def train_and_pred(train_features, test_features, train_labels, test_data, num_epochs, lr, weight_decay, batch_size): model=linreg train_ls, _ ,theta= train(model, train_features, train_labels, None, None, #None, None表示是没有验证集，只做训练，得到模型net num_epochs, lr, weight_decay, batch_size) plot(np.arange(1, num_epochs + 1), train_ls, xlabel='epoch', ylabel='log rmse', xlim=[1, num_epochs], yscale='log') print(f'训练log rmse：{float(train_ls[-1]):f}') preds = model(test_features,theta) # 将其重新格式化以导出到Kaggle test_data['SalePrice'] = pd.Series(preds.reshape(1, -1)[0]) submission = pd.concat([test_data['Id'], test_data['SalePrice']], axis=1) submission.to_csv('submission.csv', index=False)帮我写个注释

以下是对该代码的注释：

def train_and_pred(train_features, test_features, train_labels, test_data, num_epochs, lr, weight_decay, batch_size):
    # 定义模型
    model = linreg
    # 训练模型并得到训练误差、模型参数
    train_ls, _, theta = train(model, train_features, train_labels, None, None, num_epochs, lr, weight_decay, batch_size)
    # 绘制训练误差随 epoch 变化的曲线图
    plot(np.arange(1, num_epochs + 1), train_ls, xlabel='epoch', ylabel='log rmse', xlim=[1, num_epochs], yscale='log')
    # 输出最终的训练误差
    print(f'训练log rmse：{float(train_ls[-1]):f}')
    # 对测试集进行预测并将预测结果保存到 submission.csv 文件中
    preds = model(test_features, theta)
    test_data['SalePrice'] = pd.Series(preds.reshape(1, -1)[0])
    submission = pd.concat([test_data['Id'], test_data['SalePrice']], axis=1)
    submission.to_csv('submission.csv', index=False)

注释中对代码的不同部分都进行了说明，包括：

- 定义模型
- 训练模型并得到训练误差、模型参数
- 绘制训练误差随 epoch 变化的曲线图
- 输出最终的训练误差
- 对测试集进行预测并将预测结果保存到 submission.csv 文件中