From f3971a94ec4efc9cbe103786c0206566528e69af Mon Sep 17 00:00:00 2001 From: workpc Date: Tue, 5 Aug 2025 11:37:12 +0800 Subject: [PATCH] =?UTF-8?q?=E8=81=9A=E7=83=AF=E7=83=83PP=E6=9C=9F=E8=B4=A7?= =?UTF-8?q?=E9=A2=84=E6=B5=8B?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- config_juxiting.py | 3 + config_juxiting_zhoudu.py | 3 + main_juxiting.py | 70 ++++--- main_juxiting_yuedu.py | 420 +++++++++++++++++++------------------- main_juxiting_zhoudu.py | 58 +++--- 5 files changed, 273 insertions(+), 281 deletions(-) diff --git a/config_juxiting.py b/config_juxiting.py index 9c68abd..4f82c48 100644 --- a/config_juxiting.py +++ b/config_juxiting.py @@ -397,6 +397,9 @@ get_waring_data_value_list_data = { # 八大维度数据项编码 bdwd_items = { 'ciri': 'jxtppbdwdcr', + 'cierri': 'jxtppbdwdcer', + 'cisanri': 'jxtppbdwdcsanr', + 'cisiri': 'jxtppbdwdcsir', 'benzhou': 'jxtppbdwdbz', 'cizhou': 'jxtppbdwdcz', 'gezhou': 'jxtppbdwdgz', diff --git a/config_juxiting_zhoudu.py b/config_juxiting_zhoudu.py index ccac039..9c9204c 100644 --- a/config_juxiting_zhoudu.py +++ b/config_juxiting_zhoudu.py @@ -86,11 +86,14 @@ 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/main_juxiting.py b/main_juxiting.py index b747c34..c0d0268 100644 --- a/main_juxiting.py +++ b/main_juxiting.py @@ -2,7 +2,7 @@ from lib.dataread import * from config_juxiting import * -from lib.tools import SendMail, exception_logger, convert_df_to_pydantic_pp, exception_logger, get_modelsname, plot_pp_predict_result +from lib.tools import SendMail, exception_logger, convert_df_to_pydantic_pp, exception_logger, find_best_models, get_modelsname, plot_pp_predict_result from models.nerulforcastmodels import ex_Model_Juxiting, model_losss_juxiting, pp_export_pdf import datetime import torch @@ -103,49 +103,53 @@ global_config.update({ def push_market_value(): config.logger.info('发送预测结果到市场信息平台') + + current_end_time = global_config['end_time'] + previous_trading_day = (pd.Timestamp(current_end_time) - + pd.tseries.offsets.BusinessDay(1)).strftime('%Y-%m-%d') + # 读取预测数据和模型评估数据 - predict_file_path = os.path.join(config.dataset, 'predict.csv') - model_eval_file_path = os.path.join(config.dataset, 'model_evaluation.csv') - try: - predictdata_df = pd.read_csv(predict_file_path) - top_models_df = pd.read_csv(model_eval_file_path) - except FileNotFoundError as e: - config.logger.error(f"文件未找到: {e}") - return - - predictdata = predictdata_df.copy() - - # 取模型前十 - top_models = top_models_df['模型(Model)'].head(10).tolist() - # 去掉FDBformer - if 'FEDformer' in top_models: - top_models.remove('FEDformer') - - # 计算前十模型的均值 - predictdata_df['top_models_mean'] = predictdata_df[top_models].mean(axis=1) - - # 打印日期和前十模型均值 - print(predictdata_df[['ds', 'top_models_mean']]) - - # 准备要推送的数据 - first_mean = predictdata_df['top_models_mean'].iloc[0] - last_mean = predictdata_df['top_models_mean'].iloc[-1] - # 保留两位小数 - first_mean = round(first_mean, 2) - last_mean = round(last_mean, 2) + best_bdwd_price = find_best_models( + date=previous_trading_day, global_config=global_config) + + # 获取本周最佳模型的五日预测价格 + five_days_predict_price = pd.read_csv('juxitingdataset/predict.csv') + week_price_modelname = best_bdwd_price['week_price']['model_name'] + five_days_predict_price = five_days_predict_price[['ds',week_price_modelname]] + five_days_predict_price['ds'] = pd.to_datetime(five_days_predict_price['ds']) + five_days_predict_price.rename(columns={week_price_modelname:'predictresult'},inplace=True) + # 设置索引 次日 次二日 次三日 次四日 次五日 + index_labels = ["次日", "次二日", "次三日", "次四日", "次五日"] + five_days_predict_price.index = index_labels + global_config['logger'].info(f"best_bdwd_price: {best_bdwd_price}") predictdata = [ { "dataItemNo": global_config['bdwd_items']['ciri'], "dataDate": global_config['end_time'].replace('-', ''), "dataStatus": "add", - "dataValue": first_mean + "dataValue": five_days_predict_price.loc['次日','predictresult'].round(2) + },{ + "dataItemNo": global_config['bdwd_items']['cierri'], + "dataDate": global_config['end_time'].replace('-', ''), + "dataStatus": "add", + "dataValue": five_days_predict_price.loc['次二日','predictresult'].round(2) + },{ + "dataItemNo": global_config['bdwd_items']['cisanri'], + "dataDate": global_config['end_time'].replace('-', ''), + "dataStatus": "add", + "dataValue": five_days_predict_price.loc['次三日','predictresult'].round(2) + },{ + "dataItemNo": global_config['bdwd_items']['cisiri'], + "dataDate": global_config['end_time'].replace('-', ''), + "dataStatus": "add", + "dataValue": five_days_predict_price.loc['次四日','predictresult'].round(2) }, { "dataItemNo": global_config['bdwd_items']['benzhou'], "dataDate": global_config['end_time'].replace('-', ''), "dataStatus": "add", - "dataValue": last_mean + "dataValue": five_days_predict_price.loc['次五日','predictresult'].round(2) } ] @@ -553,7 +557,7 @@ if __name__ == '__main__': # except Exception as e: # logger.info(f'预测失败：{e}') # continue - + global_config['end_time'] = '2025-08-04' predict_main() # push_market_value() diff --git a/main_juxiting_yuedu.py b/main_juxiting_yuedu.py index 3562524..8d3f1b7 100644 --- a/main_juxiting_yuedu.py +++ b/main_juxiting_yuedu.py @@ -2,7 +2,7 @@ from lib.dataread import * from config_juxiting_yuedu import * -from lib.tools import SendMail, convert_df_to_pydantic_pp, exception_logger, get_modelsname +from lib.tools import SendMail, convert_df_to_pydantic_pp, exception_logger, find_best_models, get_modelsname from models.nerulforcastmodels import ex_Model, model_losss_juxiting, pp_bdwd_png, pp_export_pdf import datetime import torch @@ -93,35 +93,28 @@ global_config.update({ def push_market_value(): logger.info('发送预测结果到市场信息平台') + current_end_time = global_config['end_time'] + previous_trading_day = (pd.Timestamp(current_end_time) - + pd.tseries.offsets.BusinessDay(1)).strftime('%Y-%m-%d') + # 读取预测数据和模型评估数据 - predict_file_path = os.path.join(config.dataset, 'predict.csv') - model_eval_file_path = os.path.join(config.dataset, 'model_evaluation.csv') - try: - predictdata_df = pd.read_csv(predict_file_path) - top_models_df = pd.read_csv(model_eval_file_path) - except FileNotFoundError as e: - logger.error(f"文件未找到: {e}") - return - - predictdata = predictdata_df.copy() - - # 取模型前十 - top_models = top_models_df['模型(Model)'].head(10).tolist() - # 去掉FDBformer - if 'FEDformer' in top_models: - top_models.remove('FEDformer') - # 计算前十模型的均值 - predictdata_df['top_models_mean'] = predictdata_df[top_models].mean(axis=1) - - # 打印日期和前十模型均值 - print(predictdata_df[['ds', 'top_models_mean']]) + best_bdwd_price = find_best_models( + date=previous_trading_day, global_config=global_config) + + # 获取本月最佳模型的预测价格 + four_month_predict_price = pd.read_csv(os.path.join(global_config['dataset'], 'predict.csv')) + four_month_predict_price['ds'] = pd.to_datetime(four_month_predict_price['ds']) + # 设置索引 次月 次二月 次三月 次四月 + index_labels = ["次月", "次二月", "次三月", "次四月"] + four_month_predict_price.index = index_labels + global_config['logger'].info(f"best_bdwd_price: {best_bdwd_price}") # 准备要推送的数据 - ciyue_mean = predictdata_df['top_models_mean'].iloc[0] - cieryue_mean = predictdata_df['top_models_mean'].iloc[1] - cisanyue_mean = predictdata_df['top_models_mean'].iloc[2] - cisieryue_mean = predictdata_df['top_models_mean'].iloc[3] - # 保留两位小数 + ciyue_mean = four_month_predict_price[best_bdwd_price['next_month_price']['model_name']].iloc[0] + cieryue_mean = four_month_predict_price[best_bdwd_price['next_february_price']['model_name']].iloc[1] + cisanyue_mean = four_month_predict_price[best_bdwd_price['next_march_price']['model_name']].iloc[2] + cisieryue_mean = four_month_predict_price[best_bdwd_price['next_april_price']['model_name']].iloc[3] + # # 保留两位小数 ciyue_mean = round(ciyue_mean, 2) cieryue_mean = round(cieryue_mean, 2) cisanyue_mean = round(cisanyue_mean, 2) @@ -292,211 +285,211 @@ def predict_main(): 返回: None """ - end_time = global_config['end_time'] - signature = BinanceAPI(APPID, SECRET) - etadata = EtaReader(signature=signature, - classifylisturl=global_config['classifylisturl'], - classifyidlisturl=global_config['classifyidlisturl'], - edbcodedataurl=global_config['edbcodedataurl'], - edbcodelist=global_config['edbcodelist'], - edbdatapushurl=global_config['edbdatapushurl'], - edbdeleteurl=global_config['edbdeleteurl'], - edbbusinessurl=global_config['edbbusinessurl'], - classifyId=global_config['ClassifyId'], - ) - # 获取数据 - if is_eta: - logger.info('从eta获取数据...') + # end_time = global_config['end_time'] + # signature = BinanceAPI(APPID, SECRET) + # etadata = EtaReader(signature=signature, + # classifylisturl=global_config['classifylisturl'], + # classifyidlisturl=global_config['classifyidlisturl'], + # edbcodedataurl=global_config['edbcodedataurl'], + # edbcodelist=global_config['edbcodelist'], + # edbdatapushurl=global_config['edbdatapushurl'], + # edbdeleteurl=global_config['edbdeleteurl'], + # edbbusinessurl=global_config['edbbusinessurl'], + # classifyId=global_config['ClassifyId'], + # ) + # # 获取数据 + # if is_eta: + # logger.info('从eta获取数据...') - df_zhibiaoshuju, df_zhibiaoliebiao = etadata.get_eta_api_pp_data( - data_set=data_set, dataset=dataset) # 原始数据，未处理 + # df_zhibiaoshuju, df_zhibiaoliebiao = etadata.get_eta_api_pp_data( + # data_set=data_set, dataset=dataset) # 原始数据，未处理 - if is_market: - logger.info('从市场信息平台获取数据...') - try: - # 如果是测试环境，最高价最低价取excel文档 - if server_host == '192.168.100.53': - logger.info('从excel文档获取最高价最低价') - df_zhibiaoshuju = get_high_low_data(df_zhibiaoshuju) - else: - logger.info('从市场信息平台获取数据') - df_zhibiaoshuju = get_market_data( - end_time, df_zhibiaoshuju) + # if is_market: + # logger.info('从市场信息平台获取数据...') + # try: + # # 如果是测试环境，最高价最低价取excel文档 + # if server_host == '192.168.100.53': + # logger.info('从excel文档获取最高价最低价') + # df_zhibiaoshuju = get_high_low_data(df_zhibiaoshuju) + # else: + # logger.info('从市场信息平台获取数据') + # df_zhibiaoshuju = get_market_data( + # end_time, df_zhibiaoshuju) - except: - logger.info('最高最低价拼接失败') + # except: + # logger.info('最高最低价拼接失败') - # 保存到xlsx文件的sheet表 - with pd.ExcelWriter(os.path.join(dataset, data_set)) as file: - df_zhibiaoshuju.to_excel(file, sheet_name='指标数据', index=False) - df_zhibiaoliebiao.to_excel(file, sheet_name='指标列表', index=False) + # # 保存到xlsx文件的sheet表 + # with pd.ExcelWriter(os.path.join(dataset, data_set)) as file: + # df_zhibiaoshuju.to_excel(file, sheet_name='指标数据', index=False) + # df_zhibiaoliebiao.to_excel(file, sheet_name='指标列表', index=False) - # 数据处理 - df = datachuli_juxiting(df_zhibiaoshuju, df_zhibiaoliebiao, y=global_config['y'], dataset=dataset, add_kdj=add_kdj, is_timefurture=is_timefurture, - end_time=end_time) + # # 数据处理 + # df = datachuli_juxiting(df_zhibiaoshuju, df_zhibiaoliebiao, y=global_config['y'], dataset=dataset, add_kdj=add_kdj, is_timefurture=is_timefurture, + # end_time=end_time) - else: - # 读取数据 - logger.info('读取本地数据：' + os.path.join(dataset, data_set)) - df, df_zhibiaoliebiao = getdata_zhoudu_juxiting(filename=os.path.join(dataset, data_set), y=y, dataset=dataset, add_kdj=add_kdj, - is_timefurture=is_timefurture, end_time=end_time) # 原始数据，未处理 + # else: + # # 读取数据 + # logger.info('读取本地数据：' + os.path.join(dataset, data_set)) + # df, df_zhibiaoliebiao = getdata_zhoudu_juxiting(filename=os.path.join(dataset, data_set), y=y, dataset=dataset, add_kdj=add_kdj, + # is_timefurture=is_timefurture, end_time=end_time) # 原始数据，未处理 - # 更改预测列名称 - df.rename(columns={y: 'y'}, inplace=True) + # # 更改预测列名称 + # df.rename(columns={y: 'y'}, inplace=True) - if is_edbnamelist: - df = df[edbnamelist] - df.to_csv(os.path.join(dataset, '指标数据.csv'), index=False) - # 保存最新日期的y值到数据库 - # 取第一行数据存储到数据库中 - first_row = df[['ds', 'y']].tail(1) - # 判断y的类型是否为float - if not isinstance(first_row['y'].values[0], float): - logger.info(f'{end_time}预测目标数据为空，跳过') - return None + # if is_edbnamelist: + # df = df[edbnamelist] + # df.to_csv(os.path.join(dataset, '指标数据.csv'), index=False) + # # 保存最新日期的y值到数据库 + # # 取第一行数据存储到数据库中 + # first_row = df[['ds', 'y']].tail(1) + # # 判断y的类型是否为float + # if not isinstance(first_row['y'].values[0], float): + # logger.info(f'{end_time}预测目标数据为空，跳过') + # return None - # 将最新真实值保存到数据库 - if not sqlitedb.check_table_exists('trueandpredict'): - first_row.to_sql('trueandpredict', sqlitedb.connection, index=False) - else: - for row in first_row.itertuples(index=False): - row_dict = row._asdict() - config.logger.info(f'要保存的真实值：{row_dict}') - # 判断ds是否为字符串类型,如果不是则转换为字符串类型 - if isinstance(row_dict['ds'], (pd.Timestamp, datetime.datetime)): - row_dict['ds'] = row_dict['ds'].strftime('%Y-%m-%d') - elif not isinstance(row_dict['ds'], str): - try: - row_dict['ds'] = pd.to_datetime( - row_dict['ds']).strftime('%Y-%m-%d') - except: - logger.warning(f"无法解析的时间格式: {row_dict['ds']}") - # row_dict['ds'] = row_dict['ds'].strftime('%Y-%m-%d') - # 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()]) - sqlitedb.update_data( - 'trueandpredict', set_clause, where_condition=f"ds = '{row.ds}'") - continue - sqlitedb.insert_data('trueandpredict', tuple( - row_dict.values()), columns=row_dict.keys()) + # # 将最新真实值保存到数据库 + # if not sqlitedb.check_table_exists('trueandpredict'): + # first_row.to_sql('trueandpredict', sqlitedb.connection, index=False) + # else: + # for row in first_row.itertuples(index=False): + # row_dict = row._asdict() + # config.logger.info(f'要保存的真实值：{row_dict}') + # # 判断ds是否为字符串类型,如果不是则转换为字符串类型 + # if isinstance(row_dict['ds'], (pd.Timestamp, datetime.datetime)): + # row_dict['ds'] = row_dict['ds'].strftime('%Y-%m-%d') + # elif not isinstance(row_dict['ds'], str): + # try: + # row_dict['ds'] = pd.to_datetime( + # row_dict['ds']).strftime('%Y-%m-%d') + # except: + # logger.warning(f"无法解析的时间格式: {row_dict['ds']}") + # # row_dict['ds'] = row_dict['ds'].strftime('%Y-%m-%d') + # # 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()]) + # sqlitedb.update_data( + # 'trueandpredict', set_clause, where_condition=f"ds = '{row.ds}'") + # continue + # sqlitedb.insert_data('trueandpredict', tuple( + # row_dict.values()), columns=row_dict.keys()) - # 更新accuracy表的y值 - if not sqlitedb.check_table_exists('accuracy'): - pass - else: - update_y = sqlitedb.select_data( - 'accuracy', where_condition="y is null") - if len(update_y) > 0: - logger.info('更新accuracy表的y值') - # 找到update_y 中ds且df中的y的行 - update_y = update_y[update_y['ds'] <= end_time] - logger.info(f'要更新y的信息：{update_y}') - # try: - for row in update_y.itertuples(index=False): - try: - row_dict = row._asdict() - yy = df[df['ds'] == row_dict['ds']]['y'].values[0] - LOW = df[df['ds'] == row_dict['ds']]['Brentzdj'].values[0] - HIGH = df[df['ds'] == row_dict['ds']]['Brentzgj'].values[0] - sqlitedb.update_data( - 'accuracy', f"y = {yy},LOW_PRICE = {LOW},HIGH_PRICE = {HIGH}", where_condition=f"ds = '{row_dict['ds']}'") - except: - logger.info(f'更新accuracy表的y值失败：{row_dict}') - # except Exception as e: - # logger.info(f'更新accuracy表的y值失败：{e}') + # # 更新accuracy表的y值 + # if not sqlitedb.check_table_exists('accuracy'): + # pass + # else: + # update_y = sqlitedb.select_data( + # 'accuracy', where_condition="y is null") + # if len(update_y) > 0: + # logger.info('更新accuracy表的y值') + # # 找到update_y 中ds且df中的y的行 + # update_y = update_y[update_y['ds'] <= end_time] + # logger.info(f'要更新y的信息：{update_y}') + # # try: + # for row in update_y.itertuples(index=False): + # try: + # row_dict = row._asdict() + # yy = df[df['ds'] == row_dict['ds']]['y'].values[0] + # LOW = df[df['ds'] == row_dict['ds']]['Brentzdj'].values[0] + # HIGH = df[df['ds'] == row_dict['ds']]['Brentzgj'].values[0] + # sqlitedb.update_data( + # 'accuracy', f"y = {yy},LOW_PRICE = {LOW},HIGH_PRICE = {HIGH}", where_condition=f"ds = '{row_dict['ds']}'") + # except: + # logger.info(f'更新accuracy表的y值失败：{row_dict}') + # # except Exception as e: + # # logger.info(f'更新accuracy表的y值失败：{e}') - # 判断当前日期是不是周一 - 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60天预测残差最低的模型名称 - model_results = sqlitedb.select_data( - 'trueandpredict', order_by="ds DESC", limit="60") - # 删除空值率为90%以上的列 - if len(model_results) > 10: - model_results = model_results.dropna( - thresh=len(model_results)*0.1, axis=1) - # 删除空行 - model_results = model_results.dropna() - modelnames = model_results.columns.to_list()[2:-1] - for col in model_results[modelnames].select_dtypes(include=['object']).columns: - model_results[col] = model_results[col].astype(np.float32) - # 计算每个预测值与真实值之间的偏差率 - for model in modelnames: - model_results[f'{model}_abs_error_rate'] = abs( - model_results['y'] - model_results[model]) / model_results['y'] - # 获取每行对应的最小偏差率值 - min_abs_error_rate_values = model_results.apply( - lambda row: row[[f'{model}_abs_error_rate' for model in modelnames]].min(), axis=1) - # 获取每行对应的最小偏差率值对应的列名 - min_abs_error_rate_column_name = model_results.apply( - lambda row: row[[f'{model}_abs_error_rate' for model in modelnames]].idxmin(), axis=1) - # 将列名索引转换为列名 - min_abs_error_rate_column_name = min_abs_error_rate_column_name.map( - lambda x: x.split('_')[0]) - # 取出现次数最多的模型名称 - most_common_model = min_abs_error_rate_column_name.value_counts().idxmax() - logger.info(f"最近60天预测残差最低的模型名称：{most_common_model}") - # 保存结果到数据库 - if not sqlitedb.check_table_exists('most_model'): - sqlitedb.create_table( - 'most_model', columns="ds datetime, most_common_model TEXT") - sqlitedb.insert_data('most_model', (datetime.datetime.now().strftime( - '%Y-%m-%d %H:%M:%S'), most_common_model,), columns=('ds', 'most_common_model',)) + # # 判断当前日期是不是周一 + # 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60天预测残差最低的模型名称 + # model_results = sqlitedb.select_data( + # 'trueandpredict', order_by="ds DESC", limit="60") + # # 删除空值率为90%以上的列 + # if len(model_results) > 10: + # model_results = model_results.dropna( + # thresh=len(model_results)*0.1, axis=1) + # # 删除空行 + # model_results = model_results.dropna() + # modelnames = model_results.columns.to_list()[2:-1] + # for col in model_results[modelnames].select_dtypes(include=['object']).columns: + # model_results[col] = model_results[col].astype(np.float32) + # # 计算每个预测值与真实值之间的偏差率 + # for model in modelnames: + # model_results[f'{model}_abs_error_rate'] = abs( + # model_results['y'] - model_results[model]) / model_results['y'] + # # 获取每行对应的最小偏差率值 + # min_abs_error_rate_values = model_results.apply( + # lambda row: row[[f'{model}_abs_error_rate' for model in modelnames]].min(), axis=1) + # # 获取每行对应的最小偏差率值对应的列名 + # min_abs_error_rate_column_name = model_results.apply( + # lambda row: row[[f'{model}_abs_error_rate' for model in modelnames]].idxmin(), axis=1) + # # 将列名索引转换为列名 + # min_abs_error_rate_column_name = min_abs_error_rate_column_name.map( + # lambda x: x.split('_')[0]) + # # 取出现次数最多的模型名称 + # most_common_model = min_abs_error_rate_column_name.value_counts().idxmax() + # logger.info(f"最近60天预测残差最低的模型名称：{most_common_model}") + # # 保存结果到数据库 + # if not sqlitedb.check_table_exists('most_model'): + # sqlitedb.create_table( + # 'most_model', columns="ds datetime, most_common_model TEXT") + # sqlitedb.insert_data('most_model', (datetime.datetime.now().strftime( + # '%Y-%m-%d %H:%M:%S'), most_common_model,), columns=('ds', 'most_common_model',)) - if is_corr: - df = corr_feature(df=df) + # if is_corr: + # df = corr_feature(df=df) - df1 = df.copy() # 备份一下，后面特征筛选完之后加入ds y 列用 - logger.info(f"开始训练模型...") - row, col = df.shape + # df1 = df.copy() # 备份一下，后面特征筛选完之后加入ds y 列用 + # logger.info(f"开始训练模型...") + # row, col = df.shape - now = datetime.datetime.now().strftime('%Y%m%d%H%M%S') - ex_Model(df, - horizon=global_config['horizon'], - input_size=global_config['input_size'], - train_steps=global_config['train_steps'], - val_check_steps=global_config['val_check_steps'], - early_stop_patience_steps=global_config['early_stop_patience_steps'], - is_debug=global_config['is_debug'], - dataset=global_config['dataset'], - is_train=global_config['is_train'], - is_fivemodels=global_config['is_fivemodels'], - val_size=global_config['val_size'], - test_size=global_config['test_size'], - settings=global_config['settings'], - now=now, - etadata=etadata, - modelsindex=global_config['modelsindex'], - data=data, - is_eta=global_config['is_eta'], - end_time=global_config['end_time'], - ) + # now = datetime.datetime.now().strftime('%Y%m%d%H%M%S') + # ex_Model(df, + # horizon=global_config['horizon'], + # input_size=global_config['input_size'], + # train_steps=global_config['train_steps'], + # val_check_steps=global_config['val_check_steps'], + # early_stop_patience_steps=global_config['early_stop_patience_steps'], + # is_debug=global_config['is_debug'], + # dataset=global_config['dataset'], + # is_train=global_config['is_train'], + # is_fivemodels=global_config['is_fivemodels'], + # val_size=global_config['val_size'], + # test_size=global_config['test_size'], + # settings=global_config['settings'], + # now=now, + # etadata=etadata, + # modelsindex=global_config['modelsindex'], + # data=data, + # is_eta=global_config['is_eta'], + # end_time=global_config['end_time'], + # ) - logger.info('模型训练完成') + # 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=global_config['end_time'], is_fivemodels=global_config['is_fivemodels']) - logger.info('训练数据绘图end') + # logger.info('训练数据绘图ing') + # 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') - push_market_value() + # push_market_value() - sql_inset_predict(global_config) + # 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 - reportname = f'聚烯烃PP大模型月度预测--{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), + # # 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 + # 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), - logger.info('制作报告end') + # logger.info('制作报告end') # 图片报告 logger.info('图片报告ing') @@ -537,7 +530,8 @@ if __name__ == '__main__': # logger.info(f'预测失败：{e}') # continue - predict_main() + predict_main() + # push_market_value() # 图片报告 # global_config['end_time'] = '2025-07-31' diff --git a/main_juxiting_zhoudu.py b/main_juxiting_zhoudu.py index 16f5c17..2714533 100644 --- a/main_juxiting_zhoudu.py +++ b/main_juxiting_zhoudu.py @@ -2,8 +2,8 @@ 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_bdwd_png, pp_export_pdf +from lib.tools import SendMail, exception_logger, convert_df_to_pydantic_pp, exception_logger, find_best_models, get_modelsname +from models.nerulforcastmodels import ex_Model_Juxiting, model_losss_juxiting, pp_export_pdf import datetime import torch torch.set_float32_matmul_precision("high") @@ -23,7 +23,6 @@ global_config.update({ 'is_update_report': is_update_report, 'settings': settings, 'bdwdname': bdwdname, - 'columnsrename': columnsrename, 'price_columns': price_columns, @@ -102,32 +101,26 @@ global_config.update({ def push_market_value(): config.logger.info('发送预测结果到市场信息平台') + + current_end_time = global_config['end_time'] + previous_trading_day = (pd.Timestamp(current_end_time) - + pd.tseries.offsets.BusinessDay(1)).strftime('%Y-%m-%d') + # 读取预测数据和模型评估数据 - predict_file_path = os.path.join(config.dataset, 'predict.csv') - model_eval_file_path = os.path.join(config.dataset, 'model_evaluation.csv') - try: - predictdata_df = pd.read_csv(predict_file_path) - top_models_df = pd.read_csv(model_eval_file_path) - except FileNotFoundError as e: - config.logger.error(f"文件未找到: {e}") - return - - predictdata = predictdata_df.copy() - - # 取模型前十 - top_models = top_models_df['模型(Model)'].head(10).tolist() - # 去掉FDBformer - if 'FEDformer' in top_models: - top_models.remove('FEDformer') - # 计算前十模型的均值 - predictdata_df['top_models_mean'] = predictdata_df[top_models].mean(axis=1) - - # 打印日期和前十模型均值 - print(predictdata_df[['ds', 'top_models_mean']]) + best_bdwd_price = find_best_models( + date=previous_trading_day, global_config=global_config) + + # 获取次周，隔周 最佳模型的预测价格 + weeks_predict_price = pd.read_csv(os.path.join(global_config['dataset'], 'predict.csv')) + weeks_predict_price['ds'] = pd.to_datetime(weeks_predict_price['ds']) + # 设置索引 次周 隔周 + index_labels = ["次周", "隔周"] + weeks_predict_price.index = index_labels + global_config['logger'].info(f"best_bdwd_price: {best_bdwd_price}") # 准备要推送的数据 - first_mean = predictdata_df['top_models_mean'].iloc[0] - last_mean = predictdata_df['top_models_mean'].iloc[-1] + first_mean = weeks_predict_price[best_bdwd_price['second_week_price']['model_name']].iloc[0] + last_mean = weeks_predict_price[best_bdwd_price['next_week_price']['model_name']].iloc[-1] # 保留两位小数 first_mean = round(first_mean, 2) last_mean = round(last_mean, 2) @@ -159,7 +152,7 @@ def push_market_value(): def sql_inset_predict(global_config): df = pd.read_csv(os.path.join(config.dataset, 'predict.csv')) df['created_dt'] = pd.to_datetime(df['created_dt']) - df['ds'] = pd.to_datetime(df['ds']) + df['ds'] = pd.to_datetime(df['ds']) # 获取次周预测结果 second_week_price_df = df[df['ds'] == df['ds'].min()] # 获取隔周周预测结果 @@ -472,9 +465,6 @@ 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 @@ -485,10 +475,8 @@ def predict_main(): # logger.info('制作报告end') - # 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') + push_market_value() + sql_inset_predict(global_config) # # LSTM 单变量模型 # ex_Lstm(df,input_seq_len=input_size,output_seq_len=horizon,is_debug=is_debug,dataset=dataset) @@ -515,7 +503,7 @@ 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'): + # for i_time in pd.date_range('2025-7-28', '2025-7-28', freq='B'): # try: # global_config['end_time'] = i_time.strftime('%Y-%m-%d') # global_config['db_mysql'].connect()