In [1]:

    

from sklearn.metrics import confusion_matrix
from sklearn.metrics import precision_score
from sklearn.metrics import classification_report


    

In [2]:

    

y_true_multi = [0, 0, 0, 1, 1, 1, 2, 2, 2]
y_pred_multi = [0, 1, 1, 1, 1, 2, 2, 2, 2]


    

In [3]:

    

print(confusion_matrix(y_true_multi, y_pred_multi))


    

    




[[1 2 0]
 [0 2 1]
 [0 0 3]]

In [4]:

    

# print(precision_score(y_true_multi, y_pred_multi))
# ValueError: Target is multiclass but average='binary'. Please choose another average setting.


    

In [5]:

    

print(precision_score(y_true_multi, y_pred_multi, average=None))


    

    




[1.   0.5  0.75]

In [6]:

    

print(precision_score(y_true_multi, y_pred_multi, average='macro'))


    

    




0.75

In [7]:

    

print(precision_score(y_true_multi, y_pred_multi, average='micro'))


    

    




0.6666666666666666

In [8]:

    

print(precision_score(y_true_multi, y_pred_multi, average='weighted'))


    

    




0.75

In [9]:

    

print(classification_report(y_true_multi, y_pred_multi))


    

    




              precision    recall  f1-score   support

           0       1.00      0.33      0.50         3
           1       0.50      0.67      0.57         3
           2       0.75      1.00      0.86         3

   micro avg       0.67      0.67      0.67         9
   macro avg       0.75      0.67      0.64         9
weighted avg       0.75      0.67      0.64         9