From 3b0011ceebf5054bec001bdd1396f31ae2210ba5 Mon Sep 17 00:00:00 2001
From: jingboyitiji <jingboyitiji@admin.com>
Date: Tue, 11 Mar 2025 11:25:43 +0800
Subject: [PATCH] =?UTF-8?q?=E8=81=9A=E7=83=AF=E7=83=83=E6=97=A5=E5=BA=A6?=
 =?UTF-8?q?=E8=B0=83=E8=AF=95=E9=80=9A=E8=BF=87?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 config_juxiting.py           |   4 +-
 main_juxiting.py             |   5 +-
 models/nerulforcastmodels.py | 315 +++++++++++++++++------------------
 3 files changed, 162 insertions(+), 162 deletions(-)

diff --git a/config_juxiting.py b/config_juxiting.py
index 2977718..26b8bd0 100644
--- a/config_juxiting.py
+++ b/config_juxiting.py
@@ -202,8 +202,8 @@ table_name = 'v_tbl_crude_oil_warning'
 
 # 开关
 is_train = True  # 是否训练
-is_debug = True  # 是否调试
-is_eta = False  # 是否使用eta接口
+is_debug = False  # 是否调试
+is_eta = True  # 是否使用eta接口
 is_market = False  # 是否通过市场信息平台获取特征 ,在is_eta 为true 的情况下生效
 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
diff --git a/main_juxiting.py b/main_juxiting.py
index 1e4054b..9126724 100644
--- a/main_juxiting.py
+++ b/main_juxiting.py
@@ -39,6 +39,7 @@ global_config.update({
     'is_del_tow_month': is_del_tow_month,
     'is_eta': is_eta,
     'is_update_eta': is_update_eta,
+    'is_fivemodels': is_fivemodels,
     'early_stop_patience_steps': early_stop_patience_steps,
 
     # 时间参数
@@ -339,13 +340,13 @@ def predict_main():
     logger.info('模型训练完成')
 
     logger.info('训练数据绘图ing')
-    model_results3 = model_losss_juxiting(sqlitedb, end_time=end_time)
+    model_results3 = model_losss_juxiting(sqlitedb, end_time=global_config['end_time'],is_fivemodels=global_config['is_fivemodels'])
     logger.info('训练数据绘图end')
 
     # # 模型报告
     logger.info('制作报告ing')
     title = f'{settings}--{end_time}-预测报告'  # 报告标题
-    reportname = f'Brent原油大模型月度预测--{end_time}.pdf'  # 报告文件名
+    reportname = f'Brent原油大模型日度预测--{end_time}.pdf'  # 报告文件名
     reportname = reportname.replace(':', '-')  # 替换冒号
     pp_export_pdf(dataset=dataset, num_models=5 if is_fivemodels else 22, time=end_time,
                   reportname=reportname, sqlitedb=sqlitedb),
diff --git a/models/nerulforcastmodels.py b/models/nerulforcastmodels.py
index 5824b6e..2cd550c 100644
--- a/models/nerulforcastmodels.py
+++ b/models/nerulforcastmodels.py
@@ -173,9 +173,9 @@ def ex_Model(df, horizon, input_size, train_steps, val_check_steps, early_stop_p
 
     if is_fivemodels:
         # 获取之前存好的最好的五个模型
-        with open(os.path.join(dataset, 'best_modelnames.txt'), 'r', encoding='utf-8') as f:
+        with open(os.path.join(config.dataset, 'best_modelnames.txt'), 'r', encoding='utf-8') as f:
             best_modelnames = f.readlines()[0]
-            logger.info(f'获取本地最佳模型名称：{best_modelnames}')
+            config.logger.info(f'获取本地最佳模型名称：{best_modelnames}')
 
         # 重新拼接models
         all_models = models
@@ -194,7 +194,7 @@ def ex_Model(df, horizon, input_size, train_steps, val_check_steps, early_stop_p
         nf_preds = nf.cross_validation(
             df=df_train, val_size=val_size, test_size=test_size, n_windows=None)
         nf_preds.to_csv(os.path.join(
-            dataset, "cross_validation.csv"), index=False)
+        config.dataset,"cross_validation.csv"), index=False)
 
         nf_preds = nf_preds.reset_index()
         # 保存模型
@@ -202,12 +202,12 @@ def ex_Model(df, horizon, input_size, train_steps, val_check_steps, early_stop_p
         filename = f'{settings}--{now}.joblib'
         # 文件名去掉冒号
         filename = filename.replace(':', '-')  # 替换冒号
-        dump(nf, os.path.join(dataset, filename))
+        dump(nf, os.path.join(config.dataset, filename))
     else:
         # glob获取dataset下最新的joblib文件
         import glob
         filename = max(glob.glob(os.path.join(
-            dataset, '*.joblib')), key=os.path.getctime)
+        config.dataset,'*.joblib')), key=os.path.getctime)
         config.logger.info('读取模型：' + filename)
         nf = load(filename)
     # 测试集预测
@@ -215,7 +215,7 @@ def ex_Model(df, horizon, input_size, train_steps, val_check_steps, early_stop_p
         df=df_test, val_size=val_size, test_size=test_size, n_windows=None)
     # 测试集预测结果保存
     nf_test_preds.to_csv(os.path.join(
-        dataset, "cross_validation.csv"), index=False)
+    config.dataset,"cross_validation.csv"), index=False)
 
     df_test['ds'] = pd.to_datetime(df_test['ds'], errors='coerce')
 
@@ -231,7 +231,7 @@ def ex_Model(df, horizon, input_size, train_steps, val_check_steps, early_stop_p
     df_predict['created_dt'] = end_time
 
     # 保存预测值
-    df_predict.to_csv(os.path.join(dataset, "predict.csv"), index=False)
+    df_predict.to_csv(os.path.join(config.dataset, "predict.csv"), index=False)
 
     # 将预测结果保存到数据库
     save_to_database(config.sqlitedb, df_predict, 'predict', end_time)
@@ -330,11 +330,11 @@ def ex_Model_Juxiting(df, horizon, input_size, train_steps, val_check_steps, ear
     df_test['unique_id'] = 1
 
     # 显示划分后的数据集的前几行
-    logger.info("Training set head:")
-    logger.info(df_train.head())
+    config.logger.info("Training set head:")
+    config.logger.info(df_train.head())
 
-    logger.info("\nTesting set head:")
-    logger.info(df_test.head())
+    config.logger.info("\nTesting set head:")
+    config.logger.info(df_test.head())
 
     models = [
         NHITS(h=horizon, input_size=input_size, max_steps=train_steps, val_check_steps=val_check_steps,
@@ -391,9 +391,9 @@ def ex_Model_Juxiting(df, horizon, input_size, train_steps, val_check_steps, ear
 
     if is_fivemodels:
         # 获取之前存好的最好的五个模型
-        with open(os.path.join(dataset, 'best_modelnames.txt'), 'r', encoding='utf-8') as f:
+        with open(os.path.join(config.dataset, 'best_modelnames.txt'), 'r', encoding='utf-8') as f:
             best_modelnames = f.readlines()[0]
-            logger.info(f'获取本地最佳模型名称：{best_modelnames}')
+            config.logger.info(f'获取本地最佳模型名称：{best_modelnames}')
 
         # 重新拼接models
         all_models = models
@@ -411,7 +411,7 @@ def ex_Model_Juxiting(df, horizon, input_size, train_steps, val_check_steps, ear
         nf_preds = nf.cross_validation(
             df=df_train, val_size=val_size, test_size=test_size, n_windows=None)
         nf_preds.to_csv(os.path.join(
-            dataset, "cross_validation.csv"), index=False)
+        config.dataset,"cross_validation.csv"), index=False)
 
         nf_preds = nf_preds.reset_index()
         # 保存模型
@@ -419,20 +419,20 @@ def ex_Model_Juxiting(df, horizon, input_size, train_steps, val_check_steps, ear
         filename = f'{settings}--{now}.joblib'
         # 文件名去掉冒号
         filename = filename.replace(':', '-')  # 替换冒号
-        dump(nf, os.path.join(dataset, filename))
+        dump(nf, os.path.join(config.dataset, filename))
     else:
         # glob获取dataset下最新的joblib文件
         import glob
         filename = max(glob.glob(os.path.join(
-            dataset, '*.joblib')), key=os.path.getctime)
-        logger.info('读取模型：' + filename)
+        config.dataset,'*.joblib')), key=os.path.getctime)
+        config.logger.info('读取模型：' + filename)
         nf = load(filename)
     # 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)
     # 测试集预测结果保存
     nf_test_preds.to_csv(os.path.join(
-        dataset, "cross_validation.csv"), index=False)
+    config.dataset,"cross_validation.csv"), index=False)
 
     df_test['ds'] = pd.to_datetime(df_test['ds'], errors='coerce')
 
