矩形体周度月度配置更改

config_juxiting.py

@@ -431,15 +431,15 @@ DEFAULT_CONFIG = {
 # 开关
 is_train = True  # 是否训练
 is_debug = False  # 是否调试
-is_eta = False  # 是否使用eta接口
+is_eta = True  # 是否使用eta接口
 is_market = True  # 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
 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 = True  # 是否上传报告
-is_update_warning_data = True  # 是否上传预警数据
+is_update_report = False  # 是否上传报告
+is_update_warning_data = False  # 是否上传预警数据
 is_update_predict_value = True  # 是否上传预测值到市场信息平台
 is_del_corr = 0.6  # 是否删除相关性高的特征,取值为 0-1 ，0 为不删除，0.6 表示删除相关性小于0.6的特征
 is_del_tow_month = True  # 是否删除两个月不更新的特征

config_juxiting_yuedu.py

@@ -375,7 +375,8 @@ query_data_list_item_nos_data = {
     "data": {
         "dateStart": "20200101",
         "dateEnd": "20241231",
-        "dataItemNoList": ["Brentzdj", "Brentzgj"]  # 数据项编码,代表 brent最低价和最高价
+        # 数据项编码,代表 PP期货 价格
+        "dataItemNoList": ["MAIN_CONFT_SETTLE_PRICE"]
     }
 }
 
@@ -442,8 +443,8 @@ DEFAULT_CONFIG = {
 # 开关
 is_train = True  # 是否训练
 is_debug = False  # 是否调试
-is_eta = False  # 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_eta = True  # 是否使用eta接口
+is_market = True  # 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
 is_edbcode = False  # 特征使用edbcoding列表中的
@@ -466,7 +467,7 @@ print("数据库连接成功", host, dbname, dbusername)
 
 # 数据截取日期
 start_year = 2000  # 数据开始年份
-end_time = '2025-07-22'  # 数据截取日期
+end_time = ''  # 数据截取日期
 freq = 'M'  # 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
 is_corr = False  # 特征是否参与滞后领先提升相关系数

config_juxiting_zhoudu.py

@@ -86,6 +86,11 @@ bdwdname = [
     '次周',
     '隔周',
 ]
+# 数据库预测结果表八大维度列名
+price_columns = [
+    'day_price', 'week_price', 'second_week_price', 'next_week_price',
+    'next_month_price', 'next_february_price', 'next_march_price', 'next_april_price'
+]
 modelsindex = [{
     "NHITS": "SELF0000231",
     "Informer": "SELF0000232",

lib/dataread.py

@@ -1101,7 +1101,7 @@ def datachuli_juxiting(df_zhibiaoshuju, df_zhibiaoliebiao, datecol='date', end_t
         df = calculate_kdj(df)
 
     calculate_correlation(df=df)
-    featureAnalysis(df, dataset=dataset, y=y)
+    featureAnalysis(df, dataset=dataset, y='y')
     return df
 
 

lib/tools.py

@@ -782,6 +782,7 @@ def get_model_id_name_dict(global_config):
     sql = f'select model_name,id from {tb} '
     modelsname = global_config['db_mysql'].execute_query(sql)
     model_id_name_dict = {row['id']: row['model_name'] for row in modelsname}
+    global_config['logger'].info(f'模型id-name: {model_id_name_dict}')
     return model_id_name_dict
 
 

main_juxiting_yuedu.py

@@ -488,7 +488,7 @@ def predict_main():
 
     sql_inset_predict(global_config)
 
-    模型报告
+    # 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'聚烯烃PP大模型月度预测--{end_time}.pdf'  # 报告文件名
@@ -497,7 +497,6 @@ def predict_main():
                   reportname=reportname, sqlitedb=sqlitedb),
 
     logger.info('制作报告end')
-    logger.info('模型训练完成')
 
     # 图片报告
     logger.info('图片报告ing')
@@ -529,12 +528,13 @@ def predict_main():
 if __name__ == '__main__':
     # global end_time
     # 遍历2024-11-25 到 2024-12-3 之间的工作日日期
-    # for i_time in pd.date_range('2022-1-1', '2025-3-26', freq='M'):
-    #     try:
-    #         global_config['end_time'] = i_time.strftime('%Y-%m-%d')
-    #         predict_main()
-    #     except Exception as e:
-    #         logger.info(f'预测失败：{e}')
-    #         continue
+    for i_time in pd.date_range('2025-7-28', '2025-7-29', freq='B'):
+        try:
+            global_config['end_time'] = i_time.strftime('%Y-%m-%d')
+            global_config['db_mysql'].connect()
+            predict_main()
+        except Exception as e:
+            logger.info(f'预测失败：{e}')
+            continue
 
