问题描述
我使用 sklearns crfsuite 来计算 f1、精度和召回分数，但存在异常。仅出于测试目的，我给出了相同的测试和预测值。
from sklearn_crfsuite import scorers
from sklearn_crfsuite import metrics
cls = [i for i,_ in enumerate(CLASSES)]
cls.append(7)
cls.append(8)
print(metrics.flat_classification_report(
test["y"],test["y"],labels=cls,digits=3
))
precision recall f1-score support
0 1.000 1.000 1.000 551
1 0.000 0.000 0.000 0
2 0.000 0.000 0.000 0
3 1.000 1.000 1.000 1196
4 1.000 1.000 1.000 2593
5 1.000 1.000 1.000 95200
6 1.000 1.000 1.000 1165
7 1.000 1.000 1.000 9636
8 1.000 1.000 1.000 506363
micro avg 1.000 1.000 1.000 616704
macro avg 0.778 0.778 0.778 616704
weighted avg 1.000 1.000 1.000 616704
为什么 1 和 2 标签给出全为 0 分。 它应该给出 1 作为其余数据。谁能给我解释一下原因？
需要帮助。提前致谢！
解决方法
您的数据中似乎实际上没有第 1 类和第 2 类，因为这两个类的支持为零，但是由于您在传递给 flat_classification_report() 的标签列表中包含了第 1 类和第 2 类在计算各种指标时仍会考虑它们。
from sklearn_crfsuite import metrics
import numpy as np
np.random.seed(0)
cmin = 0
cmax = 8
labels = np.arange(1 + cmax)
print(np.unique(labels))
# [0 1 2 3 4 5 6 7 8]
y = np.random.randint(cmin,1 + cmax,1000).reshape(-1,1)
print(np.unique(y))
# [0 1 2 3 4 5 6 7 8]
# classification report when "y" takes on all the specified labels
print(metrics.flat_classification_report(y_true=y,y_pred=y,labels=labels,digits=3))
# precision recall f1-score support
# 0 1.000 1.000 1.000 117
# 1 1.000 1.000 1.000 106
# 2 1.000 1.000 1.000 106
# 3 1.000 1.000 1.000 132
# 4 1.000 1.000 1.000 110
# 5 1.000 1.000 1.000 115
# 6 1.000 1.000 1.000 104
# 7 1.000 1.000 1.000 109
# 8 1.000 1.000 1.000 101
# accuracy 1.000 1000
# macro avg 1.000 1.000 1.000 1000
# weighted avg 1.000 1.000 1.000 1000
# classification report when "y" takes on all the specified labels apart from 1 and 2,# but 1 and 2 are still included among the possible labels
y = y[np.logical_and(y != 1,y != 2)].reshape(-1,1)
print(np.unique(y))
# [0 3 4 5 6 7 8]
print(metrics.flat_classification_report(y_true=y,digits=3))
# precision recall f1-score support
# 0 1.000 1.000 1.000 117
# 1 0.000 0.000 0.000 0
# 2 0.000 0.000 0.000 0
# 3 1.000 1.000 1.000 132
# 4 1.000 1.000 1.000 110
# 5 1.000 1.000 1.000 115
# 6 1.000 1.000 1.000 104
# 7 1.000 1.000 1.000 109
# 8 1.000 1.000 1.000 101
# micro avg 1.000 1.000 1.000 788
# macro avg 0.778 0.778 0.778 788
# weighted avg 1.000 1.000 1.000 788
# classification report when "y" takes on all the specified labels apart from 1 and 2,# and 1 and 2 are not included among the possible labels
labels = labels[np.logical_and(labels != 1,labels != 2)]
print(np.unique(labels))
# [0 3 4 5 6 7 8]
print(metrics.flat_classification_report(y_true=y,digits=3))
# precision recall f1-score support
# 0 1.000 1.000 1.000 117
# 3 1.000 1.000 1.000 132
# 4 1.000 1.000 1.000 110
# 5 1.000 1.000 1.000 115
# 6 1.000 1.000 1.000 104
# 7 1.000 1.000 1.000 109
# 8 1.000 1.000 1.000 101
# accuracy 1.000 788
# macro avg 1.000 1.000 1.000 788
# weighted avg 1.000 1.000 1.000 788