@@ -451,7 +451,7 @@ def ex_Model_Juxiting(df, horizon, input_size, train_steps, val_check_steps, ear
     df_predict['created_dt'] = end_time
 
     # 保存预测值
-    df_predict.to_csv(os.path.join(dataset, "predict.csv"), index=False)
+    df_predict.to_csv(os.path.join(config.dataset, "predict.csv"), index=False)
 
     # 将预测结果保存到数据库
     save_to_database(sqlitedb, df_predict, 'predict', end_time)
@@ -485,18 +485,18 @@ def model_losss_yongan(sqlitedb, end_time, table_name_prefix):
     most_model_name = most_model[0]
 
     # 预测数据处理 predict
-    # df_combined = loadcsv(os.path.join(dataset,"cross_validation.csv"))
+    # df_combined = loadcsv(os.path.join(config.dataset,"cross_validation.csv"))
     # df_combined = dateConvert(df_combined)
     df_combined = sqlitedb.select_data(
         'accuracy', where_condition=f"created_dt <= '{end_time}'")
     df_combined4 = df_combined.copy()  # 备份df_combined,后面画图需要
     # 删除缺失值大于80%的列
-    logger.info(df_combined.shape)
+    config.logger.info(df_combined.shape)
     df_combined = df_combined.loc[:, df_combined.isnull().mean() < 0.8]
-    logger.info(df_combined.shape)
+    config.logger.info(df_combined.shape)
     # 删除缺失值
     df_combined.dropna(inplace=True)
-    logger.info(df_combined.shape)
+    config.logger.info(df_combined.shape)
     # 其他列转为数值类型
     df_combined = df_combined.astype({col: 'float32' for col in df_combined.columns if col not in [
                                      'CREAT_DATE', 'ds', 'created_dt']})
@@ -536,7 +536,7 @@ def model_losss_yongan(sqlitedb, end_time, table_name_prefix):
     model_results3 = model_results3.sort_values(
         by='平均平方误差(MSE)', ascending=True)
     model_results3.to_csv(os.path.join(
-        dataset, "model_evaluation.csv"), index=False)
+    config.dataset,"model_evaluation.csv"), index=False)
     modelnames = model_results3['模型(Model)'].tolist()
     allmodelnames = modelnames.copy()
     # 保存5个最佳模型的名称
@@ -545,7 +545,7 @@ def model_losss_yongan(sqlitedb, end_time, table_name_prefix):
     if is_fivemodels:
         pass
     else:
-        with open(os.path.join(dataset, "best_modelnames.txt"), 'w') as f:
+        with open(os.path.join(config.dataset, "best_modelnames.txt"), 'w') as f:
             f.write(','.join(modelnames) + '\n')
 
     # 预测值与真实值对比图
@@ -560,12 +560,12 @@ def model_losss_yongan(sqlitedb, end_time, table_name_prefix):
         plt.ylabel('价格')
         plt.title(model+'拟合')
     plt.subplots_adjust(hspace=0.5)
-    plt.savefig(os.path.join(dataset, '预测值与真实值对比图.png'), bbox_inches='tight')
+    plt.savefig(os.path.join(config.dataset, '预测值与真实值对比图.png'), bbox_inches='tight')
     plt.close()
 
     # # 历史数据+预测数据
     # # 拼接未来时间预测
-    df_predict = pd.read_csv(os.path.join(dataset, 'predict.csv'))
+    df_predict = pd.read_csv(os.path.join(config.dataset, 'predict.csv'))
     df_predict.drop('unique_id', inplace=True, axis=1)
     df_predict.dropna(axis=1, inplace=True)
 
@@ -713,7 +713,7 @@ def model_losss_yongan(sqlitedb, end_time, table_name_prefix):
             sqlitedb.update_data(
                 'accuracy', f"min_price = {row['min_price'].values[0]},max_price = {row['max_price'].values[0]},mean={row['mean'].values[0]}", f"id = {id}")
         except:
-            logger.error(f'更新accuracy表中的min_price,max_price,mean值失败，row={row}')
+            config.logger.error(f'更新accuracy表中的min_price,max_price,mean值失败，row={row}')
 
     df = accuracy_df.copy()
     df['ds'] = pd.to_datetime(df['ds'])
@@ -802,7 +802,7 @@ def model_losss_yongan(sqlitedb, end_time, table_name_prefix):
         except ValueError:
             pass
     df_combined3.to_csv(os.path.join(
-        dataset, "testandpredict_groupby.csv"), index=False)
+    config.dataset,"testandpredict_groupby.csv"), index=False)
 
     # 历史价格+预测价格
     sqlitedb.drop_table('testandpredict_groupby')
@@ -836,7 +836,7 @@ def model_losss_yongan(sqlitedb, end_time, table_name_prefix):
         plt.xlabel('日期')
         plt.ylabel('价格')
 
-        plt.savefig(os.path.join(dataset, '历史价格-预测值.png'), bbox_inches='tight')
+        plt.savefig(os.path.join(config.dataset, '历史价格-预测值.png'), bbox_inches='tight')
         plt.close()
 
     def _plt_modeltopten_predict_ture(df):
@@ -870,7 +870,7 @@ def model_losss_yongan(sqlitedb, end_time, table_name_prefix):
         plt.xlabel('日期')
         plt.ylabel('价格')
 
-        plt.savefig(os.path.join(dataset, '历史价格-预测值1.png'),
+        plt.savefig(os.path.join(config.dataset, '历史价格-预测值1.png'),
                     bbox_inches='tight')
         plt.close()
 
@@ -891,7 +891,7 @@ def model_losss_yongan(sqlitedb, end_time, table_name_prefix):
         table.set_fontsize(10)
 
         # 设置表格样式，列数据最小的用绿色标识
-        plt.savefig(os.path.join(dataset, '预测值表格.png'), bbox_inches='tight')
+        plt.savefig(os.path.join(config.dataset, '预测值表格.png'), bbox_inches='tight')
         plt.close()
 
     def _plt_model_results3():
@@ -906,7 +906,7 @@ def model_losss_yongan(sqlitedb, end_time, table_name_prefix):
         table.set_fontsize(10)
 
         # 设置表格样式，列数据最小的用绿色标识
-        plt.savefig(os.path.join(dataset, '模型评估.png'), bbox_inches='tight')
+        plt.savefig(os.path.join(config.dataset, '模型评估.png'), bbox_inches='tight')
         plt.close()
 
     _plt_predict_ture(df_combined3)
@@ -1405,19 +1405,19 @@ def model_losss_juxitingbak(sqlitedb, end_time):
     most_model_name = most_model[0]
 
     # 预测数据处理 predict
-    df_combined = loadcsv(os.path.join(dataset, "cross_validation.csv"))
+    df_combined = loadcsv(os.path.join(config.dataset, "cross_validation.csv"))
     df_combined.drop(columns=['cutoff'], inplace=True)
     df_combined['CREAT_DATE'] = end_time
     df_combined = dateConvert(df_combined)
     # df_combined = sqlitedb.select_data('accuracy',where_condition=f"created_dt <= '{end_time}'")
     df_combined4 = df_combined.copy()  # 备份df_combined,后面画图需要
     # 删除缺失值大于80%的列
-    logger.info(df_combined.shape)
+    config.logger.info(df_combined.shape)
     df_combined = df_combined.loc[:, df_combined.isnull().mean() < 0.8]
-    logger.info(df_combined.shape)
+    config.logger.info(df_combined.shape)
     # 删除缺失值
     df_combined.dropna(inplace=True)
-    logger.info(df_combined.shape)
+    config.logger.info(df_combined.shape)
     # 其他列转为数值类型
     df_combined = df_combined.astype({col: 'float32' for col in df_combined.columns if col not in [
                                      'CREAT_DATE', 'ds', 'created_dt']})
@@ -1457,7 +1457,7 @@ def model_losss_juxitingbak(sqlitedb, end_time):
     model_results3 = model_results3.sort_values(
         by='平均平方误差(MSE)', ascending=True)
     model_results3.to_csv(os.path.join(
-        dataset, "model_evaluation.csv"), index=False)
+    config.dataset,"model_evaluation.csv"), index=False)
     modelnames = model_results3['模型(Model)'].tolist()
     allmodelnames = modelnames.copy()
     # 保存5个最佳模型的名称
@@ -1466,7 +1466,7 @@ def model_losss_juxitingbak(sqlitedb, end_time):
     if is_fivemodels:
         pass
     else:
-        with open(os.path.join(dataset, "best_modelnames.txt"), 'w') as f:
+        with open(os.path.join(config.dataset, "best_modelnames.txt"), 'w') as f:
             f.write(','.join(modelnames) + '\n')
 
     # 预测值与真实值对比图
@@ -1481,12 +1481,12 @@ def model_losss_juxitingbak(sqlitedb, end_time):
         plt.ylabel('价格')
         plt.title(model+'拟合')
     plt.subplots_adjust(hspace=0.5)
-    plt.savefig(os.path.join(dataset, '预测值与真实值对比图.png'), bbox_inches='tight')
+    plt.savefig(os.path.join(config.dataset, '预测值与真实值对比图.png'), bbox_inches='tight')
     plt.close()
 