-    predict_main()
+    # predict_main()

main_juxiting_zhoudu.py

@@ -3,7 +3,7 @@
 from lib.dataread import *
 from config_juxiting_zhoudu import *
 from lib.tools import SendMail, exception_logger, convert_df_to_pydantic_pp, exception_logger, get_modelsname
-from models.nerulforcastmodels import ex_Model_Juxiting, model_losss_juxiting, pp_export_pdf
+from models.nerulforcastmodels import ex_Model_Juxiting, model_losss_juxiting, pp_bdwd_png, pp_export_pdf
 import datetime
 import torch
 torch.set_float32_matmul_precision("high")
@@ -23,6 +23,8 @@ global_config.update({
     'is_update_report': is_update_report,
     'settings': settings,
     'bdwdname': bdwdname,
+    'columnsrename': columnsrename,
+    'price_columns': price_columns,
 
 
     # 模型参数
@@ -470,6 +472,9 @@ def predict_main():
     #     sqlitedb, end_time=global_config['end_time'], is_fivemodels=global_config['is_fivemodels'])
     # logger.info('训练数据绘图end')
 
+    push_market_value()
+    sql_inset_predict(global_config)
+
     # # # 模型报告
     # logger.info('制作报告ing')
     # title = f'{settings}--{end_time}-预测报告'  # 报告标题
@@ -480,8 +485,10 @@ def predict_main():
 
     # logger.info('制作报告end')
 
-    push_market_value()
-    sql_inset_predict(global_config)
+    # 图片报告
+    logger.info('图片报告ing')
+    pp_bdwd_png(global_config=global_config)
+    logger.info('图片报告end')
 
     # # LSTM 单变量模型
     # ex_Lstm(df,input_seq_len=input_size,output_seq_len=horizon,is_debug=is_debug,dataset=dataset)
@@ -508,16 +515,16 @@ def predict_main():
 if __name__ == '__main__':
     # global end_time
     # 遍历2024-11-25 到 2024-12-3 之间的工作日日期
-    for i_time in pd.date_range('2025-7-18', '2025-7-23', freq='B'):
-        try:
-            global_config['end_time'] = i_time.strftime('%Y-%m-%d')
-            global_config['db_mysql'].connect()
-            predict_main()
-        except Exception as e:
-            logger.info(f'预测失败：{e}')
-            continue
+    # for i_time in pd.date_range('2025-7-18', '2025-7-23', freq='B'):
+    #     try:
+    #         global_config['end_time'] = i_time.strftime('%Y-%m-%d')
+    #         global_config['db_mysql'].connect()
+    #         predict_main()
+    #     except Exception as e:
+    #         logger.info(f'预测失败：{e}')
+    #         continue
 
-    # predict_main()
+    predict_main()
 
     # push_market_value()
     # sql_inset_predict(global_config)

models/nerulforcastmodels.py

@@ -2048,7 +2048,11 @@ def model_losss_juxiting(sqlitedb, end_time, is_fivemodels):
             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)
+    try:
+        names_df['columns'] = names_df.apply(add_rote_column, axis=1)
+    except ValueError as e:
+        print(e)
+    
 
