我在尝试获取召回率评分时遇到了这个错误。

X_test = test_pos_vec + test_neg_vecY_test = ["pos"] * len(test_pos_vec) + ["neg"] * len(test_neg_vec)recall_average = recall_score(Y_test, y_predict, average="binary")print(recall_average)

这会导致以下结果：

    C:\Users\anca_elena.moisa\AppData\Local\Programs\Python\Python36\lib\site-packages\sklearn\metrics\classification.py:1030: FutureWarning: 逐元素比较失败；返回标量而不是在未来将执行逐元素比较  if pos_label not in present_labels:Traceback (most recent call last):  File "G:/PyCharmProjects/NB/accuracy/script.py", line 812, in <module>    main()  File "G:/PyCharmProjects/NB/accuracy/script.py", line 91, in main    evaluate_model(model, train_pos_vec, train_neg_vec, test_pos_vec, test_neg_vec, False)  File "G:/PyCharmProjects/NB/accuracy/script.py", line 648, in evaluate_model    recall_average = recall_score(Y_test, y_predict, average="binary")  File "C:\Users\anca_elena.moisa\AppData\Local\Programs\Python\Python36\lib\site-packages\sklearn\metrics\classification.py", line 1359, in recall_score    sample_weight=sample_weight)  File "C:\Users\anca_elena.moisa\AppData\Local\Programs\Python\Python36\lib\site-packages\sklearn\metrics\classification.py", line 1036, in precision_recall_fscore_support    (pos_label, present_labels))ValueError: pos_label=1 is not a valid label: array(['neg', 'pos'],      dtype='<U3')

我尝试通过以下方式将’pos’转换为1，将’neg’转换为0：

for i in range(len(Y_test)):     if 'neg' in Y_test[i]:         Y_test[i] = 0     else:         Y_test[i] = 1

但这导致了另一个错误：

    C:\Users\anca_elena.moisa\AppData\Local\Programs\Python\Python36\lib\site-packages\sklearn\metrics\classification.py:181: FutureWarning: 逐元素比较失败；返回标量而不是在未来将执行逐元素比较  score = y_true == y_predTraceback (most recent call last):  File "G:/PyCharmProjects/NB/accuracy/script.py", line 812, in <module>    main()  File "G:/PyCharmProjects/NB/accuracy/script.py", line 91, in main    evaluate_model(model, train_pos_vec, train_neg_vec, test_pos_vec, test_neg_vec, False)  File "G:/PyCharmProjects/NB/accuracy/script.py", line 648, in evaluate_model    recall_average = recall_score(Y_test, y_predict, average="binary")  File "C:\Users\anca_elena.moisa\AppData\Local\Programs\Python\Python36\lib\site-packages\sklearn\metrics\classification.py", line 1359, in recall_score    sample_weight=sample_weight)  File "C:\Users\anca_elena.moisa\AppData\Local\Programs\Python\Python36\lib\site-packages\sklearn\metrics\classification.py", line 1026, in precision_recall_fscore_support    present_labels = unique_labels(y_true, y_pred)  File "C:\Users\anca_elena.moisa\AppData\Local\Programs\Python\Python36\lib\site-packages\sklearn\utils\multiclass.py", line 103, in unique_labels    raise ValueError("Mix of label input types (string and number)")ValueError: Mix of label input types (string and number)

我正在尝试获取以下指标：准确率、精确率、召回率、F测量值。使用 average='weighted'，我得到了相同的结果：准确率=召回率。我猜这不正确，所以我将 average 改为 'binary'，但出现了这些错误。有什么建议吗？

回答：

recall_average = recall_score(Y_test, y_predict, average="binary", pos_label="neg")

使用 "neg" 或 "pos" 作为 pos_label，这个错误就不会再次出现。