聚烯烃保存数据逻辑更改，更新数据库改为删除数据库，

Untitled.ipynb

@@ -17,10 +17,7 @@
    "id": "07e338e7-1dd1-417f-b4e2-65d0efc983d6",
    "metadata": {},
    "outputs": [],
-   "source": [
-    "df = pd.read_csv(os.path.join(dataset,'last_update_times.csv'))\n",
-    "df = df.sort_values(by=['warning_date'], ascending=[False])"
-   ]
+   "source": []
   },
   {
    "cell_type": "code",

config_juxiting.py

@@ -211,7 +211,7 @@ 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

lib/tools.py

@@ -425,6 +425,11 @@ class SQLiteHandler:
         result = self.execute_query(query).fetchone()
         return result is not None
 
+    def drop_table(self, table_name):
+        query = f"DROP TABLE IF EXISTS {table_name}"
+        self.execute_query(query)
+        self.commit()
+
     def add_column_if_not_exists(self, table_name, column_name, column_type):
         # 查询表结构
         query = f"PRAGMA table_info({table_name})"

main_juxiting.py

@@ -69,7 +69,7 @@ def predict_main():
 
     import datetime
     # 判断当前日期是不是周一
-    is_weekday = datetime.datetime.now().weekday() == 1
+    is_weekday = datetime.datetime.now().weekday() == 0
     if is_weekday:
         logger.info('今天是周一，更新预测模型')
         # 计算最近20天预测残差最低的模型名称

models/nerulforcastmodels.py

@@ -657,23 +657,10 @@ def model_losss_juxiting(sqlitedb):
             pass
     df_combined3.to_csv(os.path.join(dataset,"testandpredict_groupby.csv"),index=False) 
     
-     # 历史价格+预测价格
-        # 将预测结果保存到数据库
-    # 判断表存在
-    if not sqlitedb.check_table_exists('testandpredict_groupby'):
-        df_combined3.to_sql('testandpredict_groupby',sqlitedb.connection,index=False)
-    else:
-        for row in df_combined3.itertuples(index=False):
-            row_dict = row._asdict()
-            print(row_dict)
-            check_query = sqlitedb.select_data('testandpredict_groupby',where_condition = f"ds = '{row.ds}'")
-            if len(check_query) > 0:
-                set_clause = ", ".join([f"{key} = '{value}'" for key, value in row_dict.items()])
-                sqlitedb.update_data('testandpredict_groupby',set_clause,where_condition = f"ds = '{row.ds}'")
-                continue
-            row_dict['ds'] = row.ds.strftime('%Y-%m-%d 00:00:00')
-            sqlitedb.insert_data('testandpredict_groupby',tuple(row_dict.values()),columns=row_dict.keys())
     
+     # 历史价格+预测价格
+    sqlitedb.drop_table('testandpredict_groupby')
+    df_combined3.to_sql('testandpredict_groupby',sqlitedb.connection,index=False)
     
     def _plt_predict_ture(df):
         lens = df.shape[0] if df.shape[0] < 180 else 90