     def add_upper_lower_bound(row):
         print(row['columns'])
@@ -3329,21 +3333,25 @@ def pp_export_pdf(num_indicators=475, num_models=21, num_dayindicator=202, input
     df3['ds'] = df4['ds']
     for col in fivemodels_list:
         df3[col] = round(abs(df4[col] - df4['y']) / df4['y'] * 100, 2)
-    # 找出决定系数前五的偏差率
-    df3 = df3[['ds']+fivemodels_list.tolist()][-inputsize:]
-    # 找出上一预测区间的时间
-    stime = df3['ds'].iloc[0]
-    etime = df3['ds'].iloc[-1]
-    # 添加偏差率表格
-    fivemodels = '、'.join(eval_df['模型(Model)'].values[:5])  # 字符串形式，后面写入字符串使用
-    content.append(Graphs.draw_text(
-        f'预测使用了{num_models}个模型进行训练，使用评估结果MAE前五的模型分别是 {fivemodels} ，模型上一预测区间 {stime} -- {etime}的偏差率(%)分别是：'))
-    # # 添加偏差率表格
-    df3 = df3.T
-    df3 = df3.reset_index()
-    data = df3.values.tolist()
-    col_width = 500/len(df3.columns)
-    content.append(Graphs.draw_table(col_width, *data))
+
+    try:
+        # 找出决定系数前五的偏差率
+        df3 = df3[['ds']+fivemodels_list.tolist()][-inputsize:]
+        # 找出上一预测区间的时间
+        stime = df3['ds'].iloc[0]
+        etime = df3['ds'].iloc[-1]
+        # 添加偏差率表格
+        fivemodels = '、'.join(eval_df['模型(Model)'].values[:5])  # 字符串形式，后面写入字符串使用
+        content.append(Graphs.draw_text(
+            f'预测使用了{num_models}个模型进行训练，使用评估结果MAE前五的模型分别是 {fivemodels} ，模型上一预测区间 {stime} -- {etime}的偏差率(%)分别是：'))
+        # # 添加偏差率表格
+        df3 = df3.T
+        df3 = df3.reset_index()
+        data = df3.values.tolist()
+        col_width = 500/len(df3.columns)
+        content.append(Graphs.draw_table(col_width, *data))
+    except:
+        print('偏差率计算错误，跳过')
 
