From 7cf3cda87a412c72c33ea74ce1ad17b1cf7f9497 Mon Sep 17 00:00:00 2001 From: jingboyitiji Date: Thu, 20 Mar 2025 16:29:25 +0800 Subject: [PATCH] =?UTF-8?q?=E7=9F=B3=E6=B2=B9=E7=84=A6=E6=97=A5=E5=BA=A6?= =?UTF-8?q?=E9=A2=84=E6=B5=8B=E8=B0=83=E8=AF=95?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- main_shiyoujiao_lvyong.py | 420 +++++++++++++++++------------------ models/nerulforcastmodels.py | 333 ++++++++++++++++++++++++++- 2 files changed, 533 insertions(+), 220 deletions(-) diff --git a/main_shiyoujiao_lvyong.py b/main_shiyoujiao_lvyong.py index 63bb087..103fa41 100644 --- a/main_shiyoujiao_lvyong.py +++ b/main_shiyoujiao_lvyong.py @@ -3,7 +3,7 @@ from lib.dataread import * from config_shiyoujiao_lvyong import * from lib.tools import SendMail, exception_logger -from models.nerulforcastmodels import ex_Model, model_losss, model_losss_juxiting, brent_export_pdf, tansuanli_export_pdf, pp_export_pdf, model_losss_juxiting +from models.nerulforcastmodels import model_losss, shiyoujiao_lvyong_export_pdf import datetime import torch torch.set_float32_matmul_precision("high") @@ -173,228 +173,228 @@ def predict_main(): 返回: None """ - end_time = global_config['end_time'] - # 获取数据 - if is_eta: - logger.info('从eta获取数据...') - 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'], - ) - df_zhibiaoshuju, df_zhibiaoliebiao = etadata.get_eta_api_shiyoujiao_lvyong_data( - data_set=data_set, dataset=dataset) # 原始数据，未处理 + # end_time = global_config['end_time'] + # # 获取数据 + # if is_eta: + # logger.info('从eta获取数据...') + # 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'], + # ) + # df_zhibiaoshuju, df_zhibiaoliebiao = etadata.get_eta_api_shiyoujiao_lvyong_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(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(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(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(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',)) - try: - if is_weekday: - # if True: - logger.info('今天是周一，发送特征预警') - # 上传预警信息到数据库 - warning_data_df = df_zhibiaoliebiao.copy() - warning_data_df = warning_data_df[warning_data_df['停更周期'] > 3][[ - '指标名称', '指标id', '频度', '更新周期', '指标来源', '最后更新时间', '停更周期']] - # 重命名列名 - warning_data_df = warning_data_df.rename(columns={'指标名称': 'INDICATOR_NAME', '指标id': 'INDICATOR_ID', '频度': 'FREQUENCY', - '更新周期': 'UPDATE_FREQUENCY', '指标来源': 'DATA_SOURCE', '最后更新时间': 'LAST_UPDATE_DATE', '停更周期': 'UPDATE_SUSPENSION_CYCLE'}) - from sqlalchemy import create_engine - import urllib - global password - if '@' in password: - password = urllib.parse.quote_plus(password) + # try: + # if is_weekday: + # # if True: + # logger.info('今天是周一，发送特征预警') + # # 上传预警信息到数据库 + # warning_data_df = df_zhibiaoliebiao.copy() + # warning_data_df = warning_data_df[warning_data_df['停更周期'] > 3][[ + # '指标名称', '指标id', '频度', '更新周期', '指标来源', '最后更新时间', '停更周期']] + # # 重命名列名 + # warning_data_df = warning_data_df.rename(columns={'指标名称': 'INDICATOR_NAME', '指标id': 'INDICATOR_ID', '频度': 'FREQUENCY', + # '更新周期': 'UPDATE_FREQUENCY', '指标来源': 'DATA_SOURCE', '最后更新时间': 'LAST_UPDATE_DATE', '停更周期': 'UPDATE_SUSPENSION_CYCLE'}) + # from sqlalchemy import create_engine + # import urllib + # global password + # if '@' in password: + # password = urllib.parse.quote_plus(password) - engine = create_engine( - f'mysql+pymysql://{dbusername}:{password}@{host}:{port}/{dbname}') - warning_data_df['WARNING_DATE'] = datetime.date.today().strftime( - "%Y-%m-%d %H:%M:%S") - warning_data_df['TENANT_CODE'] = 'T0004' - # 插入数据之前查询表数据然后新增id列 - existing_data = pd.read_sql(f"SELECT * FROM {table_name}", engine) - if not existing_data.empty: - max_id = existing_data['ID'].astype(int).max() - warning_data_df['ID'] = range( - max_id + 1, max_id + 1 + len(warning_data_df)) - else: - warning_data_df['ID'] = range(1, 1 + len(warning_data_df)) - warning_data_df.to_sql( - table_name, con=engine, if_exists='append', index=False) - if is_update_warning_data: - upload_warning_info(len(warning_data_df)) - except: - logger.info('上传预警信息到数据库失败') + # engine = create_engine( + # f'mysql+pymysql://{dbusername}:{password}@{host}:{port}/{dbname}') + # warning_data_df['WARNING_DATE'] = datetime.date.today().strftime( + # "%Y-%m-%d %H:%M:%S") + # warning_data_df['TENANT_CODE'] = 'T0004' + # # 插入数据之前查询表数据然后新增id列 + # existing_data = pd.read_sql(f"SELECT * FROM {table_name}", engine) + # if not existing_data.empty: + # max_id = existing_data['ID'].astype(int).max() + # warning_data_df['ID'] = range( + # max_id + 1, max_id + 1 + len(warning_data_df)) + # else: + # warning_data_df['ID'] = range(1, 1 + len(warning_data_df)) + # warning_data_df.to_sql( + # table_name, con=engine, if_exists='append', index=False) + # if is_update_warning_data: + # upload_warning_info(len(warning_data_df)) + # except: + # logger.info('上传预警信息到数据库失败') - 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=global_config['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=global_config['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(sqlitedb, end_time=end_time) @@ -403,15 +403,15 @@ def predict_main(): # 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原油大模型日度预测--{end_time}.pdf' # 报告文件名 + reportname = f'石油焦铝用大模型日度预测--{end_time}.pdf' # 报告文件名 reportname = reportname.replace(':', '-') # 替换冒号 - brent_export_pdf(dataset=dataset, num_models=5 if is_fivemodels else 22, time=end_time, + shiyoujiao_lvyong_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('模型训练完成') - push_market_value() + # push_market_value() # # LSTM 单变量模型 # ex_Lstm(df,input_seq_len=input_size,output_seq_len=horizon,is_debug=is_debug,dataset=dataset) diff --git a/models/nerulforcastmodels.py b/models/nerulforcastmodels.py index c6500b5..8f68abf 100644 --- a/models/nerulforcastmodels.py +++ b/models/nerulforcastmodels.py @@ -866,7 +866,7 @@ def model_losss_yongan(sqlitedb, end_time, table_name_prefix): plt.text(i, j, str(j), ha='center', va='bottom') # 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') @@ -881,8 +881,8 @@ def model_losss_yongan(sqlitedb, end_time, table_name_prefix): 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, @@ -1297,7 +1297,7 @@ def model_losss(sqlitedb, end_time): # plt.plot(df['ds'], df[model], label=model,marker='o') plt.plot(df['ds'], df[most_model_name], 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') # 设置横轴日期格式为年-月-日 @@ -1338,7 +1338,7 @@ def model_losss(sqlitedb, end_time): plt.text(i, j, str(j), ha='center', va='bottom') # 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') # 自动设置横轴日期显示 @@ -1357,8 +1357,8 @@ def model_losss(sqlitedb, end_time): 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, @@ -1388,10 +1388,10 @@ def model_losss(sqlitedb, end_time): bbox_inches='tight') plt.close() - # _plt_predict_ture(df_combined3) + _plt_predict_ture(df_combined3) # _plt_modeltopten_predict_ture(df_combined4) - # _plt_predict_table(df_combined3) - # _plt_model_results3() + _plt_predict_table(df_combined3) + _plt_model_results3() return model_results3 @@ -2461,6 +2461,319 @@ def brent_export_pdf(num_indicators=475, num_models=21, num_dayindicator=202, in print(f"请求超时: {e}") +@exception_logger +def shiyoujiao_lvyong_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( + 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 + feature_df = feature_data_df[['ds', 'y']+features] + # 遍历X每一列，和yy画散点图 ， + + for i, col in enumerate(features): + # try: + print(f'正在绘制第{i+1}个特征{col}与价格散点图...') + if col not in ['ds', 'y']: + fig, ax1 = plt.subplots(figsize=(10, 6)) + # 在第一个坐标轴上绘制数据 + sns.lineplot(data=feature_df, x='ds', y='y', ax=ax1, color='b') + ax1.set_xlabel('日期') + ax1.set_ylabel('y', color='b') + ax1.tick_params('y', colors='b') + # 在 ax1 上添加文本显示值，添加一定的偏移避免值与曲线重叠 + for j in range(1, len(feature_df), 2): + value = feature_df['y'].iloc[j] + date = feature_df['ds'].iloc[j] + offset = 1.001 + ax1.text(date, value * offset, str(round(value, 2)), + ha='center', va='bottom', color='b', fontsize=10) + # 创建第二个坐标轴 + ax2 = ax1.twinx() + # 在第二个坐标轴上绘制数据 + sns.lineplot(data=feature_df, x='ds', y=col, ax=ax2, color='r') + ax2.set_ylabel(col, color='r') + ax2.tick_params('y', colors='r') + # 在 ax2 上添加文本显示值，添加一定的偏移避免值与曲线重叠 + for j in range(0, len(feature_df), 2): + value = feature_df[col].iloc[j] + date = feature_df['ds'].iloc[j] + offset = 1.0003 + ax2.text(date, value * offset, str(round(value, 2)), + ha='center', va='bottom', color='r', fontsize=10) + # 添加标题 + plt.title(col) + # 设置横坐标为日期格式并自动调整 + locator = mdates.AutoDateLocator() + formatter = mdates.AutoDateFormatter(locator) + ax1.xaxis.set_major_locator(locator) + ax1.xaxis.set_major_formatter(formatter) + # 文件名特殊字符处理 + col = col.replace('*', '-') + col = col.replace(':', '-') + col = col.replace(r'/', '-') + plt.savefig(os.path.join(config.dataset, f'{col}与价格散点图.png')) + content.append(Graphs.draw_img( + 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'{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(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;')) + + + # 取df中y列为空的行 + import pandas as pd + df = pd.read_csv(os.path.join(config.dataset, 'predict.csv'), encoding='gbk') + df_true = pd.read_csv(os.path.join( + 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( + 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 + df = df[['ds'] + fivemodels_list.tolist()] + # 拼接预测日期对应的真实值 + df = pd.merge(df, df_true, on='ds', how='left') + # 删除全部为nan的列 + df = df.dropna(how='all', axis=1) + # 选择除 'ds' 列外的数值列，并进行类型转换和四舍五入 + num_cols = [col for col in df.columns if col != + 'ds' and pd.api.types.is_numeric_dtype(df[col])] + for col in num_cols: + df[col] = df[col].astype(float).round(2) + # 添加最大值、最小值、平均值三列 + df['平均值'] = df[num_cols].mean(axis=1).round(2) + df['最大值'] = df[num_cols].max(axis=1) + df['最小值'] = df[num_cols].min(axis=1) + # df转置 + df = df.T + # df重置索引 + df = df.reset_index() + # 添加预测值表格 + data = df.values.tolist() + 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 = pd.read_csv(os.path.join( + 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']) + # 计算模型偏差率 + # 计算各列对于y列的差值百分比 + df3 = pd.DataFrame() # 存储偏差率 + + # 删除有null的行 + df4 = df4.dropna() + 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)) + + content.append(Graphs.draw_little_title('上一周预测准确率：')) + df4 = sqlitedb.select_data('accuracy_rote', order_by='结束日期 desc', limit=1) + df4 = df4.T + df4 = df4.reset_index() + df4 = df4.T + data = df4.values.tolist() + col_width = 500/len(df4.columns) + content.append(Graphs.draw_table(col_width, *data)) + + content.append(Graphs.draw_little_title('三、预测过程解析：')) + # 特征、模型、参数配置 + content.append(Graphs.draw_little_title('模型选择：')) + content.append(Graphs.draw_text( + 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(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(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( + 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 + + grouped = df_zhibiaofenlei.groupby('指标分类') + grouped_corr = pd.DataFrame(columns=['指标分类', '指标数量', '相关性总和']) + + content.append(Graphs.draw_little_title('按指标分类分别与预测目标进行皮尔逊相关系数分析：')) + 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(''' + 相关系数为1：表示两个变量之间存在完全正向的线性关系，即当一个变量增加时，另一个变量也相应增加，且变化是完全一致的。''')) + content.append(Graphs.draw_text( + '''相关系数为-1：表示两个变量之间存在完全负向的线性关系，即当一个变量增加时，另一个变量会相应减少，且变化是完全相反的''')) + 