添加周度准确率到数据库

config_jingbo.py

@@ -243,7 +243,7 @@ print("数据库连接成功",host,dbname,dbusername)
 
 # 数据截取日期
 start_year = 2018 # 数据开始年份
-end_time = '2024-11-29' # 数据截取日期
+end_time = '2024-12-04' # 数据截取日期
 freq = 'B'  # 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
 delweekenday = True if freq == 'B' else False # 是否删除周末数据
 is_corr = False # 特征是否参与滞后领先提升相关系数

main_yuanyou.py

@@ -9,7 +9,7 @@ torch.set_float32_matmul_precision("high")
 
 
 
-def predict_main():
+def predict_main(end_time):
     """
     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。
 
@@ -202,7 +202,7 @@ def predict_main():
     logger.info('模型训练完成')
  
     logger.info('训练数据绘图ing')
-    model_results3 = model_losss(sqlitedb)
+    model_results3 = model_losss(sqlitedb,end_time=end_time)
     logger.info('训练数据绘图end')
     
     # 模型报告
@@ -238,4 +238,8 @@ def predict_main():
 
 
 if __name__ == '__main__':
-    predict_main()
+    # 遍历2024-11-25 到 2024-12-3 之间的工作日日期
+    for i_time in pd.date_range('2024-11-22', '2024-12-17', freq='B'):
+        end_time = i_time.strftime('%Y-%m-%d')
+        # print(e_time)
+        predict_main(end_time)

models/nerulforcastmodels.py

@@ -222,7 +222,7 @@ def ex_Model(df,horizon,input_size,train_steps,val_check_steps,early_stop_patien
 
 
 # 原油计算预测评估指数
-def model_losss(sqlitedb):
+def model_losss(sqlitedb,end_time):
     global dataset
     global rote
     most_model = [sqlitedb.select_data('most_model',columns=['most_common_model'],order_by='ds desc',limit=1).values[0][0]]
@@ -357,22 +357,21 @@ def model_losss(sqlitedb):
 
     def find_closest_values(row):
         x = row.y
-        if x is None:
+        if x is None or np.isnan(x):
-            return pd.Series([None, None], index=['min_within_quantile','max_within_quantile'])
+            return pd.Series([None, None], index=['min_price','max_price'])
-        row = row.drop('ds')
+        # row = row.drop('ds')
-        row = row[:,0].values.tolist()
+        row = row.values.tolist()
-        row = row.sort()
+        row.sort()
         print(row)
         # x 在row中的索引
         index = row.index(x)
         if index == 0:
-            return pd.Series([row[index+1], row[index+2]], index=['min_within_quantile','max_within_quantile'])
+            return pd.Series([row[index+1], row[index+2]], index=['min_price','max_price'])
         elif index == len(row)-1:
-            return pd.Series([row[index-2], row[index-1]], index=['min_within_quantile','max_within_quantile'])
+            return pd.Series([row[index-2], row[index-1]], index=['min_price','max_price'])
         else:
-            return pd.Series([row[index-1], row[index+1]], index=['min_within_quantile','max_within_quantile'])
+            return pd.Series([row[index-1], row[index+1]], index=['min_price','max_price'])
 
-    # df_combined3[['min_within_quantile','max_within_quantile']] = df_combined3.apply(find_closest_values, axis=1)
 
     
     def find_most_common_model():
@@ -406,14 +405,17 @@ def model_losss(sqlitedb):
         df_predict2['id'] = range(max_id + 1, max_id + 1 + len(df_predict2))
     else:
         df_predict2['id'] = range(1, 1 + len(df_predict2))
+    # df_predict2['CREAT_DATE'] = now if end_time == '' else end_time
+    df_predict2['CREAT_DATE'] = end_time
     
-    df_predict2['CREAT_DATE'] = now if end_time == '' else end_time
     # df_predict2['PREDICT_DATE'] = df_predict2['ds']
     # df_predict2['MIN_PRICE'] = df_predict2['min_within_quantile']
     # df_predict2['MAX_PRICE'] = df_predict2['max_within_quantile']
     # df_predict2 = df_predict2[['id','PREDICT_DATE','CREAT_DATE','MIN_PRICE','MAX_PRICE']]
+
     df_predict2.to_sql("accuracy",  con=sqlitedb.connection, if_exists='append', index=False)
     
