From c49929019614b2b78ef2296c7c7761d53fe664bb Mon Sep 17 00:00:00 2001
From: liurui <liurui_test@163.com>
Date: Tue, 5 Nov 2024 15:38:14 +0800
Subject: [PATCH] =?UTF-8?q?=E8=A7=A3=E5=86=B3sqlite=E5=AD=98=E5=82=A8?=
 =?UTF-8?q?=E6=97=A5=E6=9C=9F=E6=A0=BC=E5=BC=8F?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 config_jingbo.py        |   8 ++++----
 dataset/jbsh_yuanyou.db | Bin 65536 -> 65536 bytes
 main.py                 |  39 ++++++++++++++++++++-------------------
 3 files changed, 24 insertions(+), 23 deletions(-)

diff --git a/config_jingbo.py b/config_jingbo.py
index db3255d..38ba7fd 100644
--- a/config_jingbo.py
+++ b/config_jingbo.py
@@ -167,12 +167,12 @@ upload_data = {
 
 ### 开关
 is_train = True # 是否训练
-is_debug = True # 是否调试
+is_debug = False # 是否调试
 is_eta = False # 是否使用eta接口
 is_timefurture = True # 是否使用时间特征
-is_fivemodels = True # 是否使用之前保存的最佳的5个模型
-is_edbcode = True # 特征使用edbcoding列表中的
-is_edbnamelist = True # 自定义特征，对应上面的edbnamelist
+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
+is_edbnamelist = False # 自定义特征，对应上面的edbnamelist
 is_update_eta  = False  # 预测结果上传到eta
 is_update_report = False # 是否上传报告
 
diff --git a/dataset/jbsh_yuanyou.db b/dataset/jbsh_yuanyou.db
index 0ee3db30e52c10836f4a14c1f0e2c8824837083f..d5cfb0d5c78afd8cbf3fba422dfbc98cec8dd5fc 100644
GIT binary patch
delta 72
zcmZo@U}<PznIO%WHBrWyF>7PO5`JD*UIqrfyZoAb%)BPNn*{~l^Dr5)PQEWED{81~
ZV5(qXU<F1FK?h&A%Iyf+Y$AK+5dg5?6ukfd

delta 42
ycmZo@U}<PznIO%WIZ?)$F>_<W5`G>QUK0ksyZoAb%)BO>1r=0yH=D?wc?1Cb84Jk(

diff --git a/main.py b/main.py
index b9c32cc..d11f3ce 100644
--- a/main.py
+++ b/main.py
@@ -65,6 +65,7 @@ def predict_main():
     else:
         for row in first_row.itertuples(index=False):
             row_dict = row._asdict()
+            row_dict['ds'] = row_dict['ds'].strftime('%Y-%m-%d %H:%M:%S')
             check_query = sqlitedb.select_data('trueandpredict',where_condition = f"ds = '{row.ds}'")
             if len(check_query) > 0:
                 set_clause = ", ".join([f"{key} = '{value}'" for key, value in row_dict.items()])
@@ -117,25 +118,25 @@ def predict_main():
     row,col = df.shape
     
     now = datetime.datetime.now().strftime('%Y%m%d%H%M%S')
-    # ex_Model(df,
-    #         horizon=horizon,
-    #         input_size=input_size,
-    #         train_steps=train_steps,
-    #         val_check_steps=val_check_steps,
-    #         early_stop_patience_steps=early_stop_patience_steps,
-    #         is_debug=is_debug,
-    #         dataset=dataset,
-    #         is_train=is_train,
-    #         is_fivemodels=is_fivemodels,
-    #         val_size=val_size,
-    #         test_size=test_size,
-    #         settings=settings,
-    #         now=now,
-    #         etadata = etadata,
-    #         modelsindex = modelsindex,
-    #         data = data,
-    #         is_eta=is_eta,
-    #         )
+    ex_Model(df,
+            horizon=horizon,
+            input_size=input_size,
+            train_steps=train_steps,
+            val_check_steps=val_check_steps,
+            early_stop_patience_steps=early_stop_patience_steps,
+            is_debug=is_debug,
+            dataset=dataset,
+            is_train=is_train,
+            is_fivemodels=is_fivemodels,
+            val_size=val_size,
+            test_size=test_size,
+            settings=settings,
+            now=now,
+            etadata = etadata,
+            modelsindex = modelsindex,
+            data = data,
+            is_eta=is_eta,
+            )
 
     # # 模型评估
     model_results3 = model_losss(sqlitedb)