聚烯烃日度配置调整
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workpc
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@ -201,17 +201,17 @@ table_name = 'v_tbl_crude_oil_warning'
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# 开关
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is_train = False # 是否训练
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is_train = True # 是否训练
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is_debug = True # 是否调试
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is_eta = True # 是否使用eta接口
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is_eta = False # 是否使用eta接口
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is_market = False # 是否通过市场信息平台获取特征 ,在is_eta 为true 的情况下生效
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is_timefurture = True # 是否使用时间特征
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is_fivemodels = False # 是否使用之前保存的最佳的5个模型
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is_edbcode = False # 特征使用edbcoding列表中的
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is_edbnamelist = False # 自定义特征，对应上面的edbnamelist
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is_update_eta = False # 预测结果上传到eta
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is_update_report = True # 是否上传报告
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is_update_warning_data = True # 是否上传预警数据
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is_update_report = False # 是否上传报告
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is_update_warning_data = False # 是否上传预警数据
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is_del_corr = 0.6 # 是否删除相关性高的特征,取值为 0-1 ，0 为不删除，0.6 表示删除相关性小于0.6的特征
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is_del_tow_month = True # 是否删除两个月不更新的特征
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@ -306,7 +306,7 @@ logger.setLevel(logging.INFO)
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file_handler = logging.handlers.RotatingFileHandler(os.path.join(
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log_dir, 'pricepredict.log'), maxBytes=1024 * 1024, backupCount=5)
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file_handler.setFormatter(logging.Formatter(
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'%(asctime)s - %(name)s - %(levelname)s - %(message)s'))
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'%(asctime)s - %(name)s - %(levelname)s - %(message)s', datefmt='%Y-%m-%d %H:%M:%S'))
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# 配置控制台处理器，将日志打印到控制台
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console_handler = logging.StreamHandler()
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@ -115,37 +115,6 @@ global_config = {
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# 数据库配置
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'sqlitedb': None,
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}
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# logger = global_config['logger']
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# dataset = global_config['dataset']
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# y = global_config['y']
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# data_set = global_config['data_set']
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# input_size = global_config['input_size']
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# horizon = global_config['horizon']
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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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# is_del_corr = global_config['is_del_corr']
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# is_del_tow_month = global_config['is_del_tow_month']
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# is_eta = global_config['is_eta']
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# is_update_eta = global_config['is_update_eta']
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# is_update_eta_data = global_config['is_update_eta_data']
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# start_year = global_config['start_year']
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# end_time = global_config['end_time']
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# freq = global_config['freq'][0]
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# offsite_col = global_config['offsite_col']
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# avg_cols = global_config['avg_col']
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# offsite = global_config['offsite']
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# edbcodenamedict = global_config['edbcodenamedict']
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# query_data_list_item_nos_url = global_config['query_data_list_item_nos_url']
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# query_data_list_item_nos_data = global_config['query_data_list_item_nos_data']
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# config.login_pushreport_url = global_config['config.login_pushreport_url']
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# login_data = global_config['login_data']
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# upload_url = global_config['upload_url']
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# upload_warning_url = global_config['upload_warning_url']
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# upload_warning_data = global_config['upload_warning_data']
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# warning_data = global_config['upload_warning_data']
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# APPID = global_config['APPID']
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# SECRET = global_config['SECRET']
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# 定义函数
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@ -973,14 +942,16 @@ def datachuli_juxiting(df_zhibiaoshuju, df_zhibiaoliebiao, datecol='date', end_t
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df.rename(columns={datecol: 'ds'}, inplace=True)
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# 指定列统一减少数值
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df[config.offsite_col] = df[config.offsite_col]-config.offsite
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print(global_config.keys())
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df[global_config['offsite_col']] = df[global_config['offsite_col']] - \
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global_config['offsite']
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# 预测列为avg_cols的均值
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df[y] = df[config.avg_cols].mean(axis=1)
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df[global_config['y']] = df[global_config['avg_cols']].mean(axis=1)
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# 去掉多余的列avg_cols
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df = df.drop(columns=config.avg_cols)
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df = df.drop(columns=global_config['avg_cols'])
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# 重命名预测列
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df.rename(columns={y: 'y'}, inplace=True)
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df.rename(columns={global_config['y']: 'y'}, inplace=True)
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# 按时间顺序排列
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df.sort_values(by='ds', inplace=True)
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df['ds'] = pd.to_datetime(df['ds'])
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@ -3,7 +3,7 @@
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from lib.dataread import *
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from config_juxiting 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 ex_Model, model_losss_juxiting, tansuanli_export_pdf, pp_export_pdf
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import datetime
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import torch
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torch.set_float32_matmul_precision("high")
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@ -13,6 +13,10 @@ global_config.update({
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'logger': logger,
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'dataset': dataset,
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'y': y,
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'offsite_col': offsite_col,
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'avg_cols': avg_cols,
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'offsite': offsite,
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'edbcodenamedict': edbcodenamedict,
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'is_debug': is_debug,
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'is_train': is_train,
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'is_fivemodels': is_fivemodels,
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@ -150,13 +154,13 @@ def predict_main():
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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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df = datachuli_juxiting(df_zhibiaoshuju, df_zhibiaoliebiao, y=global_config['y'], dataset=dataset, add_kdj=add_kdj, is_timefurture=is_timefurture,
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end_time=end_time)
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else:
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# 读取数据
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logger.info('读取本地数据：' + os.path.join(dataset, data_set))
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df, df_zhibiaoliebiao = getdata(filename=os.path.join(dataset, data_set), y=y, dataset=dataset, add_kdj=add_kdj,
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df, df_zhibiaoliebiao = getdata_juxiting(filename=os.path.join(dataset, data_set), y=y, dataset=dataset, add_kdj=add_kdj,
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is_timefurture=is_timefurture, end_time=end_time) # 原始数据，未处理
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# 更改预测列名称
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@ -335,19 +339,19 @@ def predict_main():
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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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model_results3 = model_losss(sqlitedb, end_time=end_time)
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model_results3 = model_losss_juxiting(sqlitedb, end_time=end_time)
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logger.info('训练数据绘图end')
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# # 模型报告
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# logger.info('制作报告ing')
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# title = f'{settings}--{end_time}-预测报告' # 报告标题
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# reportname = f'Brent原油大模型月度预测--{end_time}.pdf' # 报告文件名
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# reportname = reportname.replace(':', '-') # 替换冒号
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# brent_export_pdf(dataset=dataset, num_models=5 if is_fivemodels else 22, time=end_time,
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# reportname=reportname, sqlitedb=sqlitedb),
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logger.info('制作报告ing')
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title = f'{settings}--{end_time}-预测报告' # 报告标题
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reportname = f'Brent原油大模型月度预测--{end_time}.pdf' # 报告文件名
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reportname = reportname.replace(':', '-') # 替换冒号
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pp_export_pdf(dataset=dataset, num_models=5 if is_fivemodels else 22, time=end_time,
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reportname=reportname, sqlitedb=sqlitedb),
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# logger.info('制作报告end')
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# logger.info('模型训练完成')
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logger.info('制作报告end')
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logger.info('模型训练完成')
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# # 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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