Question

=”“

我在尝试获取召回分数时收到此错误。

X_test = test_pos_vec + test_neg_vec
Y_test = ["pos"] * len(test_pos_vec) + ["neg"] * len(test_neg_vec)

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

print(recall_average)

这会给我：

    C:\Users\anca_elena.moisa\AppData\Local\Programs\Python\Python36\lib\site-packages\sklearn\metrics\classification.py:1030: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison
  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')

我试图改变＆＃39; pos＆＃39;在1和＆＃39; neg＆＃39;在0这样：

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

但是这给了我另一个错误：

    C:\Users\anca_elena.moisa\AppData\Local\Programs\Python\Python36\lib\site-packages\sklearn\metrics\classification.py:181: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison
  score = y_true == y_pred
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 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)

我要做的是获取指标：准确度，精确度，召回率，f_measure。使用average='weighted'，我得到相同的结果：准确度=召回。我想这不对，所以我更改了average='binary'，但我有这些错误。有什么想法吗？

Answer 1

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

使用"neg"或"pos"作为pos_label，此错误不会再出现。

Answer 2

用(pos_label=pos)

表示您的正面评价

因此使用：

Recall=recall_score(Y_test, Y_predict, pos_label='pos')

Answer 3

遇到此错误时，意味着target变量的值不是recall_score()的预期值，默认情况下，对于肯定情况，其值为 1 和 0（否定情况下） [这也适用于precision_score()]

根据您提到的错误：

pos_label=1 is not a valid label: array(['neg', 'pos']

很明显，您的积极情景的值为pos而不是1，而消极的neg的价值观是0。

然后，您必须选择纠正此不匹配的选项：

在recall_score()出现以下情况时，请更改pos中的默认值：

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

将数据集中目标变量的值更改为1或0

Y_test = Y_test.map({'pos': 1, 'neg': 0}).astype(int)

ValueError：pos_label = 1不是有效的标签：数组（[＆＃39; neg＆＃39;，＆＃39; pos＆＃39;]，dtype =＆＃39; <u3'）

3 个答案: