“SVC”对象没有属性“SVC”

Question

我正在努力解决一个简单的循环：

for kernel in ('linear','poly', 'rbf'):
    svm = svm.SVC(kernel=kernel, C=1)
    svm.fit(trainingdata_without_labels, trainingdata_labels)

    predicted_labels = svm.predict(testdata_without_labels)
    print("testing success ratio with "+ kernel + "kernel :" + str(accuracy_score(testdata_labels, predicted_labels)))

它对于第一个循环工作正常，但后来我得到：

属性错误：“SVC”对象没有属性“SVC”

我真的很想了解我的错误。

非常感谢提前<3

Answer 1

您正在使用第一个循环覆盖 svm。

尝试更改分类器的名称，例如：

for kernel in ('linear','poly', 'rbf'):
    classifier_svm = svm.SVC(kernel=kernel, C=1)
    classifier_svm.fit(trainingdata_without_labels, trainingdata_labels)

    predicted_labels = classifier_svm.predict(testdata_without_labels)
    print("testing success ratio with "+ kernel + "kernel :" + str(accuracy_score(testdata_labels, predicted_labels)))

此外，我认为你尝试做的事情，找到最佳内核，使用 GridSearchCV 更容易解决：

from sklearn.model_selection import GridSearchCV
from sklearn.metrics import classification_report
from sklearn.svm import SVC


tuned_parameters = [{'kernel': ['linear', 'poly', 'rbf'],
                     'C': [1]}
                   ]

clf = GridSearchCV(SVC(), tuned_parameters, scoring='accuracy')
clf.fit(trainingdata_without_labels, trainingdata_labels)


print("Best parameters set found on development set:\n")
print(clf.best_params_)
print("\nGrid scores on development set:\n")
means = clf.cv_results_['mean_test_score']
stds = clf.cv_results_['std_test_score']
for mean, std, params in zip(means, stds, clf.cv_results_['params']):
    print("%0.3f (+/-%0.03f) for %r"
          % (mean, std * 2, params))

print("\nDetailed classification report:\n")
print("The model is trained on the full development set.")
print("The scores are computed on the full evaluation set.")

y_true, y_pred = testdata_labels, clf.predict(testdata_without_labels)

print(classification_report(y_true, y_pred))

使用此代码片段，您将使用 3 个内核训练模型，并进行 5 折交叉验证。最后计算测试变量的分类报告（精确率、召回率、f1 分数）。最终报告应如下所示（每一行都是要在数据中预测的类）：

                precision    recall  f1-score   support

           0       1.00      1.00      1.00        27
           1       0.95      1.00      0.97        35
           2       1.00      1.00      1.00        36
           3       1.00      1.00      1.00        29
           4       1.00      1.00      1.00        30
           5       0.97      0.97      0.97        40
           6       1.00      0.98      0.99        44
           7       1.00      1.00      1.00        39
           8       1.00      0.97      0.99        39
           9       0.98      0.98      0.98        41

    accuracy                           0.99       360
   macro avg       0.99      0.99      0.99       360
weighted avg       0.99      0.99      0.99       360