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ValueError: pos_label = 1 n'est pas une étiquette valide: array (['neg', 'pos'], dtype = '<U3')

Je reçois cette erreur en essayant d'obtenir le score de rappel.

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)

Cela me donnera:

    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')

J'ai essayé de transformer 'pos' en 1 et 'neg' en 0 de cette façon:

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

Mais cela me donne une autre erreur:

    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)

Ce que j'essaie de faire, c'est d'obtenir les métriques: exactitude, précision, rappel, f_measure. Avec average='weighted', J'obtiens le même résultat: précision = rappel. Je suppose que ce n'est pas correct, j'ai donc changé le average='binary', mais j'ai ces erreurs. Des idées?

pythonmachine-learningprecisionprecision-recall
7
Mr. Wizard
recall_average = recall_score(Y_test, y_predict, average="binary", pos_label="neg")

Utilisation "neg" ou "pos" comme pos_label et cette erreur ne se reproduira plus.

9
Steve

Indiquez votre classe positive avec (pos_label=pos)

Alors utilisez:

Recall=recall_score(Y_test, Y_predict, pos_label='pos')
0
Bowale Samuel