len(positive_corr_features) > 5:
|
||||
positive_corr_features = positive_corr_features[0:5]
|
||||
content.append(Graphs.draw_text(
|
||||
f'{name}类指标中，与预测目标y正相关前五的特征有：{positive_corr_features}'))
|
||||
draw_feature_trend(feature_data_df, positive_corr_features)
|
||||
elif len(positive_corr_features) == 0:
|
||||
pass
|
||||
else:
|
||||
positive_corr_features = positive_corr_features
|
||||
content.append(Graphs.draw_text(
|
||||
f'其中，与预测目标y正相关的特征有：{positive_corr_features}'))
|
||||
draw_feature_trend(feature_data_df, positive_corr_features)
|
||||
|
||||
# 相关性小于0的特征
|
||||
negative_corr_features = correlation_matrix[correlation_matrix < 0].sort_values(
|
||||
ascending=True).index.tolist()
|
||||
|
||||
print(f'{name}下负相关的特征值有：', negative_corr_features)
|
||||
if len(negative_corr_features) > 5:
|
||||
negative_corr_features = negative_corr_features[:5]
|
||||
content.append(Graphs.draw_text(
|
||||
f'与预测目标y负相关前五的特征有：{negative_corr_features}'))
|
||||
draw_feature_trend(feature_data_df, negative_corr_features)
|
||||
elif len(negative_corr_features) == 0:
|
||||
pass
|
||||
else:
|
||||
content.append(Graphs.draw_text(
|
||||
f'{name}类指标中，与预测目标y负相关的特征有：{negative_corr_features}'))
|
||||
draw_feature_trend(feature_data_df, negative_corr_features)
|
||||
# 计算correlation_sum 第一行的相关性的绝对值的总和
|
||||
correlation_sum = correlation_matrix.abs().sum()
|
||||
config.logger.info(f'{name}类指标的相关性总和为：{correlation_sum}')
|
||||
# 分组的相关性总和拼接到grouped_corr
|
||||
goup_corr = pd.DataFrame(
|
||||
{'指标分类': [name], '指标数量': [len(cols_subset)], '相关性总和': [correlation_sum]})
|
||||
grouped_corr = pd.concat(
|
||||
[grouped_corr, goup_corr], axis=0, ignore_index=True)
|
||||
|
||||
# 绘制相关性总和的气泡图
|
||||
config.logger.info(f'开始绘制相关性总和的气泡图')
|
||||
plt.figure(figsize=(10, 10))
|
||||
sns.scatterplot(data=grouped_corr, x='相关性总和', y='指标数量', size='相关性总和', sizes=(
|
||||
grouped_corr['相关性总和'].min()*5, grouped_corr['相关性总和'].max()*5), hue='指标分类', palette='viridis')
|
||||
plt.title('指标分类相关性总和的气泡图')
|
||||
plt.ylabel('数量')
|
||||
plt.savefig(os.path.join(config.dataset, '指标分类相关性总和的气泡图.png'),
|
||||
bbox_inches='tight')
|
||||
plt.close()
|
||||
content.append(Graphs.draw_img(os.path.join(config.dataset, '指标分类相关性总和的气泡图.png')))
|
||||
content.append(Graphs.draw_text(
|
||||
'气泡图中，横轴为指标分类，纵轴为指标分类下的特征数量，气泡的面积越大表示该分类中特征的相关系数和越大。'))
|
||||
config.logger.info(f'绘制相关性总和的气泡图结束')
|
||||
content.append(Graphs.draw_little_title('模型选择：'))
|
||||
content.append(Graphs.draw_text(
|
||||
f'预测使用了{num_models}个模型进行训练拟合，通过评估指标MAE从小到大排列，前5个模型的简介如下：'))
|
||||
# 读取模型简介
|
||||
with open(os.path.join(config.dataset, 'model_introduction.txt'), 'r', encoding='utf-8') as f:
|
||||
for line in f:
|
||||
line_split = line.strip().split('--')
|
||||
if line_split[0] in fivemodels_list:
|
||||
for introduction in line_split:
|
||||
content.append(Graphs.draw_text(introduction))
|
||||
content.append(Graphs.draw_little_title('模型评估：'))
|
||||
df = pd.read_csv(os.path.join(
|
||||
config.dataset,'model_evaluation.csv'), encoding='utf-8')
|
||||
# 判断 df 的数值列转为float
|
||||
for col in eval_df.columns:
|
||||
if col not in ['模型(Model)']:
|
||||
eval_df[col] = eval_df[col].astype(float)
|
||||
eval_df[col] = eval_df[col].round(3)
|
||||
# 筛选 fivemodels_list.tolist() 的行
|
||||
eval_df = eval_df[eval_df['模型(Model)'].isin(fivemodels_list)]
|
||||
# df转置
|
||||
eval_df = eval_df.T
|
||||
# df重置索引
|
||||
eval_df = eval_df.reset_index()
|
||||
eval_df = eval_df.T
|
||||
# # 添加表格
|
||||
data = eval_df.values.tolist()
|
||||
col_width = 500/len(eval_df.columns)
|
||||
content.append(Graphs.draw_table(col_width, *data))
|
||||
content.append(Graphs.draw_text('评估指标释义：'))
|
||||
content.append(Graphs.draw_text(
|
||||
'1. 均方根误差(RMSE)：均方根误差是衡量预测值与实际值之间误差的一种方法，取值越小，误差越小，预测效果越好。'))
|
||||
content.append(Graphs.draw_text(
|
||||
'2. 平均绝对误差(MAE)：平均绝对误差是衡量预测值与实际值之间误差的一种方法，取值越小，误差越小，预测效果越好。'))
|
||||
content.append(Graphs.draw_text(
|
||||
'3. 平均平方误差(MSE)：平均平方误差是衡量预测值与实际值之间误差的一种方法，取值越小，误差越小，预测效果越好。'))
|
||||
content.append(Graphs.draw_text('模型拟合：'))
|
||||
# 添加图片
|
||||
content.append(Graphs.draw_img(os.path.join(config.dataset, '预测值与真实值对比图.png')))
|
||||
# 生成pdf文件
|
||||
doc = SimpleDocTemplate(os.path.join(config.dataset, reportname), pagesize=letter)
|
||||
doc.build(content)
|
||||
# pdf 上传到数字化信息平台
|
||||
try:
|
||||
if config.is_update_report:
|
||||
with open(os.path.join(config.dataset, reportname), 'rb') as f:
|
||||
base64_data = base64.b64encode(f.read()).decode('utf-8')
|
||||
upload_data["data"]["fileBase64"] = base64_data
|
||||
upload_data["data"]["fileName"] = reportname
|
||||
token = get_head_auth_report()
|
||||
upload_report_data(token, upload_data)
|
||||
except TimeoutError as e:
|
||||
print(f"请求超时: {e}")
|
||||
|
||||
|
||||
@exception_logger
|
||||
def pp_export_pdf(num_indicators=475, num_models=21, num_dayindicator=202, inputsize=5, dataset='dataset', time='2024-07-30', reportname='report.pdf', sqlitedb='jbsh_yuanyou.db'):
|
||||
# 创建内容对应的空列表
|
||||
|
||||
Loading…
Reference in New Issue
Block a user