[英]F1-score per class for multi-class classification
I'm working on a multiclass classification problem using python and scikit-learn.我正在使用 python 和 scikit-learn 处理多类分类问题。 Currently, I'm using the classification_report
function to evaluate the performance of my classifier, obtaining reports like the following:目前,我正在使用classification_report
函数来评估classification_report
的性能,获得如下报告:
>>> 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.为了做进一步的分析,我对获得每个可用类的每类 f1 分数很感兴趣。 Maybe something like this:也许是这样的:
>>> print(calculate_f1_score(y_true, y_pred, target_class='class 0'))
0.67
Is there something like that available on scikit-learn? scikit-learn 上有类似的东西吗?
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:如果您只有混淆矩阵C
,行对应于预测,列对应于事实,则可以使用以下函数计算 F1 分数:
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
您只需要使用 pos_label 作为参数并分配要打印的类值。
f1_score(ytest, ypred_prob, pos_label=0)# default is pos_label=1
I would use the f1_score
along with the labels
argument我会使用f1_score
和labels
参数
from sklearn.metrics import f1_score
y_true = [0, 1, 2, 0, 1, 2]
y_pred = [0, 2, 1, 0, 0, 1]
labels = [0, 1, 2]
f1_scores = f1_score(y_true, y_pred, average=None, labels=labels)
f1_scores_with_labels = {label:score for label,score in zip(labels, f1_scores)}
Outputs:输出:
{0: 0.8, 1: 0.0, 2: 0.0}
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