     # # 历史数据+预测数据
     # # 拼接未来时间预测
-    df_predict = pd.read_csv(os.path.join(dataset, 'predict.csv'))
+    df_predict = pd.read_csv(os.path.join(config.dataset, 'predict.csv'))
     df_predict.drop('unique_id', inplace=True, axis=1)
     df_predict.dropna(axis=1, inplace=True)
 
@@ -1634,7 +1634,7 @@ def model_losss_juxitingbak(sqlitedb, end_time):
             sqlitedb.update_data(
                 'accuracy', f"min_price = {row['min_price'].values[0]},max_price = {row['max_price'].values[0]},mean={row['mean'].values[0]}", f"id = {id}")
         except:
-            logger.error(f'更新accuracy表中的min_price,max_price,mean值失败，row={row}')
+            config.logger.error(f'更新accuracy表中的min_price,max_price,mean值失败，row={row}')
 
     df = accuracy_df.copy()
     df['ds'] = pd.to_datetime(df['ds'])
@@ -1723,7 +1723,7 @@ def model_losss_juxitingbak(sqlitedb, end_time):
         except ValueError:
             pass
     df_combined3.to_csv(os.path.join(
-        dataset, "testandpredict_groupby.csv"), index=False)
+    config.dataset,"testandpredict_groupby.csv"), index=False)
 
     # 历史价格+预测价格
     sqlitedb.drop_table('testandpredict_groupby')
@@ -1767,7 +1767,7 @@ def model_losss_juxitingbak(sqlitedb, end_time):
         plt.xticks(rotation=45)  # 日期标签旋转45度，防止重叠
         plt.ylabel('价格')
 
-        plt.savefig(os.path.join(dataset, '历史价格-预测值.png'), bbox_inches='tight')
+        plt.savefig(os.path.join(config.dataset, '历史价格-预测值.png'), bbox_inches='tight')
         plt.close()
 
     def _plt_modeltopten_predict_ture(df):
@@ -1806,7 +1806,7 @@ def model_losss_juxitingbak(sqlitedb, end_time):
 
         plt.ylabel('价格')
 
-        plt.savefig(os.path.join(dataset, '历史价格-预测值1.png'),
+        plt.savefig(os.path.join(config.dataset, '历史价格-预测值1.png'),
                     bbox_inches='tight')
         plt.close()
 
@@ -1827,7 +1827,7 @@ def model_losss_juxitingbak(sqlitedb, end_time):
         table.set_fontsize(10)
 
         # 设置表格样式，列数据最小的用绿色标识
-        plt.savefig(os.path.join(dataset, '预测值表格.png'), bbox_inches='tight')
+        plt.savefig(os.path.join(config.dataset, '预测值表格.png'), bbox_inches='tight')
         plt.close()
 
     def _plt_model_results3():
@@ -1842,7 +1842,7 @@ def model_losss_juxitingbak(sqlitedb, end_time):
         table.set_fontsize(10)
 
         # 设置表格样式，列数据最小的用绿色标识
-        plt.savefig(os.path.join(dataset, '模型评估.png'), bbox_inches='tight')
+        plt.savefig(os.path.join(config.dataset, '模型评估.png'), bbox_inches='tight')
         plt.close()
 
     _plt_predict_ture(df_combined3)
@@ -1855,7 +1855,7 @@ def model_losss_juxitingbak(sqlitedb, end_time):
 
 # 聚烯烃计算预测评估指数
 @exception_logger
-def model_losss_juxiting(sqlitedb):
+def model_losss_juxiting(sqlitedb,end_time,is_fivemodels):
     global dataset
     global rote
     most_model = [sqlitedb.select_data('most_model', columns=[
@@ -1863,7 +1863,7 @@ def model_losss_juxiting(sqlitedb):
     most_model_name = most_model[0]
 
     # 预测数据处理 predict
-    df_combined = loadcsv(os.path.join(dataset, "cross_validation.csv"))
+    df_combined = loadcsv(os.path.join(config.dataset, "cross_validation.csv"))
     df_combined = dateConvert(df_combined)
     # 删除空列
     df_combined.dropna(axis=1, inplace=True)
@@ -1909,7 +1909,7 @@ def model_losss_juxiting(sqlitedb):
     model_results3 = model_results3.sort_values(
         by='平均平方误差(MSE)', ascending=True)
     model_results3.to_csv(os.path.join(
-        dataset, "model_evaluation.csv"), index=False)
+        config.dataset, "model_evaluation.csv"), index=False)
     modelnames = model_results3['模型(Model)'].tolist()
     allmodelnames = modelnames.copy()
     # 保存5个最佳模型的名称
@@ -1918,7 +1918,7 @@ def model_losss_juxiting(sqlitedb):
     if is_fivemodels:
         pass
     else:
-        with open(os.path.join(dataset, "best_modelnames.txt"), 'w') as f:
+        with open(os.path.join(config.dataset, "best_modelnames.txt"), 'w') as f:
             f.write(','.join(modelnames) + '\n')
 
     # 预测值与真实值对比图
@@ -1933,12 +1933,12 @@ def model_losss_juxiting(sqlitedb):
         plt.ylabel('价格')
         plt.title(model+'拟合')
     plt.subplots_adjust(hspace=0.5)
-    plt.savefig(os.path.join(dataset, '预测值与真实值对比图.png'), bbox_inches='tight')
+    plt.savefig(os.path.join(config.dataset, '预测值与真实值对比图.png'), bbox_inches='tight')
     plt.close()
 
     # # 历史数据+预测数据
     # # 拼接未来时间预测
-    df_predict = pd.read_csv(os.path.join(dataset, 'predict.csv'))
+    df_predict = pd.read_csv(os.path.join(config.dataset, 'predict.csv'))
     df_predict.drop('unique_id', inplace=True, axis=1)
     df_predict.dropna(axis=1, inplace=True)
 
@@ -1990,7 +1990,7 @@ def model_losss_juxiting(sqlitedb):
     def add_rote_column(row):
         columns = []
         for r in names_df.columns:
-            if row[r] <= rote:
+            if row[r] <= config.rote:
                 columns.append(r.split('-')[0])
         return pd.Series([columns], index=['columns'])
     names_df['columns'] = names_df.apply(add_rote_column, axis=1)
@@ -2061,7 +2061,7 @@ def model_losss_juxiting(sqlitedb):
         except ValueError:
             pass
     df_combined3.to_csv(os.path.join(
-        dataset, "testandpredict_groupby.csv"), index=False)
+    config.dataset,"testandpredict_groupby.csv"), index=False)
 
     # 历史价格+预测价格
     sqlitedb.drop_table('testandpredict_groupby')
@@ -2092,12 +2092,12 @@ def model_losss_juxiting(sqlitedb):
         for model in most_model:
             plt.plot(df['ds'], df[model], label=model, marker='o')
         # 当前日期画竖虚线
-        plt.axvline(x=df['ds'].iloc[-horizon], color='r', linestyle='--')
+        plt.axvline(x=df['ds'].iloc[-config.horizon], color='r', linestyle='--')
         plt.legend()
         plt.xlabel('日期')
         plt.ylabel('价格')
 
-        plt.savefig(os.path.join(dataset, '历史价格-预测值.png'), bbox_inches='tight')
+        plt.savefig(os.path.join(config.dataset, '历史价格-预测值.png'), bbox_inches='tight')
         plt.close()
 
     def _plt_predict_table(df):
@@ -2106,8 +2106,8 @@ def model_losss_juxiting(sqlitedb):
         ax.axis('off')  # 关闭坐标轴
         # 数值保留2位小数
         df = df.round(2)
-        df = df[-horizon:]
-        df['Day'] = [f'Day_{i}' for i in range(1, horizon+1)]
+        df = df[-config.horizon:]
+        df['Day'] = [f'Day_{i}' for i in range(1, config.horizon+1)]
         # Day列放到最前面
         df = df[['Day'] + list(df.columns[:-1])]
         table = ax.table(cellText=df.values,
@@ -2117,7 +2117,7 @@ def model_losss_juxiting(sqlitedb):
         table.set_fontsize(10)
 
         # 设置表格样式，列数据最小的用绿色标识
-        plt.savefig(os.path.join(dataset, '预测值表格.png'), bbox_inches='tight')
+        plt.savefig(os.path.join(config.dataset, '预测值表格.png'), bbox_inches='tight')
         plt.close()
 
     def _plt_model_results3():
@@ -2132,7 +2132,7 @@ def model_losss_juxiting(sqlitedb):
         table.set_fontsize(10)
 
