添加异常装饰器
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liurui
parent
34c4a9e205
commit
9de1b4a857
@ -223,7 +223,7 @@ 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 = False # 是否调试
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is_eta = False # 是否使用eta接口
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is_timefurture = True # 是否使用时间特征
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13
lib/tools.py
13
lib/tools.py
@ -512,5 +512,18 @@ class MySQLDB:
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self.connection.close()
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logging.info("Database connection closed.")
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def exception_logger(func):
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def wrapper(*args, **kwargs):
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try:
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return func(*args, **kwargs)
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except Exception as e:
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# 记录异常日志
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logging.error(f"An error occurred in function {func.__name__}: {str(e)}")
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# 可以选择重新抛出异常，或者在这里处理异常
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raise e # 重新抛出异常
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return wrapper
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if __name__ == '__main__':
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print('This is a tool, not a script.')
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@ -1,6 +1,6 @@
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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
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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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import glob
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@ -206,14 +206,14 @@ def predict_main(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}--{now}-预测报告' # 报告标题
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# logger.info('制作报告ing')
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# title = f'{settings}--{now}-预测报告' # 报告标题
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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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# 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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# 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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@ -234,12 +234,12 @@ def predict_main(end_time):
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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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# m.send_mail()
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if __name__ == '__main__':
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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('2024-11-22', '2024-12-16', freq='B'):
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for i_time in pd.date_range('2024-12-02', '2024-12-16', freq='B'):
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end_time = i_time.strftime('%Y-%m-%d')
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# print(e_time)
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predict_main(end_time)
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@ -6,7 +6,7 @@ import seaborn as sns
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import matplotlib.pyplot as plt
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import matplotlib.dates as mdates
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import datetime
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from lib.tools import Graphs,mse,rmse,mae
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from lib.tools import Graphs,mse,rmse,mae,exception_logger
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from lib.dataread import *
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from neuralforecast import NeuralForecast
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from neuralforecast.models import NHITS,Informer, NBEATSx,LSTM,PatchTST, iTransformer, TSMixer
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@ -36,7 +36,7 @@ from reportlab.lib.units import cm # 单位：cm
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pdfmetrics.registerFont(TTFont('SimSun', 'SimSun.ttf'))
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@exception_logger
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def ex_Model(df,horizon,input_size,train_steps,val_check_steps,early_stop_patience_steps,
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is_debug,dataset,is_train,is_fivemodels,val_size,test_size,settings,now,
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etadata,modelsindex,data,is_eta):
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@ -222,6 +222,7 @@ def ex_Model(df,horizon,input_size,train_steps,val_check_steps,early_stop_patien
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# 原油计算预测评估指数
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@exception_logger
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def model_losss(sqlitedb,end_time):
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global dataset
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global rote
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@ -411,6 +412,14 @@ def model_losss(sqlitedb,end_time):
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df_predict2['id'] = range(1, 1 + len(df_predict2))
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# df_predict2['CREAT_DATE'] = now if end_time == '' else end_time
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df_predict2['CREAT_DATE'] = end_time
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def get_common_columns(df1, df2):
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# 获取两个DataFrame的公共列名
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return list(set(df1.columns).intersection(df2.columns))
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common_columns = get_common_columns(df_predict2, existing_data)
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try:
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df_predict2[common_columns].to_sql("accuracy", con=sqlitedb.connection, if_exists='append', index=False)
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except:
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df_predict2.to_sql("accuracy", con=sqlitedb.connection, if_exists='append', index=False)
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# 更新accuracy表中的y值
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@ -425,15 +434,21 @@ def model_losss(sqlitedb,end_time):
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logger.error(f'更新accuracy表中的y值失败，row={row}')
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# 上周准确率计算
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predict_y = sqlitedb.select_data(table_name = "accuracy")
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ids = predict_y[predict_y['min_price'].isnull()]['id'].tolist()
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# 模型评估前五最大最小
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# ids = predict_y[predict_y['min_price'].isnull()]['id'].tolist()
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ids = predict_y['id'].tolist()
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# 准确率基准与绘图上下界逻辑一致
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# predict_y[['min_price','max_price']] = predict_y[['min_within_quantile','max_within_quantile']]
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# 模型评估前五均值 df_combined3['mean'] = df_combined3[modelnames].mean(axis=1)
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# 模型评估前五均值
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# predict_y['min_price'] = predict_y[modelnames].mean(axis=1) -1
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# predict_y['max_price'] = predict_y[modelnames].mean(axis=1) +1
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# 模型评估前十均值
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predict_y['min_price'] = predict_y[allmodelnames[0:5]].min(axis=1)
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predict_y['max_price'] = predict_y[allmodelnames[0:5]].max(axis=1)
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# predict_y['min_price'] = predict_y[allmodelnames[0:10]].mean(axis=1) -1
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# predict_y['max_price'] = predict_y[allmodelnames[0:10]].mean(axis=1) +1
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# 模型评估前十最大最小