+    # 更新accuracy表中的y值
     update_y = sqlitedb.select_data(table_name = "accuracy",where_condition='y is null')
     if len(update_y) > 0:
         df_combined4 = df_combined3[(df_combined3['ds'].isin(update_y['ds'])) & (df_combined3['y'].notnull())]
@@ -423,7 +425,86 @@ def model_losss(sqlitedb):
                     sqlitedb.update_data('accuracy',f"y = {row['y']}",f"ds = '{row['ds']}'")
                 except:
                     print(row)
-            print(df_combined4)
+    # 准确率计算
+    predict_y = sqlitedb.select_data(table_name = "accuracy") 
+    # 找到min_price为空的行的id
+    ids = predict_y[predict_y['min_price'].isnull()]['id'].tolist()
+    # 计算min_price和max_price
+    # predict_y[['min_price','max_price']] = predict_y[['y']+allmodelnames].apply(find_closest_values, axis=1)
+    predict_y[['min_price','max_price']] = predict_y[['min_within_quantile','max_within_quantile']]
+    # min_price为空的行更新数据到数据库中
+    for id in ids:
+        row = predict_y[predict_y['id'] == id]
+        print(row)
+        try:
+            sqlitedb.update_data('accuracy',f"min_price = {row['min_price'].values[0]},max_price = {row['max_price'].values[0]}",f"id = {id}")
+        except:
+            print(row)
+
+    # 最高最低价
+    xlsfilename = os.path.join(dataset,'数据项下载.xls')
+    df2 = pd.read_excel(xlsfilename)[5:]
+    df2 = df2.rename(columns = {'数据项名称':'ds','布伦特最低价':'LOW_PRICE','布伦特最高价':'HIGH_PRICE'})
+    print(df2.shape)
+    df = pd.merge(predict_y,df2,on=['ds'],how='left')
+    df['ds'] =  pd.to_datetime(df['ds'])
+    # df['PREDICT_DATE'] =  pd.to_datetime(df['PREDICT_DATE'])
+    df = df.reindex()
+    print(df.shape)
+
+    df.to_csv(os.path.join(dataset,'123.csv'))
+
+
+    def calculate_accuracy(row):
+        if row['HIGH_PRICE'] > row['min_price'] or row['max_price'] > row['LOW_PRICE']:
+            sorted_prices = sorted([row['LOW_PRICE'], row['min_price'], row['max_price'], row['HIGH_PRICE']])
+            middle_diff = sorted_prices[2] - sorted_prices[1]
+            price_range = row['HIGH_PRICE'] - row['LOW_PRICE']
+            accuracy = middle_diff / price_range
+            return accuracy
+        else:
+            return 0
+
+    # 使用 apply 函数来应用计算准确率的函数
+    columns = ['HIGH_PRICE','LOW_PRICE','min_price','max_price']
+    df[columns] = df[columns].astype(float)
+    df['ACCURACY'] = df.apply(calculate_accuracy, axis=1)
+
+
+    # 取结束日期上一周的日期
+    endtime = end_time
+    endtimeweek = datetime.datetime.strptime(endtime, '%Y-%m-%d')
+    up_week = endtimeweek - datetime.timedelta(days=endtimeweek.weekday() + 14)
+    up_week_dates = [up_week + datetime.timedelta(days=i) for i in range(14)][4:-2]
+    up_week_dates = [date.strftime('%Y-%m-%d') for date in up_week_dates]
+    print(up_week_dates)
+
+
+    df3 = df.copy()
+    df3 = df3[df3['CREAT_DATE'].isin(up_week_dates)]
+    df3 = df3[df3['ds'].isin(up_week_dates)]
+    print(df3.shape)
+    df3.to_csv('up_week_dates.csv',index=False)
+
+    total = len(df3)
+    accuracy_rote = 0
+    # for i,group in df3.groupby('CREAT_DATE'):
+    for i,group in df3.groupby('ds'):
+        print(i)
+        print('权重：',round(len(group)/total,2))
+        print('准确率：',group['ACCURACY'].sum()/(len(group)/total))
+        accuracy_rote += group['ACCURACY'].sum()/(len(group)/total)
+
+    print(accuracy_rote)
+    # 列  开始日期，结束日期，准确率
+    df4 = pd.DataFrame(columns=['开始日期','结束日期','准确率'])
+    # 追加一行数据
+    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}
+    print(df4)
+    df4.to_sql("accuracy_rote",  con=sqlitedb.connection, if_exists='append', index=False)
+
+
+
 
 
     def _add_abs_error_rate():

up_week_dates.csv

@@ -1,16 +1 @@
-ds,ACCURACY,PREDICT_DATE,CREAT_DATE,HIGH_PRICE_y,LOW_PRICE_y,MIN_PRICE,MAX_PRICE,Ds_Week,Pre_Week
+ds,NHITS,Informer,LSTM,iTransformer,TSMixer,TSMixerx,PatchTST,RNN,GRU,TCN,BiTCN,DilatedRNN,MLP,DLinear,NLinear,TFT,StemGNN,MLPMultivariate,TiDE,DeepNPTS,y,min_within_quantile,max_within_quantile,min_price,max_price,id,CREAT_DATE,序号,LOW_PRICE,HIGH_PRICE,ACCURACY
-2024-11-26,1.0,2024-11-26,2024-11-25,73.8,71.63,71.071556,76.0069,47,47
-2024-11-27,1.0,2024-11-27,2024-11-25,72.85,71.71,71.003624,75.58056,47,47
-2024-11-28,0.7893243243243208,2024-11-28,2024-11-25,72.96,71.85,72.08385,76.20426,47,47
-2024-11-29,1.0,2024-11-29,2024-11-25,73.34,71.75,71.32973,75.70395,47,47
-2024-11-27,0.6068728070175414,2024-11-27,2024-11-26,72.85,71.71,72.158165,76.17365,47,47
-2024-11-28,0.8021441441441385,2024-11-28,2024-11-26,72.96,71.85,72.06962,76.447,47,47
-2024-11-29,0.6082389937106918,2024-11-29,2024-11-26,73.34,71.75,72.3729,76.08291,47,47
-2024-11-28,1.0,2024-11-28,2024-11-27,72.96,71.85,70.70975,75.20803,47,47
-2024-11-29,1.0,2024-11-29,2024-11-27,73.34,71.75,69.92311,75.423775,47,47
-2024-11-29,1.0,2024-11-29,2024-11-28,73.34,71.75,70.43276,75.48062,47,47
-2024-11-25,0.11832806324110515,2024-11-25,2024-11-22,74.83,72.3,74.53063,76.67314,47,47
-2024-11-26,0.0,2024-11-26,2024-11-22,73.8,71.63,74.44043,76.874565,47,47
-2024-11-27,0.0,2024-11-27,2024-11-22,72.85,71.71,74.66318,76.73413,47,47
-2024-11-28,0.0,2024-11-28,2024-11-22,72.96,71.85,74.70841,77.14105,47,47
-2024-11-29,0.0,2024-11-29,2024-11-22,73.34,71.75,74.70321,77.74617,47,47