Question

我有两个数组：

correct = [['*','*'],['*','PER','*','GPE','ORG'],['GPE','*','*','*','ORG']]
predicted = [['PER','*'],['*','ORG','*','GPE','ORG'],['PER','*','*','*','MISC']]

正确和预测的长度相同（10K +）两个阵列中每个位置元素的长度也具有相同的长度。我想用python计算这两个数组的精度，召回率和f1分数。我有以下6个班级：＆＃39; PER＆＃39;＆＃39; ORG＆＃39;＆＃39; MISC＆＃39;＆＃39; LOC＆＃39;＆＃39; *＆＃39;，＆＃39; GPE＆＃39;

想要计算5个班级的精确度和召回率（除了＆＃39; *＆＃39;）也要找到f1得分。什么是使用python执行此操作的有效方法？

Answer 1

您必须按照here展示您的列表，然后使用scikit-learn中的classification_report：

correct = [['*','*'],['*','PER','*','GPE','ORG'],['GPE','*','*','*','ORG']]
predicted = [['PER','*'],['*','ORG','*','GPE','ORG'],['PER','*','*','*','MISC']]
target_names = ['PER','ORG','MISC','LOC','GPE'] # leave out '*'

correct_flat = [item for sublist in correct for item in sublist]
predicted_flat = [item for sublist in predicted for item in sublist]

from sklearn.metrics import classification_report
print(classification_report(correct_flat, predicted_flat, target_names=target_names))

结果：

             precision    recall  f1-score   support

        PER       1.00      0.86      0.92         7
        ORG       1.00      0.50      0.67         2
       MISC       0.00      0.00      0.00         0
        LOC       0.50      0.50      0.50         2
        GPE       0.00      0.00      0.00         1

avg / total       0.83      0.67      0.73        12

在此特定示例中，您还会收到警告：

UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples.

这是由于'MISC'不存在于此处的真实标签中（correct），但可以说这不应该发生在您的真实数据中。

如何计算两个嵌套数组的精度和召回率

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