         # 设置表格样式，列数据最小的用绿色标识
-        plt.savefig(os.path.join(dataset, '模型评估.png'), bbox_inches='tight')
+        plt.savefig(os.path.join(config.dataset, '模型评估.png'), bbox_inches='tight')
         plt.close()
 
     _plt_predict_ture(df_combined3)
@@ -2150,7 +2150,7 @@ def brent_export_pdf(num_indicators=475, num_models=21, num_dayindicator=202, in
     # 获取特征的近一月值
     import pandas as pd
     feature_data_df = pd.read_csv(os.path.join(
-        dataset, '指标数据添加时间特征.csv'), parse_dates=['ds']).tail(60)
+    config.dataset,'指标数据添加时间特征.csv'), parse_dates=['ds']).tail(60)
 
     def draw_feature_trend(feature_data_df, features):
         # 画特征近60天的趋势图
@@ -2198,9 +2198,9 @@ def brent_export_pdf(num_indicators=475, num_models=21, num_dayindicator=202, in
                 col = col.replace('*', '-')
                 col = col.replace(':', '-')
                 col = col.replace(r'/', '-')
-                plt.savefig(os.path.join(dataset, f'{col}与价格散点图.png'))
+                plt.savefig(os.path.join(config.dataset, f'{col}与价格散点图.png'))
                 content.append(Graphs.draw_img(
-                    os.path.join(dataset, f'{col}与价格散点图.png')))
+                    os.path.join(config.dataset, f'{col}与价格散点图.png')))
                 plt.close()
             # except Exception as e:
             #     print(f'绘制第{i+1}个特征{col}与价格散点图时出错：{e}')
@@ -2211,26 +2211,26 @@ def brent_export_pdf(num_indicators=475, num_models=21, num_dayindicator=202, in
     # 预测结果
     content.append(Graphs.draw_little_title('一、预测结果：'))
     # 添加历史走势及预测价格的走势图片
-    content.append(Graphs.draw_img(os.path.join(dataset, '历史价格-预测值.png')))
+    content.append(Graphs.draw_img(os.path.join(config.dataset, '历史价格-预测值.png')))
     # 波动率画图逻辑
     content.append(Graphs.draw_text('图示说明：'))
     content.append(Graphs.draw_text(
         '    确定置信区间：设置残差置信阈值，以每周最佳模型为基准，选取在置信区间的预测值作为置信区间;'))
 
     # 添加历史走势及预测价格的走势图片
-    content.append(Graphs.draw_img(os.path.join(dataset, '历史价格-预测值1.png')))
+    content.append(Graphs.draw_img(os.path.join(config.dataset, '历史价格-预测值1.png')))
     content.append(Graphs.draw_text('图示说明：'))
     content.append(Graphs.draw_text(
         '    确定置信区间：使用模型评估指标MAE得到前十个模型，取平均值上下1.5作为价格波动置信区间;'))
 
     # 取df中y列为空的行
     import pandas as pd
-    df = pd.read_csv(os.path.join(dataset, 'predict.csv'), encoding='gbk')
+    df = pd.read_csv(os.path.join(config.dataset, 'predict.csv'), encoding='gbk')
     df_true = pd.read_csv(os.path.join(
-        dataset, '指标数据添加时间特征.csv'), encoding='utf-8')  # 获取预测日期对应的真实值
+    config.dataset,'指标数据添加时间特征.csv'), encoding='utf-8')  # 获取预测日期对应的真实值
     df_true = df_true[['ds', 'y']]
     eval_df = pd.read_csv(os.path.join(
-        dataset, 'model_evaluation.csv'), encoding='utf-8')
+    config.dataset,'model_evaluation.csv'), encoding='utf-8')
     # 按评估指标排序，取前五
     fivemodels_list = eval_df['模型(Model)'].values  # 列表形式，后面当作列名索引使用
     # 取 fivemodels_list 和 ds 列
@@ -2258,7 +2258,7 @@ def brent_export_pdf(num_indicators=475, num_models=21, num_dayindicator=202, in
     content.append(Graphs.draw_table(col_width, *data))
     content.append(Graphs.draw_little_title('二、上一预测周期偏差率分析：'))
     df = pd.read_csv(os.path.join(
-        dataset, 'testandpredict_groupby.csv'), encoding='utf-8')
+    config.dataset,'testandpredict_groupby.csv'), encoding='utf-8')
     df4 = df.copy()  # 计算偏差率使用
     # 去掉created_dt 列
     df4 = df4.drop(columns=['created_dt'])
@@ -2303,14 +2303,14 @@ def brent_export_pdf(num_indicators=475, num_models=21, num_dayindicator=202, in
         f'本次预测使用了一个专门收集时间序列的NeuralForecast库中的{num_models}个模型：'))
     content.append(Graphs.draw_text(f'使用40天的数据预测未来{inputsize}天的数据。'))
     content.append(Graphs.draw_little_title('指标情况：'))
-    with open(os.path.join(dataset, '特征频度统计.txt'), encoding='utf-8') as f:
+    with open(os.path.join(config.dataset, '特征频度统计.txt'), encoding='utf-8') as f:
         for line in f.readlines():
             content.append(Graphs.draw_text(line))
 
-    data = pd.read_csv(os.path.join(dataset, '指标数据添加时间特征.csv'),
+    data = pd.read_csv(os.path.join(config.dataset, '指标数据添加时间特征.csv'),
                        encoding='utf-8')  # 计算相关系数用
     df_zhibiaofenlei = loadcsv(os.path.join(
-        dataset, '特征处理后的指标名称及分类.csv'))  # 气泡图用
+    config.dataset,'特征处理后的指标名称及分类.csv'))  # 气泡图用
     df_zhibiaoshuju = data.copy()  # 气泡图用
 
     # 绘制特征相关气泡图
@@ -2342,10 +2342,10 @@ def brent_export_pdf(num_indicators=475, num_models=21, num_dayindicator=202, in
         plt.xlabel('相关系数')
         plt.ylabel('频数')
         plt.savefig(os.path.join(
-            dataset, f'{name}类指标相关性直方分布图.png'), bbox_inches='tight')
+        config.dataset,f'{name}类指标相关性直方分布图.png'), bbox_inches='tight')
         plt.close()
         content.append(Graphs.draw_img(
-            os.path.join(dataset, f'{name}类指标相关性直方分布图.png')))
+            os.path.join(config.dataset, f'{name}类指标相关性直方分布图.png')))
         content.append(Graphs.draw_text(
             f'{name}类指标(共{len(cols_subset)}个)的相关性直方分布图如上所示。'))
         # 相关性大于0的特征
@@ -2398,10 +2398,10 @@ def brent_export_pdf(num_indicators=475, num_models=21, num_dayindicator=202, in
         grouped_corr['相关性总和'].min()*5, grouped_corr['相关性总和'].max()*5), hue='指标分类', palette='viridis')
     plt.title('指标分类相关性总和的气泡图')
     plt.ylabel('数量')
-    plt.savefig(os.path.join(dataset, '指标分类相关性总和的气泡图.png'),
+    plt.savefig(os.path.join(config.dataset, '指标分类相关性总和的气泡图.png'),
                 bbox_inches='tight')
     plt.close()
-    content.append(Graphs.draw_img(os.path.join(dataset, '指标分类相关性总和的气泡图.png')))
+    content.append(Graphs.draw_img(os.path.join(config.dataset, '指标分类相关性总和的气泡图.png')))
     content.append(Graphs.draw_text(
         '气泡图中，横轴为指标分类，纵轴为指标分类下的特征数量，气泡的面积越大表示该分类中特征的相关系数和越大。'))
     config.logger.info(f'绘制相关性总和的气泡图结束')
@@ -2409,7 +2409,7 @@ def brent_export_pdf(num_indicators=475, num_models=21, num_dayindicator=202, in
     content.append(Graphs.draw_text(
         f'预测使用了{num_models}个模型进行训练拟合，通过评估指标MAE从小到大排列，前5个模型的简介如下：'))
     # 读取模型简介
-    with open(os.path.join(dataset, 'model_introduction.txt'), 'r', encoding='utf-8') as f:
+    with open(os.path.join(config.dataset, 'model_introduction.txt'), 'r', encoding='utf-8') as f:
         for line in f:
             line_split = line.strip().split('--')
             if line_split[0] in fivemodels_list:
@@ -2417,7 +2417,7 @@ def brent_export_pdf(num_indicators=475, num_models=21, num_dayindicator=202, in
                     content.append(Graphs.draw_text(introduction))
     content.append(Graphs.draw_little_title('模型评估：'))
     df = pd.read_csv(os.path.join(
-        dataset, 'model_evaluation.csv'), encoding='utf-8')
+    config.dataset,'model_evaluation.csv'), encoding='utf-8')
     # 判断 df 的数值列转为float
     for col in eval_df.columns:
         if col not in ['模型(Model)']:
@@ -2443,14 +2443,14 @@ def brent_export_pdf(num_indicators=475, num_models=21, num_dayindicator=202, in
         '3. 平均平方误差(MSE)：平均平方误差是衡量预测值与实际值之间误差的一种方法，取值越小，误差越小，预测效果越好。'))
     content.append(Graphs.draw_text('模型拟合：'))
     # 添加图片
-    content.append(Graphs.draw_img(os.path.join(dataset, '预测值与真实值对比图.png')))
+    content.append(Graphs.draw_img(os.path.join(config.dataset, '预测值与真实值对比图.png')))
     # 生成pdf文件
-    doc = SimpleDocTemplate(os.path.join(dataset, reportname), pagesize=letter)
+    doc = SimpleDocTemplate(os.path.join(config.dataset, reportname), pagesize=letter)
     doc.build(content)
     # pdf 上传到数字化信息平台
     try:
         if config.is_update_report:
-            with open(os.path.join(dataset, reportname), 'rb') as f:
+            with open(os.path.join(config.dataset, reportname), 'rb') as f:
                 base64_data = base64.b64encode(f.read()).decode('utf-8')
                 upload_data["data"]["fileBase64"] = base64_data
             upload_data["data"]["fileName"] = reportname
@@ -2462,13 +2462,12 @@ def brent_export_pdf(num_indicators=475, num_models=21, num_dayindicator=202, in
 
