聚烯烃周度预测调试完成
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jingboyitiji
parent
3b0011ceeb
commit
95da926b3a
@ -202,15 +202,15 @@ 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_debug = False # 是否调试
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is_eta = False # 是否使用eta接口
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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_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 = True # 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_eta = False # 预测结果上传到eta
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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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@ -224,9 +224,9 @@ print("数据库连接成功",host,dbname,dbusername)
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# 数据截取日期
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start_year = 2020 # 数据开始年份
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end_time = '2025-01-27' # 数据截取日期
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freq = 'W' # 时间频率，"D": 天 "W": 周"M": 月"Q": 季度"A": 年 "H": 小时 "T": 分钟 "S": 秒 "B": 工作日
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start_year = 2015 # 数据开始年份
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end_time = '' # 数据截取日期
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freq = 'WW' # 时间频率，"D": 天 "W": 周"M": 月"Q": 季度"A": 年 "H": 小时 "T": 分钟 "S": 秒 "B": 工作日
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delweekenday = True if freq == 'B' else False # 是否删除周末数据
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is_corr = False # 特征是否参与滞后领先提升相关系数
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add_kdj = False # 是否添加kdj指标
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@ -243,8 +243,8 @@ avg_cols = [
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]
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offsite = 80
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offsite_col = ['PP：拉丝：HP550J：市场价：青岛：金能化学（日）']
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horizon =1 # 预测的步长
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input_size = 7 # 输入序列长度
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horizon =2 # 预测的步长
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input_size = 14 # 输入序列长度
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train_steps = 50 if is_debug else 1000 # 训练步数,用来限定epoch次数
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val_check_steps = 30 # 评估频率
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early_stop_patience_steps = 5 # 早停的耐心步数
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@ -263,10 +263,10 @@ weight_dict = [0.4,0.15,0.1,0.1,0.25] # 权重
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### 文件
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data_set = 'PP指标数据.xlsx' # 数据集文件
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dataset = 'juxitingzhududataset' # 数据集文件夹
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dataset = 'juxitingzhoududataset' # 数据集文件夹
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# 数据库名称
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db_name = os.path.join(dataset,'jbsh_juxiting.db')
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db_name = os.path.join(dataset,'jbsh_juxiting_zhoudu.db')
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sqlitedb = SQLiteHandler(db_name)
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sqlitedb.connect()
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@ -1063,6 +1063,22 @@ def getdata_juxiting(filename, datecol='date', y='y', dataset='', add_kdj=False,
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return df, df_zhibiaoliebiao
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def getdata_zhoudu_juxiting(filename, datecol='date', y='y', dataset='', add_kdj=False, is_timefurture=False, end_time=''):
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config.logger.info('getdata接收：'+filename+' '+datecol+' '+end_time)
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# 判断后缀名 csv或excel
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if filename.endswith('.csv'):
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df = loadcsv(filename)
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else:
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# 读取excel 指标数据
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df_zhibiaoshuju = pd.read_excel(filename, sheet_name='指标数据')
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df_zhibiaoliebiao = pd.read_excel(filename, sheet_name='指标列表')
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# 日期字符串转为datatime
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df = zhoududatachuli(df_zhibiaoshuju, df_zhibiaoliebiao, datecol, y=y, dataset=dataset,
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add_kdj=add_kdj, is_timefurture=is_timefurture, end_time=end_time)
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return df, df_zhibiaoliebiao
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def sanitize_filename(filename):
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# 使用正则表达式替换不合规的字符
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@ -1,12 +1,80 @@
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# 读取配置
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from lib.dataread import *
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from lib.tools import SendMail,exception_logger
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from models.nerulforcastmodels import ex_Model_Juxiting,model_losss,model_losss_juxiting,brent_export_pdf,tansuanli_export_pdf,pp_export_pdf,model_losss_juxiting
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import glob
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from lib.dataread import *
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from config_juxiting_zhoudu import *
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from lib.tools import SendMail, exception_logger
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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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global_config.update({
