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预测结果保存到数据库

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liurui 2024-12-13 18:25:23 +08:00
parent 1f827d8224
commit 24174fa9a0
3 changed files with 41 additions and 36 deletions
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4
config_jingbo.py
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@ -223,7 +223,7 @@ table_name = 'v_tbl_crude_oil_warning'
### 开关 ### 开关
is_train = True # 是否训练 is_train = False # 是否训练
is_debug = False # 是否调试 is_debug = False # 是否调试
is_eta = False # 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_timefurture = True # 是否使用时间特征
@ -243,7 +243,7 @@ print("数据库连接成功",host,dbname,dbusername)
# 数据截取日期 # 数据截取日期
start_year = 2018 # 数据开始年份 start_year = 2018 # 数据开始年份
end_time = '' # 数据截取日期 end_time = '2024-11-29' # 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 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 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 is_corr = False # 特征是否参与滞后领先提升相关系数

40
main_yuanyou.py
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@ -178,25 +178,25 @@ def predict_main():
row, col = df.shape row, col = df.shape
now = datetime.datetime.now().strftime('%Y%m%d%H%M%S') now = datetime.datetime.now().strftime('%Y%m%d%H%M%S')
# ex_Model(df, ex_Model(df,
# horizon=horizon, horizon=horizon,
# input_size=input_size, input_size=input_size,
# train_steps=train_steps, train_steps=train_steps,
# val_check_steps=val_check_steps, val_check_steps=val_check_steps,
# early_stop_patience_steps=early_stop_patience_steps, early_stop_patience_steps=early_stop_patience_steps,
# is_debug=is_debug, is_debug=is_debug,
# dataset=dataset, dataset=dataset,
# is_train=is_train, is_train=is_train,
# is_fivemodels=is_fivemodels, is_fivemodels=is_fivemodels,
# val_size=val_size, val_size=val_size,
# test_size=test_size, test_size=test_size,
# settings=settings, settings=settings,
# now=now, now=now,
# etadata=etadata, etadata=etadata,
# modelsindex=modelsindex, modelsindex=modelsindex,
# data=data, data=data,
# is_eta=is_eta, is_eta=is_eta,
# ) )
logger.info('模型训练完成') logger.info('模型训练完成')
@ -234,7 +234,7 @@ def predict_main():
file=max(glob.glob(os.path.join(dataset,'*.pdf')), key=os.path.getctime), file=max(glob.glob(os.path.join(dataset,'*.pdf')), key=os.path.getctime),
ssl=ssl, ssl=ssl,
) )
m.send_mail() # m.send_mail()
if __name__ == '__main__': if __name__ == '__main__':

33
models/nerulforcastmodels.py
View File

@ -179,17 +179,17 @@ def ex_Model(df,horizon,input_size,train_steps,val_check_steps,early_stop_patien
filename = f'{settings}--{now}.joblib' filename = f'{settings}--{now}.joblib'
#文件名去掉冒号 #文件名去掉冒号
filename = filename.replace(':', '-') # 替换冒号 filename = filename.replace(':', '-') # 替换冒号
# dump(nf, os.path.join(dataset,filename)) dump(nf, os.path.join(dataset,filename))
else: else:
# glob获取dataset下最新的joblib文件 # glob获取dataset下最新的joblib文件
import glob import glob
filename = max(glob.glob(os.path.join(dataset,'*.joblib')), key=os.path.getctime) filename = max(glob.glob(os.path.join(dataset,'*.joblib')), key=os.path.getctime)
# logger.info('读取模型：'+ filename) logger.info('读取模型：'+ filename)
nf = load(filename) nf = load(filename)
# # 测试集预测 # # # 测试集预测
nf_test_preds = nf.cross_validation(df=df_test, val_size=val_size, test_size=test_size, n_windows=None) # nf_test_preds = nf.cross_validation(df=df_test, val_size=val_size, test_size=test_size, n_windows=None)
# 测试集预测结果保存 # # 测试集预测结果保存
nf_test_preds.to_csv(os.path.join(dataset,"cross_validation.csv"),index=False) # nf_test_preds.to_csv(os.path.join(dataset,"cross_validation.csv"),index=False)
df_test['ds'] = pd.to_datetime(df_test['ds'], errors='coerce') df_test['ds'] = pd.to_datetime(df_test['ds'], errors='coerce')
@ -217,7 +217,8 @@ def ex_Model(df,horizon,input_size,train_steps,val_check_steps,early_stop_patien
etadata.push_data(data) etadata.push_data(data)
return nf_test_preds # return nf_test_preds
return
# 原油计算预测评估指数 # 原油计算预测评估指数
@ -399,12 +400,7 @@ def model_losss(sqlitedb):
columns = ','.join(df_combined3.columns.to_list()+['id','CREAT_DATE']) columns = ','.join(df_combined3.columns.to_list()+['id','CREAT_DATE'])
sqlitedb.create_table('accuracy',columns=columns) sqlitedb.create_table('accuracy',columns=columns)
existing_data = sqlitedb.select_data(table_name = "accuracy") existing_data = sqlitedb.select_data(table_name = "accuracy")
update_y = sqlitedb.select_data(table_name = "accuracy",where_condition='y is null')
df_combined4 = df_combined3[(df_combined3['ds'].isin(update_y['ds'])) & (df_combined3['y'].notnull())]
if len(df_combined4) > 0:
for index, row in df_combined4.iterrows():
sqlitedb.update_data('accuracy',f"y = {row['y']}",f"ds = '{row['ds']}'")
print(df_combined4)
if not existing_data.empty: if not existing_data.empty:
max_id = existing_data['id'].astype(int).max() max_id = existing_data['id'].astype(int).max()
df_predict2['id'] = range(max_id + 1, max_id + 1 + len(df_predict2)) df_predict2['id'] = range(max_id + 1, max_id + 1 + len(df_predict2))
@ -418,7 +414,16 @@ def model_losss(sqlitedb):
# df_predict2 = df_predict2[['id','PREDICT_DATE','CREAT_DATE','MIN_PRICE','MAX_PRICE']] # 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) df_predict2.to_sql("accuracy", con=sqlitedb.connection, if_exists='append', index=False)
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())]
if len(df_combined4) > 0:
for index, row in df_combined4.iterrows():
try:
sqlitedb.update_data('accuracy',f"y = {row['y']}",f"ds = '{row['ds']}'")
except:
print(row)
print(df_combined4)
def _add_abs_error_rate(): def _add_abs_error_rate():