 @exception_logger
 def pp_export_pdf(num_indicators=475, num_models=21, num_dayindicator=202, inputsize=5, dataset='dataset', time='2024-07-30', reportname='report.pdf', sqlitedb='jbsh_yuanyou.db'):
-    global y
     # 创建内容对应的空列表
     content = list()
     # 获取特征的近一月值
     import pandas as pd
     feature_data_df = pd.read_csv(os.path.join(
-        dataset, '指标数据添加时间特征.csv'), parse_dates=['ds']).tail(20)
+    config.dataset,'指标数据添加时间特征.csv'), parse_dates=['ds']).tail(20)
 
     def draw_feature_trend(feature_data_df, features):
         # 画特征近一周的趋势图
@@ -2516,28 +2515,28 @@ def pp_export_pdf(num_indicators=475, num_models=21, num_dayindicator=202, input
                 col = col.replace('*', '-')
                 col = col.replace(':', '-')
                 col = col.replace(r'/', '-')
-                plt.savefig(os.path.join(dataset, f'{col}与价格散点图.png'))
+                plt.savefig(os.path.join(config.dataset, f'{col}与价格散点图.png'))
                 content.append(Graphs.draw_img(
-                    os.path.join(dataset, f'{col}与价格散点图.png')))
+                    os.path.join(config.dataset, f'{col}与价格散点图.png')))
                 plt.close()
             # except Exception as e:
             #     print(f'绘制第{i+1}个特征{col}与价格散点图时出错：{e}')
 
     # 添加标题
-    content.append(Graphs.draw_title(f'{y}{time}预测报告'))
+    content.append(Graphs.draw_title(f'{config.y}{time}预测报告'))
     # 预测结果
     content.append(Graphs.draw_little_title('一、预测结果：'))
     # 添加历史走势及预测价格的走势图片
-    content.append(Graphs.draw_img(os.path.join(dataset, '历史价格-预测值.png')))
+    content.append(Graphs.draw_img(os.path.join(config.dataset, '历史价格-预测值.png')))
 
     # 取df中y列为空的行
     import pandas as pd
-    df = pd.read_csv(os.path.join(dataset, 'predict.csv'), encoding='gbk')
+    df = pd.read_csv(os.path.join(config.dataset, 'predict.csv'), encoding='gbk')
     df_true = pd.read_csv(os.path.join(
-        dataset, '指标数据添加时间特征.csv'), encoding='utf-8')  # 获取预测日期对应的真实值
+    config.dataset,'指标数据添加时间特征.csv'), encoding='utf-8')  # 获取预测日期对应的真实值
     df_true = df_true[['ds', 'y']]
     eval_df = pd.read_csv(os.path.join(
-        dataset, 'model_evaluation.csv'), encoding='utf-8')
+    config.dataset,'model_evaluation.csv'), encoding='utf-8')
     # 按评估指标排序，取前五
     fivemodels_list = eval_df['模型(Model)'].values  # 列表形式，后面当作列名索引使用
     # 取 fivemodels_list 和 ds 列
@@ -2565,7 +2564,7 @@ def pp_export_pdf(num_indicators=475, num_models=21, num_dayindicator=202, input
     content.append(Graphs.draw_table(col_width, *data))
     content.append(Graphs.draw_little_title('二、上一预测周期偏差率分析：'))
     df = pd.read_csv(os.path.join(
-        dataset, 'testandpredict_groupby.csv'), encoding='utf-8')
+    config.dataset,'testandpredict_groupby.csv'), encoding='utf-8')
     df4 = df.copy()  # 计算偏差率使用
     # 计算模型偏差率
     # 计算各列对于y列的差值百分比
@@ -2601,14 +2600,14 @@ def pp_export_pdf(num_indicators=475, num_models=21, num_dayindicator=202, input
         f'本次预测使用了一个专门收集时间序列的NeuralForecast库中的{num_models}个模型：'))
     content.append(Graphs.draw_text(f'使用40天的数据预测未来{inputsize}天的数据。'))
     content.append(Graphs.draw_little_title('指标情况：'))
-    with open(os.path.join(dataset, '特征频度统计.txt'), encoding='utf-8') as f:
+    with open(os.path.join(config.dataset, '特征频度统计.txt'), encoding='utf-8') as f:
         for line in f.readlines():
             content.append(Graphs.draw_text(line))
 
-    data = pd.read_csv(os.path.join(dataset, '指标数据添加时间特征.csv'),
+    data = pd.read_csv(os.path.join(config.dataset, '指标数据添加时间特征.csv'),
                        encoding='utf-8')  # 计算相关系数用
     df_zhibiaofenlei = loadcsv(os.path.join(
-        dataset, '特征处理后的指标名称及分类.csv'))  # 气泡图用
+    config.dataset,'特征处理后的指标名称及分类.csv'))  # 气泡图用
     df_zhibiaoshuju = data.copy()  # 气泡图用
 
     # 绘制特征相关气泡图
@@ -2627,7 +2626,7 @@ def pp_export_pdf(num_indicators=475, num_models=21, num_dayindicator=202, input
         '''相关系数接近0：表示两个变量之间不存在线性关系，即它们的变化不会随着对方的变化而变化。'''))
     for name, group in grouped:
         cols = group['指标名称'].tolist()
-        logger.info(f'开始绘制{name}类指标的相关性直方图')
+        config.logger.info(f'开始绘制{name}类指标的相关性直方图')
         cols_subset = cols
         feature_names = ['y'] + cols_subset
         correlation_matrix = df_zhibiaoshuju[feature_names].corr()['y']
@@ -2640,10 +2639,10 @@ def pp_export_pdf(num_indicators=475, num_models=21, num_dayindicator=202, input
         plt.xlabel('相关系数')
         plt.ylabel('频数')
         plt.savefig(os.path.join(
-            dataset, f'{name}类指标相关性直方分布图.png'), bbox_inches='tight')
+        config.dataset,f'{name}类指标相关性直方分布图.png'), bbox_inches='tight')
         plt.close()
         content.append(Graphs.draw_img(
-            os.path.join(dataset, f'{name}类指标相关性直方分布图.png')))
+            os.path.join(config.dataset, f'{name}类指标相关性直方分布图.png')))
         content.append(Graphs.draw_text(
             f'{name}类指标(共{len(cols_subset)}个)的相关性直方分布图如上所示。'))
         # 相关性大于0的特征
@@ -2683,7 +2682,7 @@ def pp_export_pdf(num_indicators=475, num_models=21, num_dayindicator=202, input
 
         # 计算correlation_sum 第一行的相关性的绝对值的总和
         correlation_sum = correlation_matrix.abs().sum()
-        logger.info(f'{name}类指标的相关性总和为：{correlation_sum}')
+        config.logger.info(f'{name}类指标的相关性总和为：{correlation_sum}')
         # 分组的相关性总和拼接到grouped_corr
         goup_corr = pd.DataFrame(
             {'指标分类': [name], '指标数量': [len(cols_subset)], '相关性总和': [correlation_sum]})
@@ -2691,26 +2690,26 @@ def pp_export_pdf(num_indicators=475, num_models=21, num_dayindicator=202, input
             [grouped_corr, goup_corr], axis=0, ignore_index=True)
 
