liurui/PriceForecast
Template
1
0
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity

根据预测列判断预警特征并删除预警的特征

Browse Source
This commit is contained in:
workpc 2024-11-18 10:34:40 +08:00
parent e2380f5615
commit da929edbac
4 changed files with 23 additions and 8 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

2
config_jingbo.py
View File

@ -168,7 +168,7 @@ upload_data = {
### 开关
is_train = True # 是否训练
is_debug = False # 是否调试
is_eta = True # 是否使用eta接口
is_eta = False # 是否使用eta接口
is_timefurture = True # 是否使用时间特征
is_fivemodels = False # 是否使用之前保存的最佳的5个模型
is_edbcode = False # 特征使用edbcoding列表中的

BIN
dataset/jbsh_yuanyou.db
View File

Binary file not shown.

22
lib/dataread.py
View File

@ -463,6 +463,8 @@ def datachuli(df_zhibiaoshuju,df_zhibiaoliebiao,datecol='date',end_time='',y='y'
# 保存每列的最后更新时间到文件
last_update_times_df = pd.DataFrame(columns = ['feature', 'last_update_time','is_value','update_period','warning_date'])
# 打印每列的最后更新时间
for column, last_update_time in last_update_times.items():
values = []
@ -476,7 +478,7 @@ def datachuli(df_zhibiaoshuju,df_zhibiaoliebiao,datecol='date',end_time='',y='y'
# 计算预警日期
time_diff = (df1[column].dropna().index.to_series().diff().mode()[0]).total_seconds() / 3600 / 24
from datetime import timedelta
early_warning_date = datetime.datetime.strptime(last_update_time, '%Y-%m-%d') + timedelta(days=time_diff)*2
early_warning_date = datetime.datetime.strptime(last_update_time, '%Y-%m-%d') + timedelta(days=time_diff)*2 + timedelta(days=1)
early_warning_date = early_warning_date.strftime('%Y-%m-%d')
except KeyError:
time_diff = 0
@ -489,15 +491,25 @@ def datachuli(df_zhibiaoshuju,df_zhibiaoliebiao,datecol='date',end_time='',y='y'
last_update_times_df.to_csv(os.path.join(dataset,'last_update_times.csv'), index=False)
logger.info('特征停更信息保存到文件：last_update_times.csv')
logger.info(f'删除预警的特征前数据量：{df.shape}')
y_last_update_time = last_update_times['y']
columns_to_drop = last_update_times_df[last_update_times_df['warning_date'] < y_last_update_time ]['feature'].values.tolist()
df = df.drop(columns = columns_to_drop)
logger.info(f'删除预警的特征后数据量：{df.shape}')
logger.info(f'删除两月不更新特征前数据量：{df.shape}')
# 去掉近最后数据对应的日期在六月以前的列，删除近2月的数据是常熟的列
current_date = datetime.datetime.now()
two_months_ago = current_date - timedelta(days=180)
def check_column(col_name):
'''
判断两月不更新指标
去掉空值列
去掉180天没有更新的列
去掉常数值列
输入：列名
输出：True or False
'''
@ -505,6 +517,7 @@ def datachuli(df_zhibiaoshuju,df_zhibiaoliebiao,datecol='date',end_time='',y='y'
return False
df_check_column = df[['ds',col_name]]
df_check_column = df_check_column.dropna()
if len(df_check_column) == 0:
print(f'空值列：{col_name}')
return True
@ -516,8 +529,13 @@ def datachuli(df_zhibiaoshuju,df_zhibiaoliebiao,datecol='date',end_time='',y='y'
return corresponding_date < two_months_ago
columns_to_drop = df.columns[df.columns.map(check_column)].tolist()
df = df.drop(columns = columns_to_drop)
logger.info(f'删除两月不更新特征后数据量：{df.shape}')
# 删除预测列空值的行
df = df.dropna(subset=['y'])
logger.info(f'删除预测列为空值的行后数据量：{df.shape}')

5
main_yuanyou.py
View File

@ -141,12 +141,9 @@ def predict_main():
logger.info('制作报告ing')
title = f'{settings}--{now}-预测报告' # 报告标题
if 'Brent' in y:
brent_export_pdf(dataset=dataset,num_models = 5 if is_fivemodels else 22,time=end_time,
reportname=reportname,sqlitedb=sqlitedb),
else:
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')