ValueError: pos_label=1 is not a valid label: array(['neg', 'pos'], dtype='<U3')

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')

我尝试以这种方式在 1 中转换 'pos' 并在 0 中转换 'neg'：

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

但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)

我想做的是获取指标：准确性、精确度、召回率，f_measure。使用 average='weighted'，我得到相同的结果：accuracy=recall。我猜这是不正确的，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")

将 "neg" 或 "pos" 用作 pos_label，此错误将不会再次出现。

Answer 2

用(pos_label=pos)

表明你的积极class

所以使用：

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

Answer 3

当您遇到此错误时，这意味着您的 target 变量的值不是 recall_score() 的预期值，对于正例 [=40]，默认情况下为 1 =] 和 0 用于否定案例 [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，您的积极情景的价值是 pos 而不是 1 和消极的 neg 而不是 0。

那么你有两种选择来解决这个不匹配问题：

更改 recall_score() 中的默认值以考虑 pos 出现时的积极情况：

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)

Answer 4

recall_average = recall_score(Y_test, y_predict, pos_label="no")

pos_label 的数组只有 ["yes","no"]

ValueError: pos_label=1 is not a valid label: array(['neg', 'pos'], dtype='<U3')

ValueError: pos_label=1 is not a valid label: array(['neg', 'pos'], dtype='<U3')

python

precision

machine-learning

precision-recall