     # 绘制相关性总和的气泡图
-    logger.info(f'开始绘制相关性总和的气泡图')
+    config.logger.info(f'开始绘制相关性总和的气泡图')
     plt.figure(figsize=(10, 10))
     sns.scatterplot(data=grouped_corr, x='相关性总和', y='指标数量', size='相关性总和', sizes=(
         grouped_corr['相关性总和'].min()*5, grouped_corr['相关性总和'].max()*5), hue='指标分类', palette='viridis')
     plt.title('指标分类相关性总和的气泡图')
     plt.ylabel('数量')
-    plt.savefig(os.path.join(dataset, '指标分类相关性总和的气泡图.png'),
+    plt.savefig(os.path.join(config.dataset, '指标分类相关性总和的气泡图.png'),
                 bbox_inches='tight')
     plt.close()
-    content.append(Graphs.draw_img(os.path.join(dataset, '指标分类相关性总和的气泡图.png')))
+    content.append(Graphs.draw_img(os.path.join(config.dataset, '指标分类相关性总和的气泡图.png')))
     content.append(Graphs.draw_text(
         '气泡图中，横轴为指标分类，纵轴为指标分类下的特征数量，气泡的面积越大表示该分类中特征的相关系数和越大。'))
-    logger.info(f'绘制相关性总和的气泡图结束')
+    config.logger.info(f'绘制相关性总和的气泡图结束')
 
     content.append(Graphs.draw_little_title('模型选择：'))
     content.append(Graphs.draw_text(
         f'预测使用了{num_models}个模型进行训练拟合，通过评估指标MAE从小到大排列，前5个模型的简介如下：'))
 
     # 读取模型简介
-    with open(os.path.join(dataset, 'model_introduction.txt'), 'r', encoding='utf-8') as f:
+    with open(os.path.join(config.dataset, 'model_introduction.txt'), 'r', encoding='utf-8') as f:
         for line in f:
             line_split = line.strip().split('--')
             if line_split[0] in fivemodels_list:
@@ -2720,7 +2719,7 @@ def pp_export_pdf(num_indicators=475, num_models=21, num_dayindicator=202, input
     content.append(Graphs.draw_little_title('模型评估：'))
 
     df = pd.read_csv(os.path.join(
-        dataset, 'model_evaluation.csv'), encoding='utf-8')
+    config.dataset,'model_evaluation.csv'), encoding='utf-8')
     # 判断 df 的数值列转为float
     for col in eval_df.columns:
         if col not in ['模型(Model)']:
@@ -2742,12 +2741,12 @@ def pp_export_pdf(num_indicators=475, num_models=21, num_dayindicator=202, input
     content.append(Graphs.draw_text(
         '3. 平均平方误差(MSE)：平均平方误差是衡量预测值与实际值之间误差的一种方法，取值越小，误差越小，预测效果越好。'))
     content.append(Graphs.draw_text('模型拟合：'))
-    content.append(Graphs.draw_img(os.path.join(dataset, '预测值与真实值对比图.png')))
+    content.append(Graphs.draw_img(os.path.join(config.dataset, '预测值与真实值对比图.png')))
 
     # 附1，特征列表
     content.append(Graphs.draw_little_title('附1、特征列表：'))
     df_fuyi = pd.read_csv(os.path.join(
-        dataset, '特征频度统计.csv'), encoding='utf-8')
+    config.dataset,'特征频度统计.csv'), encoding='utf-8')
     for col in df_fuyi.columns:
         fuyi = df_fuyi[col]
         fuyi = fuyi.dropna()
@@ -2756,18 +2755,18 @@ def pp_export_pdf(num_indicators=475, num_models=21, num_dayindicator=202, input
             content.append(Graphs.draw_text(f'{i+1}、{fuyi[i]}'))
 
     # 生成pdf文件
-    doc = SimpleDocTemplate(os.path.join(dataset, reportname), pagesize=letter)
-    # doc = SimpleDocTemplate(os.path.join(dataset,'reportname.pdf'), pagesize=letter)
+    doc = SimpleDocTemplate(os.path.join(config.dataset, reportname), pagesize=letter)
+    # doc = SimpleDocTemplate(os.path.join(config.dataset,'reportname.pdf'), pagesize=letter)
     doc.build(content)
     # pdf 上传到数字化信息平台
     try:
-        if is_update_report:
-            with open(os.path.join(dataset, reportname), 'rb') as f:
+        if config.is_update_report:
+            with open(os.path.join(config.dataset, reportname), 'rb') as f:
                 base64_data = base64.b64encode(f.read()).decode('utf-8')
-                upload_data["data"]["fileBase64"] = base64_data
-            upload_data["data"]["fileName"] = reportname
+                global_config['upload_data']["data"]["fileBase64"] = base64_data
+            global_config['upload_data']["data"]["fileName"] = reportname
             token = get_head_auth_report()
-            upload_report_data(token, upload_data)
+            upload_report_data(token, config.upload_data)
     except TimeoutError as e:
         print(f"请求超时: {e}")
 
@@ -2785,19 +2784,19 @@ def pp_export_pdf_v1(num_indicators=475, num_models=21, num_dayindicator=202, in
     # 添加图片
     # 找出后缀是历史价格-预测值.png的图片
     # import glob
-    # imgs = glob.glob(os.path.join(dataset,'*历史价格-预测值.png'))
+    # imgs = glob.glob(os.path.join(config.dataset,'*历史价格-预测值.png'))
     # for img in imgs:
     #     content.append(Graphs.draw_img(img))
-    content.append(Graphs.draw_img(os.path.join(dataset, '历史价格-预测值.png')))
+    content.append(Graphs.draw_img(os.path.join(config.dataset, '历史价格-预测值.png')))
 
     # 取df中y列为空的行
     import pandas as pd
-    df = pd.read_csv(os.path.join(dataset, 'predict.csv'), encoding='gbk')
+    df = pd.read_csv(os.path.join(config.dataset, 'predict.csv'), encoding='gbk')
     df_true = pd.read_csv(os.path.join(
-        dataset, '指标数据添加时间特征.csv'), encoding='utf-8')  # 获取预测日期对应的真实值
+    config.dataset,'指标数据添加时间特征.csv'), encoding='utf-8')  # 获取预测日期对应的真实值
     df_true = df_true[['ds', 'y']]
     eval_df = pd.read_csv(os.path.join(
-        dataset, 'model_evaluation.csv'), encoding='utf-8')
+    config.dataset,'model_evaluation.csv'), encoding='utf-8')
     # 按评估指标排序，取前五
     fivemodels_list = eval_df['模型(Model)'].values  # 列表形式，后面当作列名索引使用
     # 取 fivemodels_list 和 ds 列
@@ -2825,7 +2824,7 @@ def pp_export_pdf_v1(num_indicators=475, num_models=21, num_dayindicator=202, in
     content.append(Graphs.draw_table(col_width, *data))
     content.append(Graphs.draw_little_title('二、上一预测周期偏差率分析：'))
     df = pd.read_csv(os.path.join(
-        dataset, 'testandpredict_groupby.csv'), encoding='utf-8')
+    config.dataset,'testandpredict_groupby.csv'), encoding='utf-8')
     df4 = df.copy()  # 计算偏差率使用
     # 计算模型偏差率
     # 计算各列对于y列的差值百分比
@@ -2860,7 +2859,7 @@ def pp_export_pdf_v1(num_indicators=475, num_models=21, num_dayindicator=202, in
         f'本次预测使用了一个专门收集时间序列的NeuralForecast库中的{num_models}个模型：'))
     content.append(Graphs.draw_text(f'使用40天的数据预测未来{inputsize}天的数据。'))
     content.append(Graphs.draw_little_title('指标情况：'))
-    with open(os.path.join(dataset, '特征频度统计.txt'), encoding='utf-8') as f:
+    with open(os.path.join(config.dataset, '特征频度统计.txt'), encoding='utf-8') as f:
         for line in f.readlines():
             content.append(Graphs.draw_text(line))
 
@@ -2869,7 +2868,7 @@ def pp_export_pdf_v1(num_indicators=475, num_models=21, num_dayindicator=202, in
     # 读取数据
     from scipy.stats import spearmanr
     data = pd.read_csv(os.path.join(
-        dataset, '指标数据添加时间特征.csv'), encoding='utf-8')
+    config.dataset,'指标数据添加时间特征.csv'), encoding='utf-8')
     # 重命名预测列
     data.rename(columns={y: 'y'}, inplace=True)  # 修改
     data['ds'] = pd.to_datetime(data['ds'])  # 修改
@@ -2889,7 +2888,7 @@ def pp_export_pdf_v1(num_indicators=475, num_models=21, num_dayindicator=202, in
     # 删除空列
     correlation_df.drop('Correlation', axis=1, inplace=True)
     correlation_df.dropna(inplace=True)
-    correlation_df.to_csv(os.path.join(dataset, '指标相关性分析.csv'), index=False)
+    correlation_df.to_csv(os.path.join(config.dataset, '指标相关性分析.csv'), index=False)
 
     data = correlation_df['Pearson_Correlation'].values.tolist()
     # 生成 -1 到 1 的 20 个区间
@@ -2907,7 +2906,7 @@ def pp_export_pdf_v1(num_indicators=475, num_models=21, num_dayindicator=202, in
     plt.title('皮尔逊相关系数分布图')
     plt.xlabel('区间')
     plt.ylabel('统计数')
-    plt.savefig(os.path.join(dataset, '皮尔逊相关性系数.png'))
+    plt.savefig(os.path.join(config.dataset, '皮尔逊相关性系数.png'))
     plt.close()
 