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# allmodelnames 和 predict_y 列 重复的
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allmodelnames = [col for col in allmodelnames if col in predict_y.columns]
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predict_y['min_price'] = predict_y[allmodelnames[0:10]].min(axis=1)
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predict_y['max_price'] = predict_y[allmodelnames[0:10]].max(axis=1)
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for id in ids:
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row = predict_y[predict_y['id'] == id]
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try:
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@ -448,25 +463,37 @@ def model_losss(sqlitedb,end_time):
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df = pd.merge(predict_y,df2,on=['ds'],how='left')
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df['ds'] = pd.to_datetime(df['ds'])
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df = df.reindex()
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def calculate_accuracy(row):
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# 子集情况：
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if (row['HIGH_PRICE'] >= row['max_price'] and row['min_price'] >= row['LOW_PRICE']) or \
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(row['LOW_PRICE'] >= row['min_price'] and row['max_price'] >= row['HIGH_PRICE']):
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# 判断预测值在不在布伦特最高最低价范围内，准确率为1，否则为0
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def is_within_range(row):
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for model in allmodelnames:
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if row['LOW_PRICE'] <= row[col] <= row['HIGH_PRICE']:
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return 1
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else:
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return 0
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# 比较真实最高最低，和预测最高最低 计算准确率
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def calculate_accuracy(row):
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# 全子集情况：
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if (row['max_price'] >= row['HIGH_PRICE'] and row['min_price'] <= row['LOW_PRICE']) or \
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(row['max_price'] <= row['HIGH_PRICE'] and row['min_price'] >= row['LOW_PRICE']):
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return 1
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# 无交集情况：
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if row['max_price'] < row['LOW_PRICE'] or \
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row['min_price'] > row['HIGH_PRICE']:
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return 0
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# 有交集情况：
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if row['HIGH_PRICE'] > row['min_price'] or \
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row['max_price'] > row['LOW_PRICE']:
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else:
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sorted_prices = sorted([row['LOW_PRICE'], row['min_price'], row['max_price'], row['HIGH_PRICE']])
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middle_diff = sorted_prices[2] - sorted_prices[1]
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price_range = row['HIGH_PRICE'] - row['LOW_PRICE']
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accuracy = middle_diff / price_range
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return accuracy
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# 无交集情况：
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else:
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return 0
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columns = ['HIGH_PRICE','LOW_PRICE','min_price','max_price']
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df[columns] = df[columns].astype(float)
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df['ACCURACY'] = df.apply(calculate_accuracy, axis=1)
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# df['ACCURACY'] = df.apply(is_within_range, axis=1)
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# 取结束日期上一周的日期
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endtime = end_time
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endtimeweek = datetime.datetime.strptime(endtime, '%Y-%m-%d')
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@ -477,12 +504,15 @@ def model_losss(sqlitedb,end_time):
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df3 = df.copy()
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df3 = df3[df3['CREAT_DATE'].isin(up_week_dates)]
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df3 = df3[df3['ds'].isin(up_week_dates)]
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# df3.to_csv(os.path.join(dataset,f'accuracy_{endtime}.csv'),index=False)
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total = len(df3)
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accuracy_rote = 0
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# 设置权重字典
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weight_dict = [0.4,0.15,0.1,0.1,0.25]
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for i,group in df3.groupby('ds'):
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print('权重：',round(len(group)/total,2))
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print('准确率：',group['ACCURACY'].sum()/(len(group)/total))
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accuracy_rote += group['ACCURACY'].sum()/(len(group)/total)
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print('权重：',weight_dict[len(group)-1])
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print('准确率：',(group['ACCURACY'].sum()/len(group))*weight_dict[len(group)-1])
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accuracy_rote += (group['ACCURACY'].sum()/len(group))*weight_dict[len(group)-1]
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df4 = pd.DataFrame(columns=['开始日期','结束日期','准确率'])
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df4.loc[len(df4)] = {'开始日期':up_week_dates[0],'结束日期':up_week_dates[-1],'准确率':accuracy_rote}
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print(df4)
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@ -598,6 +628,7 @@ def model_losss(sqlitedb,end_time):
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# 聚烯烃计算预测评估指数
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@exception_logger
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def model_losss_juxiting(sqlitedb):
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global dataset
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global rote
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@ -857,7 +888,7 @@ def model_losss_juxiting(sqlitedb):
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import matplotlib.dates as mdates
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@exception_logger
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def brent_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'):
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global y
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# 创建内容对应的空列表
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@ -1138,6 +1169,7 @@ def brent_export_pdf(num_indicators=475,num_models=21, num_dayindicator=202,inpu
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except TimeoutError as e:
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print(f"请求超时: {e}")
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@exception_logger
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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'):
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global y
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# 创建内容对应的空列表
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@ -1749,6 +1781,7 @@ def pp_export_pdf_v1(num_indicators=475,num_models=21, num_dayindicator=202,inpu
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except TimeoutError as e:
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print(f"请求超时: {e}")
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@exception_logger
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def tansuanli_export_pdf(num_indicators=475,num_models=22, num_dayindicator=202,inputsize=5,dataset='dataset',y='电碳价格',end_time='2024-07-30',reportname='tansuanli.pdf'):
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# 创建内容对应的空列表
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content = list()
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