From c8320bf849860b27a556af5852ea8c161ee289f7 Mon Sep 17 00:00:00 2001 From: liurui Date: Thu, 7 Nov 2024 11:32:38 +0800 Subject: [PATCH] =?UTF-8?q?=E8=B0=83=E6=95=B4=E6=9C=80=E4=BD=B3=E6=A8=A1?= =?UTF-8?q?=E5=9E=8B=E5=9C=A8=E7=BB=98=E5=9B=BE=E4=B9=8B=E5=89=8D?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- main.py | 38 ++++++++++++++-------------- models/nerulforcastmodels.py | 49 ++++++++++++++++++------------------ 2 files changed, 44 insertions(+), 43 deletions(-) diff --git a/main.py b/main.py index d06d24b..05d09b9 100644 --- a/main.py +++ b/main.py @@ -118,25 +118,25 @@ def predict_main(): row,col = df.shape now = datetime.datetime.now().strftime('%Y%m%d%H%M%S') - ex_Model(df, - horizon=horizon, - input_size=input_size, - train_steps=train_steps, - val_check_steps=val_check_steps, - early_stop_patience_steps=early_stop_patience_steps, - is_debug=is_debug, - dataset=dataset, - is_train=is_train, - is_fivemodels=is_fivemodels, - val_size=val_size, - test_size=test_size, - settings=settings, - now=now, - etadata = etadata, - modelsindex = modelsindex, - data = data, - is_eta=is_eta, - ) + # ex_Model(df, + # horizon=horizon, + # input_size=input_size, + # train_steps=train_steps, + # val_check_steps=val_check_steps, + # early_stop_patience_steps=early_stop_patience_steps, + # is_debug=is_debug, + # dataset=dataset, + # is_train=is_train, + # is_fivemodels=is_fivemodels, + # val_size=val_size, + # test_size=test_size, + # settings=settings, + # now=now, + # etadata = etadata, + # modelsindex = modelsindex, + # data = data, + # is_eta=is_eta, + # ) logger.info('模型训练完成') diff --git a/models/nerulforcastmodels.py b/models/nerulforcastmodels.py index 74dc7d6..e26296e 100644 --- a/models/nerulforcastmodels.py +++ b/models/nerulforcastmodels.py @@ -509,6 +509,31 @@ def model_losss_juxiting(sqlitedb): df_combined3 = df_combined.copy() # 备份df_combined,后面画图需要 + # 空的列表存储每个模型的MSE、RMSE、MAE、MAPE、SMAPE + cellText = [] + + # 遍历模型名称，计算模型评估指标 + for model in modelnames: + modelmse = mse(df_combined['y'], df_combined[model]) + modelrmse = rmse(df_combined['y'], df_combined[model]) + modelmae = mae(df_combined['y'], df_combined[model]) + # modelmape = mape(df_combined['y'], df_combined[model]) + # modelsmape = smape(df_combined['y'], df_combined[model]) + # modelr2 = r2_score(df_combined['y'], df_combined[model]) + cellText.append([model,round(modelmse, 3), round(modelrmse, 3), round(modelmae, 3)]) + + model_results3 = pd.DataFrame(cellText,columns=['模型(Model)','平均平方误差(MSE)', '均方根误差(RMSE)', '平均绝对误差(MAE)']) + # 按MSE降序排列 + model_results3 = model_results3.sort_values(by='平均平方误差(MSE)', ascending=True) + model_results3.to_csv(os.path.join(dataset,"model_evaluation.csv"),index=False) + modelnames = model_results3['模型(Model)'].tolist() + allmodelnames = modelnames.copy() + # 保存5个最佳模型的名称 + if len(modelnames) > 5: + modelnames = modelnames[0:5] + with open(os.path.join(dataset,"best_modelnames.txt"), 'w') as f: + f.write(','.join(modelnames) + '\n') + # 使用最佳五个模型进行绘图 best_models = pd.read_csv(os.path.join(dataset,'best_modelnames.txt'),header=None).values.flatten().tolist() def find_min_max_within_quantile(row): @@ -605,30 +630,6 @@ def model_losss_juxiting(sqlitedb): df_combined3.to_csv(os.path.join(dataset,"testandpredict_groupby.csv"),index=False) - # 空的列表存储每个模型的MSE、RMSE、MAE、MAPE、SMAPE - cellText = [] - - # 遍历模型名称，计算模型评估指标 - for model in modelnames: - modelmse = mse(df_combined['y'], df_combined[model]) - modelrmse = rmse(df_combined['y'], df_combined[model]) - modelmae = mae(df_combined['y'], df_combined[model]) - # modelmape = mape(df_combined['y'], df_combined[model]) - # modelsmape = smape(df_combined['y'], df_combined[model]) - # modelr2 = r2_score(df_combined['y'], df_combined[model]) - cellText.append([model,round(modelmse, 3), round(modelrmse, 3), round(modelmae, 3)]) - - model_results3 = pd.DataFrame(cellText,columns=['模型(Model)','平均平方误差(MSE)', '均方根误差(RMSE)', '平均绝对误差(MAE)']) - # 按MSE降序排列 - model_results3 = model_results3.sort_values(by='平均平方误差(MSE)', ascending=True) - model_results3.to_csv(os.path.join(dataset,"model_evaluation.csv"),index=False) - modelnames = model_results3['模型(Model)'].tolist() - allmodelnames = modelnames.copy() - # 保存5个最佳模型的名称 - if len(modelnames) > 5: - modelnames = modelnames[0:5] - with open(os.path.join(dataset,"best_modelnames.txt"), 'w') as f: - f.write(','.join(modelnames) + '\n') # 预测值与真实值对比图 plt.rcParams['font.sans-serif'] = ['SimHei']