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# 核心参数
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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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'settings': settings,
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# 模型参数
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'data_set': data_set,
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'input_size': input_size,
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'horizon': horizon,
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'train_steps': train_steps,
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'val_check_steps': val_check_steps,
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'val_size': val_size,
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'test_size': test_size,
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'modelsindex': modelsindex,
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'rote': rote,
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# 特征工程开关
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'is_del_corr': is_del_corr,
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'is_del_tow_month': is_del_tow_month,
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'is_eta': is_eta,
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'is_update_eta': is_update_eta,
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'is_fivemodels': is_fivemodels,
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'early_stop_patience_steps': early_stop_patience_steps,
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# 时间参数
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'start_year': start_year,
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'end_time': end_time or datetime.datetime.now().strftime("%Y-%m-%d"),
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'freq': freq, # 保持列表结构
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# 接口配置
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'login_pushreport_url': login_pushreport_url,
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'login_data': login_data,
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'upload_url': upload_url,
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'upload_warning_url': upload_warning_url,
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'warning_data': warning_data,
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# 查询接口
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'query_data_list_item_nos_url': query_data_list_item_nos_url,
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'query_data_list_item_nos_data': query_data_list_item_nos_data,
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# eta 配置
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'APPID': APPID,
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'SECRET': SECRET,
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'etadata': data,
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'edbcodelist': edbcodelist,
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'ClassifyId': ClassifyId,
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'edbcodedataurl': edbcodedataurl,
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'classifyidlisturl': classifyidlisturl,
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'edbdatapushurl': edbdatapushurl,
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'edbdeleteurl': edbdeleteurl,
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'edbbusinessurl': edbbusinessurl,
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'ClassifyId': ClassifyId,
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'classifylisturl': classifylisturl,
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# 数据库配置
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'sqlitedb': sqlitedb,
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})
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def predict_main():
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@ -48,31 +116,23 @@ def predict_main():
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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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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=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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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_pp_data(data_set=data_set, dataset=dataset) # 原始数据，未处理
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df_zhibiaoshuju, df_zhibiaoliebiao = etadata.get_eta_api_pp_data(
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data_set=data_set, dataset=dataset) # 原始数据，未处理
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if is_market:
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logger.info('从市场信息平台获取数据...')
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@ -83,26 +143,26 @@ def predict_main():
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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(end_time,df_zhibiaoshuju)
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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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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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with pd.ExcelWriter(os.path.join(dataset, data_set)) as file:
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df_zhibiaoshuju.to_excel(file, sheet_name='指标数据', index=False)
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df_zhibiaoliebiao.to_excel(file, sheet_name='指标列表', index=False)
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# 数据处理
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df = datachuli_juxiting(df_zhibiaoshuju, df_zhibiaoliebiao, y=y, dataset=dataset, add_kdj=add_kdj, is_timefurture=is_timefurture,
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end_time=end_time)
