F1-score per class for multi-class classification

Question

我正在使用 python 和 scikit-learn 解决多类分类问题。目前，我正在使用classification_report 函数来评估我的分类器的性能，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

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。也许是这样的：

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

scikit-learn 上有类似的东西吗？

Answer 1

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)}

输出：

{0: 0.8, 1: 0.0, 2: 0.0}

Answer 2

您只需要使用 pos_label 作为参数并分配您要打印的类值。

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

Answer 3

如果你只有混淆矩阵C，行对应预测，列对应真实，你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

Answer 4

取自f1_scoredocs。

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)

输出：

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

每门课的分数是多少。