diff --git a/config_juxiting.py b/config_juxiting.py index f84c822..9c68abd 100644 --- a/config_juxiting.py +++ b/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 diff --git a/config_juxiting_yuedu.py b/config_juxiting_yuedu.py index 9900153..1be7581 100644 --- a/config_juxiting_yuedu.py +++ b/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 diff --git a/config_juxiting_zhoudu.py b/config_juxiting_zhoudu.py index b7d58cb..ccac039 100644 --- a/config_juxiting_zhoudu.py +++ b/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", diff --git a/lib/dataread.py b/lib/dataread.py index 6253b03..a76247a 100644 --- a/lib/dataread.py +++ b/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 diff --git a/lib/tools.py b/lib/tools.py index 24f5e64..0188f49 100644 --- a/lib/tools.py +++ b/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 diff --git a/main_juxiting_yuedu.py b/main_juxiting_yuedu.py index c4c2edf..bcad998 100644 --- a/main_juxiting_yuedu.py +++ b/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() diff --git a/main_juxiting_zhoudu.py b/main_juxiting_zhoudu.py index 95930c3..16f5c17 100644 --- a/main_juxiting_zhoudu.py +++ b/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) diff --git a/models/nerulforcastmodels.py b/models/nerulforcastmodels.py index b803cb4..f613641 100644 --- a/models/nerulforcastmodels.py +++ b/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 diff --git a/up_week_dates.csv b/up_week_dates.csv new file mode 100644 index 0000000..919e1c3 --- /dev/null +++ b/up_week_dates.csv @@ -0,0 +1,17 @@ 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