     # 设置画布大小
@@ -2924,11 +2923,11 @@ def pp_export_pdf_v1(num_indicators=475, num_models=21, num_dayindicator=202, in
     plt.title('斯皮尔曼相关系数分布图')
     plt.xlabel('区间')
     plt.ylabel('统计数')
-    plt.savefig(os.path.join(dataset, '斯皮尔曼相关性系数.png'))
+    plt.savefig(os.path.join(config.dataset, '斯皮尔曼相关性系数.png'))
     plt.close()
     content.append(Graphs.draw_text(f'指标相关性分析--皮尔逊相关系数：'))
     # 皮尔逊正相关 不相关  负相关 的表格
-    content.append(Graphs.draw_img(os.path.join(dataset, '皮尔逊相关性系数.png')))
+    content.append(Graphs.draw_img(os.path.join(config.dataset, '皮尔逊相关性系数.png')))
     content.append(Graphs.draw_text('''皮尔逊相关系数说明：'''))
     content.append(Graphs.draw_text('''衡量两个特征之间的线性相关性。'''))
     content.append(Graphs.draw_text('''
@@ -2940,7 +2939,7 @@ def pp_export_pdf_v1(num_indicators=475, num_models=21, num_dayindicator=202, in
     content.append(Graphs.draw_text(f'''{top10}'''))
     # 获取特征的近一月值
     feature_data_df = pd.read_csv(os.path.join(
-        dataset, '填充后的特征数据.csv'), parse_dates=['ds']).tail(20)
+    config.dataset,'填充后的特征数据.csv'), parse_dates=['ds']).tail(20)
     feature_df = feature_data_df[['ds', 'y']+top10_columns]
     # feature_df['ds'] = pd.to_datetime(df['ds'], format = '%Y-%m-%d' )
     # 遍历X每一列，和yy画散点图 ，
@@ -2983,9 +2982,9 @@ def pp_export_pdf_v1(num_indicators=475, num_models=21, num_dayindicator=202, in
             # 文件名特殊字符处理
             col = col.replace('*', '-')
             col = col.replace(':', '-')
-            plt.savefig(os.path.join(dataset, f'{col}与价格散点图.png'))
+            plt.savefig(os.path.join(config.dataset, f'{col}与价格散点图.png'))
             content.append(Graphs.draw_img(
-                os.path.join(dataset, f'{col}与价格散点图.png')))
+                os.path.join(config.dataset, f'{col}与价格散点图.png')))
             plt.close()
 
     content.append(Graphs.draw_text(
@@ -3038,15 +3037,15 @@ def pp_export_pdf_v1(num_indicators=475, num_models=21, num_dayindicator=202, in
             # 文件名特殊字符处理
             col = col.replace('*', '-')
             col = col.replace(':', '-')
-            plt.savefig(os.path.join(dataset, f'{col}与价格散点图.png'))
+            plt.savefig(os.path.join(config.dataset, f'{col}与价格散点图.png'))
             content.append(Graphs.draw_img(
-                os.path.join(dataset, f'{col}与价格散点图.png')))
+                os.path.join(config.dataset, f'{col}与价格散点图.png')))
             plt.close()
     content.append(Graphs.draw_text(
         '''相关系数接近0：表示两个变量之间不存在线性关系，即它们的变化不会随着对方的变化而变化。'''))
     content.append(Graphs.draw_text(f'指标相关性分析--斯皮尔曼相关系数：'))
     # 皮尔逊正相关 不相关  负相关 的表格
-    content.append(Graphs.draw_img(os.path.join(dataset, '斯皮尔曼相关性系数.png')))
+    content.append(Graphs.draw_img(os.path.join(config.dataset, '斯皮尔曼相关性系数.png')))
     content.append(Graphs.draw_text(
         '斯皮尔曼相关系数（Spearmans rank correlation coefficient）是一种用于衡量两个变量之间的单调关系（不一定是线性关系）的统计指标。'))
     content.append(Graphs.draw_text('它的计算基于变量的秩次（即变量值的排序位置）而非变量的原始值。'))
@@ -3069,7 +3068,7 @@ def pp_export_pdf_v1(num_indicators=475, num_models=21, num_dayindicator=202, in
         f'预测使用了{num_models}个模型进行训练拟合，通过评估指标MAE从小到大排列，前5个模型的简介如下：'))
 
     # 读取模型简介
-    with open(os.path.join(dataset, 'model_introduction.txt'), 'r', encoding='utf-8') as f:
+    with open(os.path.join(config.dataset, 'model_introduction.txt'), 'r', encoding='utf-8') as f:
         for line in f:
             line_split = line.strip().split('--')
             if line_split[0] in fivemodels_list:
@@ -3079,7 +3078,7 @@ def pp_export_pdf_v1(num_indicators=475, num_models=21, num_dayindicator=202, in
     content.append(Graphs.draw_little_title('模型评估：'))
 
     df = pd.read_csv(os.path.join(
-        dataset, 'model_evaluation.csv'), encoding='utf-8')
+    config.dataset,'model_evaluation.csv'), encoding='utf-8')
     # 判断 df 的数值列转为float
     for col in eval_df.columns:
         if col not in ['模型(Model)']:
@@ -3105,12 +3104,12 @@ def pp_export_pdf_v1(num_indicators=475, num_models=21, num_dayindicator=202, in
         '3. 平均平方误差(MSE)：平均平方误差是衡量预测值与实际值之间误差的一种方法，取值越小，误差越小，预测效果越好。'))
     content.append(Graphs.draw_text('模型拟合：'))
     # 添加图片
-    content.append(Graphs.draw_img(os.path.join(dataset, '预测值与真实值对比图.png')))
+    content.append(Graphs.draw_img(os.path.join(config.dataset, '预测值与真实值对比图.png')))
 
     # 附1，特征列表
     content.append(Graphs.draw_little_title('附1、特征列表：'))
     df_fuyi = pd.read_csv(os.path.join(
-        dataset, '特征频度统计.csv'), encoding='utf-8')
+    config.dataset,'特征频度统计.csv'), encoding='utf-8')
     for col in df_fuyi.columns:
         fuyi = df_fuyi[col]
         fuyi = fuyi.dropna()
@@ -3119,14 +3118,14 @@ def pp_export_pdf_v1(num_indicators=475, num_models=21, num_dayindicator=202, in
             content.append(Graphs.draw_text(f'{i+1}、{fuyi[i]}'))
 