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。''')) + for name, group in grouped: + cols = group['指标名称'].tolist() + 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'] + + # 绘制特征相关性直方分布图 + plt.figure(figsize=(10, 8)) + sns.histplot(correlation_matrix.values.flatten(), + bins=20, kde=True, color='skyblue') + plt.title(f'{name}类指标(共{len(cols_subset)}个)相关性直方分布图') + plt.xlabel('相关系数') + plt.ylabel('频数') + plt.savefig(os.path.join( + 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(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 + positive_corr_features = correlation_matrix[correlation_matrix > 0].sort_values( + ascending=False).index.tolist()[1:] + + print(f'{name}下正相关的特征值有：', positive_corr_features) + if len(positive_corr_features) > 5: + positive_corr_features = positive_corr_features[0:5] + content.append(Graphs.draw_text( + f'{name}类指标中，与预测目标y正相关前五的特征有：{positive_corr_features}')) + draw_feature_trend(feature_data_df, positive_corr_features) + elif len(positive_corr_features) == 0: + pass + else: + positive_corr_features = positive_corr_features + content.append(Graphs.draw_text( + f'其中，与预测目标y正相关的特征有：{positive_corr_features}')) + draw_feature_trend(feature_data_df, positive_corr_features) + + # 相关性小于0的特征 + negative_corr_features = correlation_matrix[correlation_matrix < 0].sort_values( + ascending=True).index.tolist() + + print(f'{name}下负相关的特征值有：', negative_corr_features) + if len(negative_corr_features) > 5: + negative_corr_features = negative_corr_features[:5] + content.append(Graphs.draw_text( + f'与预测目标y负相关前五的特征有：{negative_corr_features}')) + draw_feature_trend(feature_data_df, negative_corr_features) + elif len(negative_corr_features) == 0: + pass + else: + content.append(Graphs.draw_text( + f'{name}类指标中，与预测目标y负相关的特征有：{negative_corr_features}')) + draw_feature_trend(feature_data_df, negative_corr_features) + # 计算correlation_sum 第一行的相关性的绝对值的总和 + correlation_sum = correlation_matrix.abs().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]}) + grouped_corr = pd.concat( + [grouped_corr, goup_corr], axis=0, ignore_index=True) + + # 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(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(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') + 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(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: + for introduction in line_split: + content.append(Graphs.draw_text(introduction)) + content.append(Graphs.draw_little_title('模型评估：')) + df = pd.read_csv(os.path.join( + 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)']: + eval_df[col] = eval_df[col].astype(float) + eval_df[col] = eval_df[col].round(3) + # 筛选 fivemodels_list.tolist() 的行 + eval_df = eval_df[eval_df['模型(Model)'].isin(fivemodels_list)] + # df转置 + eval_df = eval_df.T + # df重置索引 + eval_df = eval_df.reset_index() + eval_df = eval_df.T + # # 添加表格 + data = eval_df.values.tolist() + col_width = 500/len(eval_df.columns) + content.append(Graphs.draw_table(col_width, *data)) + content.append(Graphs.draw_text('评估指标释义：')) + content.append(Graphs.draw_text( + '1. 均方根误差(RMSE)：均方根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( + '2. 平均绝对误差(MAE)：平均绝对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( + '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(config.dataset, '预测值与真实值对比图.png'))) + # 生成pdf文件 + 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(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 + token = get_head_auth_report() + upload_report_data(token, upload_data) + except TimeoutError as e: + print(f"请求超时: {e}") + + @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'): # 创建内容对应的空列表