相同的测试和预测值给出 0 精度、召回率和 NER 的 f1 分数
[英]Same test and prediction values gives 0 precision, recall, f1 score for NER
问题描述
I was using sklearns crfsuite to compute the f1, precision, and recall scores but there is an anomaly.
我使用 sklearns crfsuite 来计算 f1、精度和召回分数，但出现异常。 For just testing purposes I gave the same test and prediction values.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
Why 1 and 2 labels are giving all 0 scores.为什么 1 和 2 个标签都给出 0 分。 It should give 1 as the rest of the data.它应该给出 1 作为数据的 rest。 Can anyone explain to me the reason?谁能给我解释一下原因？
Need help.需要帮忙。 Thanks in advance!提前致谢！
1 个解决方案
解决方案1
1 已采纳 2021-04-18 17:23:11
It seems that you don't actually have classes 1 and 2 in your data as the support of these two classes is zero, but since you have included classes 1 and 2 in the list of labels passed to flat_classification_report() they are still considered in the calculation of the various metrics.似乎您的数据中实际上没有类 1 和 2，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类 1 和 2，因此它们仍然被考虑在各种指标的计算。
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, y_pred=y, labels=labels, 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, y_pred=y, labels=labels, 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
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