From e165f556e7d633e9a5f9265047e417551f0ca0f8 Mon Sep 17 00:00:00 2001 From: workpc Date: Tue, 27 May 2025 10:36:37 +0800 Subject: [PATCH 1/2] =?UTF-8?q?=E8=81=9A=E7=83=AF=E7=83=83=E5=91=A8?= =?UTF-8?q?=E5=BA=A6=E8=B0=83=E8=AF=95?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- main_juxiting_zhoudu.py | 107 ++++++++++++++++++++-------------------- 1 file changed, 53 insertions(+), 54 deletions(-) diff --git a/main_juxiting_zhoudu.py b/main_juxiting_zhoudu.py index 26a5db0..5048ad6 100644 --- a/main_juxiting_zhoudu.py +++ b/main_juxiting_zhoudu.py @@ -301,42 +301,42 @@ def predict_main(): # 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:-2] - 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_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:-2] + # 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) @@ -375,17 +375,17 @@ def predict_main(): 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'聚烯烃PP大模型周度预测--{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ing') + # title = f'{settings}--{end_time}-预测报告' # 报告标题 + # reportname = f'聚烯烃PP大模型周度预测--{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('模型训练完成') + # 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) @@ -412,12 +412,11 @@ 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 - - predict_main() + for i_time in pd.date_range('2025-3-1', '2025-5-26', freq='W'): + 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 + # predict_main() From 6ea5881cc4f2d1f312e02c6f8f6289b85042345d Mon Sep 17 00:00:00 2001 From: workpc Date: Tue, 27 May 2025 11:26:12 +0800 Subject: [PATCH 2/2] =?UTF-8?q?=E7=9F=B3=E6=B2=B9=E7=84=A6=E9=93=9D?= =?UTF-8?q?=E7=94=A8=E8=87=AA=E5=BB=BA=E6=8C=87=E6=A0=87=E5=88=9B=E5=BB=BA?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- eta数据调试.py | 309 ++++++++++++++++++++++++++++--------------------- 1 file changed, 177 insertions(+), 132 deletions(-) diff --git a/eta数据调试.py b/eta数据调试.py index 25c720f..1273afe 100644 --- a/eta数据调试.py +++ b/eta数据调试.py @@ -128,138 +128,183 @@ modelsindex = { } selfid = { - "聚烯烃价格预测NHITS模型-次周": "SELF0000231", - "聚烯烃价格预测Informer模型-次周": "SELF0000232", - "聚烯烃价格预测LSTM模型-次周": "SELF0000233", - "聚烯烃价格预测iTransformer模型-次周": "SELF0000234", - "聚烯烃价格预测TSMixer模型-次周": "SELF0000235", - "聚烯烃价格预测TSMixerx模型-次周": "SELF0000236", - "聚烯烃价格预测PatchTST模型-次周": "SELF0000237", - "聚烯烃价格预测RNN模型-次周": "SELF0000238", - "聚烯烃价格预测GRU模型-次周": "SELF0000239", - "聚烯烃价格预测TCN模型-次周": "SELF0000240", - "聚烯烃价格预测BiTCN模型-次周": "SELF0000241", - "聚烯烃价格预测DilatedRNN模型-次周": "SELF0000242", - "聚烯烃价格预测MLP模型-次周": "SELF0000243", - "聚烯烃价格预测DLinear模型-次周": "SELF0000244", - "聚烯烃价格预测NLinear模型-次周": "SELF0000245", - "聚烯烃价格预测TFT模型-次周": "SELF0000246", - "聚烯烃价格预测FEDformer模型-次周": "SELF0000247", - "聚烯烃价格预测StemGNN模型-次周": "SELF0000248", - "聚烯烃价格预测MLPMultivariate模型-次周": 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