     # 生成pdf文件
-    doc = SimpleDocTemplate(os.path.join(dataset, reportname), pagesize=letter)
-    # doc = SimpleDocTemplate(os.path.join(dataset,'reportname.pdf'), pagesize=letter)
+    doc = SimpleDocTemplate(os.path.join(config.dataset, reportname), pagesize=letter)
+    # doc = SimpleDocTemplate(os.path.join(config.dataset,'reportname.pdf'), pagesize=letter)
     doc.build(content)
     # pdf 上传到数字化信息平台
     # 读取pdf并转为base64
     try:
         if is_update_report:
-            with open(os.path.join(dataset, reportname), 'rb') as f:
+            with open(os.path.join(config.dataset, reportname), 'rb') as f:
                 base64_data = base64.b64encode(f.read()).decode('utf-8')
                 upload_data["data"]["fileBase64"] = base64_data
             upload_data["data"]["fileName"] = reportname
@@ -3144,13 +3143,13 @@ def tansuanli_export_pdf(num_indicators=475, num_models=22, num_dayindicator=202
     content.append(Graphs.draw_title(f'{y}{end_time}预测报告'))
     # 预测结果
     content.append(Graphs.draw_little_title('一、预测结果：'))
-    content.append(Graphs.draw_img(os.path.join(dataset, '历史价格-预测值.png')))
+    content.append(Graphs.draw_img(os.path.join(config.dataset, '历史价格-预测值.png')))
     # 取df中y列为空的行
     from lib.dataread import loadcsv
-    df = loadcsv(os.path.join(dataset, 'predict.csv'))
-    df_true = loadcsv(os.path.join(dataset, '指标数据添加时间特征.csv'))  # 获取预测日期对应的真实值
+    df = loadcsv(os.path.join(config.dataset, 'predict.csv'))
+    df_true = loadcsv(os.path.join(config.dataset, '指标数据添加时间特征.csv'))  # 获取预测日期对应的真实值
     df_true = df_true[['ds', 'y']]
-    eval_df = loadcsv(os.path.join(dataset, 'model_evaluation.csv'))
+    eval_df = loadcsv(os.path.join(config.dataset, 'model_evaluation.csv'))
     # 按评估指标排序，取前五
     fivemodels_list = eval_df['模型(Model)'].values[:5]  # 列表形式，后面当作列名索引使用
     # 取 fivemodels_list 和 ds 列
@@ -3190,7 +3189,7 @@ def tansuanli_export_pdf(num_indicators=475, num_models=22, num_dayindicator=202
     col_width = 500/len(df.columns)
     content.append(Graphs.draw_table(col_width, *data))
     content.append(Graphs.draw_little_title('二、上一预测周期偏差率分析：'))
-    df = loadcsv(os.path.join(dataset, 'testandpredict_groupby.csv'))
+    df = loadcsv(os.path.join(config.dataset, 'testandpredict_groupby.csv'))
     df4 = df.copy()  # 计算偏差率使用
     # 计算模型偏差率
     # 计算各列对于y列的差值百分比
@@ -3226,7 +3225,7 @@ def tansuanli_export_pdf(num_indicators=475, num_models=22, num_dayindicator=202
     content.append(Graphs.draw_little_title('指标情况：'))
     content.append(Graphs.draw_text('  指标频度包括'))
     # 添加频度统计表格
-    pindu_df = loadcsv(os.path.join(dataset, '特征频度统计.csv'))
+    pindu_df = loadcsv(os.path.join(config.dataset, '特征频度统计.csv'))
     pindu_df.fillna('-', inplace=True)
     pindu_df = pindu_df.T
     pindu_df = pindu_df.reset_index()
@@ -3251,8 +3250,8 @@ def tansuanli_export_pdf(num_indicators=475, num_models=22, num_dayindicator=202
     # 特征工程
     # 预测列分析
     content.append(Graphs.draw_text('  电碳价格自相关ACF和偏自相关PACF分析：'))
-    content.append(Graphs.draw_img(os.path.join(dataset, '指标数据自相关图.png')))
-    content.append(Graphs.draw_img(os.path.join(dataset, '指标数据偏自相关图.png')))
+    content.append(Graphs.draw_img(os.path.join(config.dataset, '指标数据自相关图.png')))
+    content.append(Graphs.draw_img(os.path.join(config.dataset, '指标数据偏自相关图.png')))
     content.append(Graphs.draw_text('  解读：'))
     content.append(Graphs.draw_text(
         ' 自相关函数的取值范围为 [-1, 1]。正值表示信号在不同时间点之间具有正相关性，负值表示信号具有负相关性，而 0 表示信号在不同时间点之间不相关。 '))
@@ -3263,7 +3262,7 @@ def tansuanli_export_pdf(num_indicators=475, num_models=22, num_dayindicator=202
     content.append(Graphs.draw_text('  数据特征可视化分析：'))
     # 找出所有后缀为散点图.png的文件
     import glob
-    scatter_files = glob.glob(os.path.join(dataset, '*散点图.png'))
+    scatter_files = glob.glob(os.path.join(config.dataset, '*散点图.png'))
     for file in scatter_files:
         content.append(Graphs.draw_img(file))
     content.append(Graphs.draw_text('  解读：'))
@@ -3273,7 +3272,7 @@ def tansuanli_export_pdf(num_indicators=475, num_models=22, num_dayindicator=202
     # 计算特征相关性
     # 读取数据
     from scipy.stats import spearmanr
-    data = loadcsv(os.path.join(dataset, '指标数据添加时间特征.csv'))
+    data = loadcsv(os.path.join(config.dataset, '指标数据添加时间特征.csv'))
     # 重命名预测列
     data.rename(columns={y: 'y'}, inplace=True)  # 修改
     from lib.tools import dateConvert
@@ -3294,7 +3293,7 @@ def tansuanli_export_pdf(num_indicators=475, num_models=22, num_dayindicator=202
      # 删除空列
     correlation_df.drop('Correlation', axis=1, inplace=True)
     correlation_df.dropna(inplace=True)
-    correlation_df.to_csv(os.path.join(dataset, '指标相关性分析.csv'), index=False)
+    correlation_df.to_csv(os.path.join(config.dataset, '指标相关性分析.csv'), index=False)
     data = correlation_df['Pearson_Correlation'].values.tolist()
     # 生成 -1 到 1 的 20 个区间
     bins = np.linspace(-1, 1, 21)
@@ -3309,7 +3308,7 @@ def tansuanli_export_pdf(num_indicators=475, num_models=22, num_dayindicator=202
     plt.title('皮尔逊相关系数分布图')
     plt.xlabel('区间')
     plt.ylabel('统计数')
-    plt.savefig(os.path.join(dataset, '皮尔逊相关性系数.png'))
+    plt.savefig(os.path.join(config.dataset, '皮尔逊相关性系数.png'))
     plt.close()
     # 设置画布大小
     plt.figure(figsize=(10, 6))
@@ -3323,11 +3322,11 @@ def tansuanli_export_pdf(num_indicators=475, num_models=22, num_dayindicator=202
     plt.title('斯皮尔曼相关系数分布图')
     plt.xlabel('区间')
     plt.ylabel('统计数')
-    plt.savefig(os.path.join(dataset, '斯皮尔曼相关性系数.png'))
+    plt.savefig(os.path.join(config.dataset, '斯皮尔曼相关性系数.png'))
     plt.close()
     content.append(Graphs.draw_text(f'指标相关性分析--皮尔逊相关系数：'))
     # 皮尔逊正相关 不相关  负相关 的表格
-    content.append(Graphs.draw_img(os.path.join(dataset, '皮尔逊相关性系数.png')))
+    content.append(Graphs.draw_img(os.path.join(config.dataset, '皮尔逊相关性系数.png')))
     content.append(Graphs.draw_text('''皮尔逊相关系数说明：'''))
     content.append(Graphs.draw_text('''衡量两个特征之间的线性相关性。'''))
     content.append(Graphs.draw_text('''
@@ -3346,7 +3345,7 @@ def tansuanli_export_pdf(num_indicators=475, num_models=22, num_dayindicator=202
         '''相关系数接近0：表示两个变量之间不存在线性关系，即它们的变化不会随着对方的变化而变化。'''))
     content.append(Graphs.draw_text(f'指标相关性分析--斯皮尔曼相关系数：'))
     # 皮尔逊正相关 不相关  负相关 的表格
-    content.append(Graphs.draw_img(os.path.join(dataset, '斯皮尔曼相关性系数.png')))
+    content.append(Graphs.draw_img(os.path.join(config.dataset, '斯皮尔曼相关性系数.png')))
     content.append(Graphs.draw_text(
         '斯皮尔曼相关系数（Spearmans rank correlation coefficient）是一种用于衡量两个变量之间的单调关系（不一定是线性关系）的统计指标。'))
     content.append(Graphs.draw_text('它的计算基于变量的秩次（即变量值的排序位置）而非变量的原始值。'))
@@ -3368,7 +3367,7 @@ def tansuanli_export_pdf(num_indicators=475, num_models=22, num_dayindicator=202
     content.append(Graphs.draw_text(f'预测使用了{num_models}个模型进行训练拟合，模型的简介如下：'))
 
     # 读取模型简介
-    with open(os.path.join(dataset, 'model_introduction.txt'), 'r', encoding='utf-8') as f:
+    with open(os.path.join(config.dataset, 'model_introduction.txt'), 'r', encoding='utf-8') as f:
         for line in f:
             line_split = line.strip().split('--')
             # if line_split[0] in fivemodels_list:
@@ -3377,7 +3376,7 @@ def tansuanli_export_pdf(num_indicators=475, num_models=22, num_dayindicator=202
 
     content.append(Graphs.draw_little_title('模型评估：'))
     content.append(Graphs.draw_text(f'通过评估指标MAE从小到大排列，前5个模型的评估详情如下：'))
-    df = loadcsv(os.path.join(dataset, 'model_evaluation.csv'))
+    df = loadcsv(os.path.join(config.dataset, 'model_evaluation.csv'))
     # 判断 df 的数值列转为float
     for col in eval_df.columns:
         if col not in ['模型(Model)']:
@@ -3403,7 +3402,7 @@ def tansuanli_export_pdf(num_indicators=475, num_models=22, num_dayindicator=202
         '3. 平均平方误差(MSE)：平均平方误差是衡量预测值与实际值之间误差的一种方法，先计算预测值与真实值之差的平方，然后对这些平方差求平均值。取值越小，误差越小，预测效果越好。'))
     content.append(Graphs.draw_text('模型拟合：'))
     # 添加图片
-    content.append(Graphs.draw_img(os.path.join(dataset, '预测值与真实值对比图.png')))
+    content.append(Graphs.draw_img(os.path.join(config.dataset, '预测值与真实值对比图.png')))
     # 生成pdf文件
-    doc = SimpleDocTemplate(os.path.join(dataset, reportname), pagesize=letter)
+    doc = SimpleDocTemplate(os.path.join(config.dataset, reportname), pagesize=letter)
     doc.build(content)