我在尝试计算使用拉普拉斯核（作为预计算核）的SVM的准确性得分时，遇到了下面的错误。
我的代码如下：
import numpy as npfrom sklearn.model_selection import train_test_splitfrom sklearn.datasets import load_irisfrom sklearn.metrics import accuracy_scorefrom sklearn.svm import SVCfrom sklearn.metrics.pairwise import laplacian_kernel#加载鸢尾花数据iris_data = load_iris()#分割数据和目标X = iris_data.datay = iris_data.target#将X和y转换为numpy数组X = np.array(X)y = np.array(y)#执行训练-测试分割X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.30, random_state=42, shuffle=True)#使用拉普拉斯核 - https://scikit-learn.org/stable/modules/metrics.html#laplacian-kernelK = np.array(laplacian_kernel(X_train, gamma=.5))svm = SVC(kernel='precomputed').fit(K, np.ravel(y_train))pred_y = svm.predict(K)#打印准确性得分 - 错误发生在这里print(accuracy_score(y_test, pred_y))当我运行这段代码时，出现了如下错误：
Traceback (most recent call last): File "/Users/user/Desktop/Research/Src/Laplace.py", line 36, in <module> print(accuracy_score(y_test, pred_y)) File "/Users/user/miniforge3/envs/user_venv/lib/python3.8/site-packages/sklearn/utils/validation.py", line 63, in inner_f return f(*args, **kwargs) File "/Users/user/miniforge3/envs/user/lib/python3.8/site-packages/sklearn/metrics/_classification.py", line 202, in accuracy_score y_type, y_true, y_pred = _check_targets(y_true, y_pred) File "/Users/user/miniforge3/envs/user/lib/python3.8/site-packages/sklearn/metrics/_classification.py", line 83, in _check_targets check_consistent_length(y_true, y_pred) File "/Users/user/miniforge3/envs/user/lib/python3.8/site-packages/sklearn/utils/validation.py", line 262, in check_consistent_length raise ValueError("Found input variables with inconsistent numbers of"ValueError: Found input variables with inconsistent numbers of samples: [45, 105]那么，我该如何解决这个错误呢？
回答：
您使用训练输入计算了pred_y，它包含105个元素，而y_test包含45个元素。
您需要添加以下步骤：
#user3046211's codeimport numpy as npfrom sklearn.model_selection import train_test_splitfrom sklearn.datasets import load_irisfrom sklearn.metrics import accuracy_scorefrom sklearn.svm import SVCfrom sklearn.metrics.pairwise import laplacian_kernel#加载鸢尾花数据iris_data = load_iris()#分割数据和目标X = iris_data.datay = iris_data.target#将X和y转换为numpy数组X = np.array(X)y = np.array(y)#执行训练-测试分割X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.30, random_state=42, shuffle=True)#使用拉普拉斯核 - https://scikit-learn.org/stable/modules/metrics.html#laplacian-kernelK = np.array(laplacian_kernel(X_train, gamma=.5))svm = SVC(kernel='precomputed').fit(K, np.ravel(y_train))pred_y = svm.predict(K)#打印准确性得分 - 错误发生在这里print(accuracy_score(y_test, pred_y))# 新代码从这里开始K_test = np.array(laplacian_kernel(X=X_test,Y=X_train, gamma=.5))pred_y_test = svm.predict(K_test)print(accuracy_score(y_test, pred_y_test))