     content.append(Graphs.draw_little_title('三、预测过程解析：'))
     # 特征、模型、参数配置

up_week_dates.csv

@@ -0,0 +1,17 @@
+ds,NHITS,Informer,LSTM,iTransformer,TSMixer,TSMixerx,PatchTST,RNN,GRU,TCN,BiTCN,DilatedRNN,MLP,DLinear,NLinear,TFT,StemGNN,MLPMultivariate,TiDE,DeepNPTS,y,min_within_quantile,max_within_quantile,min_price,max_price,id,CREAT_DATE,序号,LOW_PRICE,HIGH_PRICE,ACCURACY
+2024-12-09,71.63069,72.14122,70.19943,69.644196,71.80898,70.52471,71.53064,67.38501,70.75815,73.546684,71.92033,70.327484,71.95594,71.18323,71.882935,71.90951,73.94965,72.28691,71.25138,73.34018,72.13999938964844,67.38501,73.94965,67.38501,73.94965,51,2024-12-06,5.0,70.92,72.65,1.0
+2024-12-10,71.14343,71.9462,70.405106,69.48242,71.70601,70.66241,71.57484,67.23587,70.46323,73.37324,71.720894,70.45846,71.88132,72.34705,71.75997,72.41326,73.74943,72.31887,71.47953,72.78831,72.19000244140625,67.23587,73.74943,67.23587,73.74943,52,2024-12-06,4.0,70.73,71.77,1.0
+2024-12-11,71.71588,72.31544,70.175125,69.58213,71.59609,70.91783,71.54794,67.433334,70.518196,73.76477,71.84062,70.746284,72.27111,71.85789,70.77939,72.912704,73.91716,72.42111,71.47695,72.61624,73.5199966430664,67.433334,73.91716,67.433334,73.91716,53,2024-12-06,3.0,72.15,73.75,1.0
+2024-12-12,72.46348,71.87648,70.26041,69.922165,71.65103,70.689384,71.72716,67.54506,70.99872,73.52567,71.78495,70.777115,72.34328,72.756325,70.9607,73.391495,73.944244,72.465836,71.445244,71.69109,73.88999938964844,67.54506,73.944244,67.54506,73.944244,54,2024-12-06,2.0,72.42,74.0,0.9647113924050618
+2024-12-13,72.85073,72.36679,70.489136,69.759766,71.78641,70.69935,71.60861,67.47295,70.81146,73.85618,71.966835,70.923485,72.63866,72.29209,71.11011,73.777534,73.82516,72.58803,71.807915,72.6083,,67.47295,73.85618,67.47295,73.85618,55,2024-12-06,1.0,73.3,74.59,0.43114728682170156
+2024-12-10,71.97169,73.11586,71.41545,70.59598,71.81998,71.541016,72.397606,68.78458,71.30409,73.65467,71.78349,71.03456,71.8188,71.85696,72.014534,72.33824,73.77812,72.2061,71.36985,72.46154,72.19000244140625,68.78458,73.77812,68.78458,73.77812,56,2024-12-09,4.0,70.73,71.77,1.0
+2024-12-11,72.35509,73.087166,71.63927,70.430176,71.82658,71.76624,72.57018,68.59579,71.03884,73.45442,71.75791,71.15297,72.066956,72.28318,72.36303,72.75233,73.594864,72.24322,71.16944,72.39418,73.5199966430664,68.59579,73.594864,68.59579,73.594864,57,2024-12-09,3.0,72.15,73.75,0.9030400000000004
+2024-12-12,73.268654,73.31714,71.39498,70.65145,71.76794,71.6176,73.038315,68.85988,71.12751,73.84763,71.82094,71.41621,72.99199,72.42496,71.474464,73.17588,73.74838,72.31574,71.692444,72.48112,73.88999938964844,68.85988,73.84763,68.85988,73.84763,58,2024-12-09,2.0,72.42,74.0,0.9035632911392374
+2024-12-13,73.11824,73.70929,71.442184,70.72518,71.878975,71.82435,73.13541,68.760376,71.51967,73.62109,71.86448,71.44768,72.952484,72.107254,72.57947,73.61362,73.77317,72.3857,71.873566,72.40313,,68.760376,73.77317,68.760376,73.77317,59,2024-12-09,1.0,73.3,74.59,0.36679844961239827
+2024-12-11,72.372795,72.6546,71.37516,71.41169,71.977135,72.13211,72.774284,69.25488,71.62906,73.70365,72.27147,71.489716,72.77891,71.61932,72.090294,72.166916,73.72794,72.78662,71.960884,72.473854,73.5199966430664,69.25488,73.72794,69.25488,73.72794,61,2024-12-10,3.0,72.15,73.75,0.9862125000000024
+2024-12-12,72.91286,72.80857,71.612946,71.193184,72.05397,72.48625,72.85768,69.06833,71.35641,73.5444,72.19988,71.585815,73.47307,72.22734,72.219765,72.48997,73.544106,72.96339,71.66363,72.122116,73.88999938964844,69.06833,73.5444,69.06833,73.5444,62,2024-12-10,2.0,72.42,74.0,0.7116455696202503
+2024-12-13,73.01643,72.78796,71.3701,71.516174,72.21711,72.4036,73.66599,69.338615,71.467476,73.9247,72.2582,71.84913,73.643265,71.70761,72.41268,72.85244,73.69811,72.91225,71.75731,71.76489,,69.338615,73.9247,69.338615,73.9247,63,2024-12-10,1.0,73.3,74.59,0.4842635658914738
+2024-12-12,73.82699,73.58274,72.81162,73.18572,72.8827,72.958374,74.36039,71.09452,72.673904,74.025635,73.17438,72.72577,73.668365,72.48243,73.224655,74.286606,73.70824,73.668076,72.934685,73.16352,73.88999938964844,71.09452,74.36039,71.09452,74.36039,66,2024-12-11,2.0,72.42,74.0,1.0
+2024-12-13,74.025696,73.275696,73.0588,72.85848,72.719444,73.238945,74.05083,71.060295,72.455666,73.90173,72.966385,72.81345,73.77382,72.488365,73.78919,74.5482,73.52537,73.58226,72.8307,73.26384,,71.060295,74.5482,71.060295,74.5482,67,2024-12-11,1.0,73.3,74.59,0.9675968992247993
+2024-12-13,73.624176,73.15132,73.069374,73.517944,73.56706,74.08185,73.880775,71.83479,73.23418,74.28763,73.372154,73.431366,73.51813,73.90266,73.87494,74.43155,73.691505,73.46715,73.56533,73.50562,,71.83479,74.43155,71.83479,74.43155,71,2024-12-12,1.0,73.3,74.59,0.877170542635658
+2024-12-13,73.624176,73.15132,73.069374,73.517944,73.56706,74.08185,73.880775,71.83479,73.23418,74.28763,73.372154,73.431366,73.51813,73.90266,73.87494,74.43155,73.691505,73.46715,73.56533,73.50562,,71.83479,74.43155,71.83479,74.43155,76,2024-12-13,1.0,73.3,74.59,0.877170542635658