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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_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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df, df_zhibiaoliebiao = getdata_zhoudu_juxiting(filename=os.path.join(dataset, data_set), y=y, dataset=dataset, add_kdj=add_kdj,
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is_timefurture=is_timefurture, end_time=end_time) # 原始数据，未处理
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# 更改预测列名称
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df.rename(columns={y: 'y'}, inplace=True)
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@ -124,47 +184,65 @@ def predict_main():
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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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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([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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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(row_dict.values()), columns=row_dict.keys())
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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('accuracy',where_condition="y is null")
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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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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('accuracy', f"y = {yy},LOW_PRICE = {LOW},HIGH_PRICE = {HIGH}", where_condition=f"ds = '{row_dict['ds']}'")
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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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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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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(thresh=len(model_results)*0.1,axis=1)
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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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@ -172,47 +250,59 @@ def predict_main():
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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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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(lambda row: row[[f'{model}_abs_error_rate' for model in modelnames]].min(), axis=1)
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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(lambda row: row[[f'{model}_abs_error_rate' for model in modelnames]].idxmin(), axis=1)
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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(lambda x: x.split('_')[0])
|
||||
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',))
|
||||
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:
|
||||
# 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']]
|
||||
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'})
|
||||
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'
|
||||
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))
|
||||
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)
|
||||
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:
|
||||
@ -226,41 +316,40 @@ def predict_main():
|
||||
row, col = df.shape
|
||||
|
||||
now = datetime.datetime.now().strftime('%Y%m%d%H%M%S')
|
||||
ex_Model_Juxiting(df,
|
||||
horizon=horizon,
|
||||
input_size=input_size,
|
||||
train_steps=train_steps,
|
||||
val_check_steps=val_check_steps,
|
||||
early_stop_patience_steps=early_stop_patience_steps,
|
||||
is_debug=is_debug,
|
||||
dataset=dataset,
|
||||
is_train=is_train,
|
||||
is_fivemodels=is_fivemodels,
|
||||
val_size=val_size,
|
||||
test_size=test_size,
|
||||
settings=settings,
|
||||
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=modelsindex,
|
||||
etadata=global_config['etadata'],
|
||||
modelsindex=global_config['modelsindex'],
|
||||
data=data,
|
||||
is_eta=is_eta,
|
||||
end_time=end_time,
|
||||
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_juxiting(sqlitedb)
|
||||
model_results3 = model_losss_juxiting(sqlitedb, end_time=global_config['end_time'],is_fivemodels=global_config['is_fivemodels'])
|
||||
logger.info('训练数据绘图end')
|
||||
|
||||
# 模型报告
|
||||
# # 模型报告
|
||||
logger.info('制作报告ing')
|
||||
title = f'{settings}--{end_time}-预测报告' # 报告标题
|
||||
reportname = f'PP大模型预测报告--{end_time}.pdf' # 报告文件名
|
||||
reportname = reportname.replace(':', '-') # 替换冒号
|
||||
pp_export_pdf(dataset=dataset,num_models = 5 if is_fivemodels else 22,time=end_time,
|
||||
reportname=reportname,sqlitedb=sqlitedb),
|
||||
title = f'{settings}--{end_time}-预测报告' # 报告标题
|
||||
reportname = f'Brent原油大模型周度预测--{end_time}.pdf' # 报告文件名
|
||||
reportname = reportname.replace(':', '-') # 替换冒号
|
||||
pp_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')
|
||||
@ -275,27 +364,27 @@ def predict_main():
|
||||
# # ex_GRU(df)
|
||||
|
||||
# 发送邮件
|
||||
m = SendMail(
|
||||
username=username,
|
||||
passwd=passwd,
|
||||
recv=recv,
|
||||
title=title,
|
||||
content=content,
|
||||
file=max(glob.glob(os.path.join(dataset,'*.pdf')), key=os.path.getctime),
|
||||
ssl=ssl,
|
||||
)
|
||||
# m = SendMail(
|
||||
# username=username,
|
||||
# passwd=passwd,
|
||||
# recv=recv,
|
||||
# title=title,
|
||||
# content=content,
|
||||
# file=max(glob.glob(os.path.join(dataset,'*.pdf')), key=os.path.getctime),
|
||||
# ssl=ssl,
|
||||
# )
|
||||
# m.send_mail()
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
# global end_time
|
||||
# is_on = True
|
||||
# # 遍历2024-11-25 到 2024-12-3 之间的工作日日期
|
||||
# for i_time in pd.date_range('2025-1-20', '2025-2-6', freq='B'):
|
||||
# end_time = i_time.strftime('%Y-%m-%d')
|
||||
# 遍历2024-11-25 到 2024-12-3 之间的工作日日期
|
||||
# for i_time in pd.date_range('2022-1-1', '2025-3-26', freq='M'):
|
||||
# try:
|
||||
# global_config['end_time'] = i_time.strftime('%Y-%m-%d')
|
||||
# predict_main()
|
||||
# except:
|
||||
# pass
|
||||
# except Exception as e:
|
||||
# logger.info(f'预测失败：{e}')
|
||||
# continue
|
||||
|
||||
predict_main()
|
||||
Loading…
Reference in New Issue
Block a user