F1-score per class for multi-class classification

15
votes

I'm working on a multiclass classification problem using python and scikit-learn. Currently, I'm using the classification_report function to evaluate the performance of my classifier, obtaining reports like the following:

>>> print(classification_report(y_true, y_pred, target_names=target_names))
             precision    recall  f1-score   support

    class 0       0.50      1.00      0.67         1
    class 1       0.00      0.00      0.00         1
    class 2       1.00      0.67      0.80         3

avg / total       0.70      0.60      0.61         5

To do further analysis, I'm interesting in obtaining the per-class f1 score of each of the classes available. Maybe something like this:

>>> print(calculate_f1_score(y_true, y_pred, target_class='class 0'))
0.67

Is there something like that available on scikit-learn?

3
pythonmachine-learningscikit-learn

3 Answers

27
votes

Taken from the f1_score docs.

from sklearn.metrics import f1_score
y_true = [0, 1, 2, 0, 1, 2]
y_pred = [0, 2, 1, 0, 0, 1]

f1_score(y_true, y_pred, average=None)

Ouputs:

array([ 0.8,  0. ,  0. ])

Which is the scores for each class.

0
votes

If you only have the confusion matrix C, with rows corresponding to predictions and columns corresponding to truth, you can compute F1 score using the following function:

def f1(C):
    num_classes = np.shape(C)[0]
    f1_score = np.zeros(shape=(num_classes,), dtype='float32')
    weights = np.sum(C, axis=0)/np.sum(C)

    for j in range(num_classes):
        tp = np.sum(C[j, j])
        fp = np.sum(C[j, np.concatenate((np.arange(0, j), np.arange(j+1, num_classes)))])
        fn = np.sum(C[np.concatenate((np.arange(0, j), np.arange(j+1, num_classes))), j])
#         tn = np.sum(C[np.concatenate((np.arange(0, j), np.arange(j+1, num_classes))), np.concatenate((np.arange(0, j), np.arange(j+1, num_classes)))])

        precision = tp/(tp+fp) if (tp+fp) > 0 else 0
        recall = tp/(tp+fn) if (tp+fn) > 0 else 0
        f1_score[j] = 2*precision*recall/(precision + recall)*weights[j] if (precision + recall) > 0 else 0

    f1_score = np.sum(f1_score)
    return f1_score
0
votes

You just need to use pos_label as parameter and assign the class value which you want to print.

f1_score(ytest, ypred_prob, pos_label=0)# default is pos_label=1