2024-12-30 14:00:16 +08:00
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# 读取配置
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2024-12-31 15:27:59 +08:00
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from lib.dataread import *
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2024-12-30 14:00:16 +08:00
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from lib.tools import SendMail,exception_logger
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2024-12-31 15:27:59 +08:00
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from models.nerulforcastmodels import ex_Model,brent_export_pdf,tansuanli_export_pdf,pp_export_pdf,model_losss_yongan
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2024-12-30 14:00:16 +08:00
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import glob
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import torch
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torch.set_float32_matmul_precision("high")
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def predict_main():
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"""
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主预测函数，用于从 ETA 获取数据、处理数据、训练模型并进行预测。
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参数:
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signature (BinanceAPI): Binance API 实例。
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etadata (EtaReader): ETA 数据读取器实例。
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is_eta (bool): 是否从 ETA 获取数据。
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data_set (str): 数据集名称。
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dataset (str): 数据集路径。
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add_kdj (bool): 是否添加 KDJ 指标。
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is_timefurture (bool): 是否添加时间衍生特征。
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end_time (str): 结束时间。
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is_edbnamelist (bool): 是否使用 EDB 名称列表。
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edbnamelist (list): EDB 名称列表。
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y (str): 预测目标列名。
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sqlitedb (SQLiteDB): SQLite 数据库实例。
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is_corr (bool): 是否进行相关性分析。
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horizon (int): 预测时域。
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input_size (int): 输入数据大小。
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train_steps (int): 训练步数。
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val_check_steps (int): 验证检查步数。
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early_stop_patience_steps (int): 早停耐心步数。
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is_debug (bool): 是否调试模式。
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dataset (str): 数据集名称。
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is_train (bool): 是否训练模型。
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is_fivemodels (bool): 是否使用五个模型。
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val_size (float): 验证集大小。
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test_size (float): 测试集大小。
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settings (dict): 模型设置。
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now (str): 当前时间。
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etadata (EtaReader): ETA 数据读取器实例。
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modelsindex (list): 模型索引列表。
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data (str): 数据类型。
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is_eta (bool): 是否从 ETA 获取数据。
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返回:
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None
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"""
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# global end_time
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# signature = BinanceAPI(APPID, SECRET)
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# etadata = EtaReader(signature=signature,
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# classifylisturl=classifylisturl,
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# classifyidlisturl=classifyidlisturl,
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# edbcodedataurl=edbcodedataurl,
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# edbcodelist=edbcodelist,
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# edbdatapushurl=edbdatapushurl,
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# edbdeleteurl=edbdeleteurl,
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# edbbusinessurl=edbbusinessurl
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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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# signature = BinanceAPI(APPID, SECRET)
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# etadata = EtaReader(signature=signature,
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# classifylisturl=classifylisturl,
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# classifyidlisturl=classifyidlisturl,
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# edbcodedataurl=edbcodedataurl,
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# edbcodelist=edbcodelist,
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# edbdatapushurl=edbdatapushurl,
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# edbdeleteurl=edbdeleteurl,
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# edbbusinessurl=edbbusinessurl,
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# )
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# df_zhibiaoshuju, df_zhibiaoliebiao = etadata.get_eta_api_yuanyou_data(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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# df_zhibiaoshuju = get_market_data(end_time,df_zhibiaoshuju)
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# except :
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# logger.info('从市场信息平台获取数据失败')
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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=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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# print(first_row['ds'].values[0])
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# print(first_row['y'].values[0])
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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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# row_dict['ds'] = row_dict['ds'].strftime('%Y-%m-%d %H:%M:%S')
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# check_query = sqlitedb.select_data('trueandpredict', where_condition=f"ds = '{row.ds}'")
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# if len(check_query) > 0:
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# set_clause = ", ".join([f"{key} = '{value}'" for key, value in row_dict.items()])
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# sqlitedb.update_data('trueandpredict', set_clause, where_condition=f"ds = '{row.ds}'")
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# continue
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# sqlitedb.insert_data('trueandpredict', tuple(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('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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# 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('accuracy', f"y = {yy},LOW_PRICE = {LOW},HIGH_PRICE = {HIGH}", where_condition=f"ds = '{row_dict['ds']}'")
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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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# import datetime
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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('trueandpredict', order_by="ds DESC", limit="60")
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# # 删除空值率为40%以上的列
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# if len(model_results) > 10:
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# model_results = model_results.dropna(thresh=len(model_results)*0.6,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:]
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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(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(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(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(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('most_model', columns="ds datetime, most_common_model TEXT")
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# sqlitedb.insert_data('most_model', (datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S'), most_common_model,), columns=('ds', 'most_common_model',))
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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 ][['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', '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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# engine = create_engine(f'mysql+pymysql://{dbusername}:{password}@{host}:{port}/{dbname}')
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# warning_data_df['WARNING_DATE'] = datetime.date.today().strftime("%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(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(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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# 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...")
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# row, col = df.shape
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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=horizon,
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# input_size=input_size,
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# train_steps=train_steps,
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# val_check_steps=val_check_steps,
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# early_stop_patience_steps=early_stop_patience_steps,
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# is_debug=is_debug,
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# dataset=dataset,
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# is_train=is_train,
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# is_fivemodels=is_fivemodels,
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# val_size=val_size,
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# test_size=test_size,
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# settings=settings,
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# now=now,
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# etadata=etadata,
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# modelsindex=modelsindex,
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# data=data,
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# is_eta=is_eta,
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# end_time=end_time,
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# )
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# logger.info('模型训练完成')
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# logger.info('训练数据绘图ing')
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2024-12-31 15:27:59 +08:00
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# model_results3 = model_losss_yongan(sqlitedb,end_time=end_time,table_name_prefix=table_name_prefix)
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2024-12-30 14:00:16 +08:00
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# logger.info('训练数据绘图end')
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# 模型报告
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logger.info('制作报告ing')
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title = f'{settings}--{end_time}-预测报告' # 报告标题
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reportname = f'Brent原油大模型预测--{end_time}.pdf' # 报告文件名
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reportname = reportname.replace(':', '-') # 替换冒号
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brent_export_pdf(dataset=dataset,num_models = 5 if is_fivemodels else 22,time=end_time,
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reportname=reportname,sqlitedb=sqlitedb),
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logger.info('制作报告end')
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logger.info('模型训练完成')
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# # LSTM 单变量模型
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# ex_Lstm(df,input_seq_len=input_size,output_seq_len=horizon,is_debug=is_debug,dataset=dataset)
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# # lstm 多变量模型
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# ex_Lstm_M(df,n_days=input_size,out_days=horizon,is_debug=is_debug,datasetpath=dataset)
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# # GRU 模型
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# # ex_GRU(df)
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# 发送邮件
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# m = SendMail(
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# username=username,
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# passwd=passwd,
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# recv=recv,
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# title=title,
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# content=content,
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# file=max(glob.glob(os.path.join(dataset,'*.pdf')), key=os.path.getctime),
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# ssl=ssl,
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# )
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# m.send_mail()
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if __name__ == '__main__':
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2024-12-30 15:29:36 +08:00
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# global end_time
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# is_on = True
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# # 遍历2024-11-25 到 2024-12-3 之间的工作日日期
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# for i_time in pd.date_range('2024-12-27', '2024-12-28', freq='B'):
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# end_time = i_time.strftime('%Y-%m-%d')
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# predict_main()
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predict_main()
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