我正在尝试在数据集S_train的长度小于或等于给定数字（在本例中为11）时迭代一些值。这是目前我所做的

S_new = trainT_new = testmu_new = mumu_test_new = mu_testwhile len(S_new) <= 11:  ground_test =  T_new[target].values.tolist()  acquisition_function = abs(mu_test - ground_test)  max_item = np.argmax(acquisition_function) #step 3 : value in test set that maximizes the abs difference of the energy  alpha_al = test.iloc[[max_item]]  #identify the minimum step in test set  S_new = S_new.append(alpha_al)  len(S_new)  T_new = T_new.drop(test.index[max_item])  len(T_new)  gpr = GaussianProcessRegressor(    # kernel is the covariance function of the gaussian process (GP)    kernel=Normalization( # kernel equals to normalization -> normalizes a kernel using the cosine of angle formula, k_normalized(x,y) = k(x,y)/sqrt(k(x,x)*k(y,y))        # graphdot.kernel.fix.Normalization(kernel), set kernel as marginalized graph kernel, which is used to calculate the similarity between 2 graphs        # implement the random walk-based graph similarity kernel as Kashima, H., Tsuda, K., & Inokuchi, A. (2003). Marginalized kernels between labeled graphs. ICML        Tang2019MolecularKernel()    ),    alpha=1e-4, # value added to the diagonal of the kernel matrix during fitting    optimizer=True, # default optimizer of L-BFGS-B based on scipy.optimize.minimize    normalize_y=True, # normalize the y values so taht the means and variance is 0 and 1, repsectively. Will be reversed when predicions are returned    regularization='+', # alpha (1e-4 in this case) is added to the diagonals of the kernal matrix     )    start_time = time.time()  gpr.fit(S_new.graphs, S_new[target], repeat=1, verbose=True) # Fitting train set as graphs (independent variable) with train[target] as dependient variable  end_time = time.time()  print("the total time consumption is " + str(end_time - start_time) + ".")   gpr.kernel.hyperparameters    rmse_training = []  rmse_test = []  mu_new = gpr.predict(S_new.graphs)  print('Training set')  print('MAE:', np.mean(np.abs(S_new[target] - mu_new)))  print('RMSE:', np.std(S_new[target] - mu_new))  rmse_training.append(np.std(S_new[target] - mu_new)  mu_test_new = gpr.predict(T_new.graphs)  print('Training set')  print('MAE:', np.mean(np.abs(T_new[target] - mu_test_new)))  print('RMSE:', np.std(T_new[target] - mu_test_new))  rmse_test.append(np.std(T_new[target] - mu_test_new)

基本来说，我正在计算T_new中最大化T_new中第i个元素与mu_test之间的绝对误差的值，并将其添加到S_train集合中，然后从T_new中删除它。使用新的S_train，我将再次训练我的模型，然后重复上述操作。我从未使用过while循环，我在寻找正确的语法，看起来是正确的，但我得到了这个错误消息：

File "<ipython-input-55-d284ca5f9d1f>", line 42    mu_test_new = gpr.predict(T_new.graphs)              ^SyntaxError: invalid syntax

你知道可能是什么原因吗？任何建议都将不胜感激。感谢你一直以来的帮助。

回答：

问题不在于while循环，而只是一个打字错误。具体来说，这行代码 –

  rmse_training.append(np.std(S_new[target] - mu_new)

缺少一个闭合括号。
如果你尝试

  rmse_training.append(np.std(S_new[target] - mu_new))

你看到的错误将会消失。

值得注意的是，有时报告的特定行错误可能是由于之前的语法错误引起的，在调试时要注意这一点。