使用sklear.metrics计算precision等指标

在Precision、Recall、F1-score、Micro-F1、Macro-F1、Recall K文章中介绍了一些分类指标的理论 计算方式。在本文中将介绍使用sklearn.metrics库来计算这些指标

sklearn.metrics.accuracy_score(y_true, y_pred, normalize True, sample_weight None)

y_true: 真实标签
y_pred: 预测标签
normalize: 若为True结果返回准确率 若为False结果返回分类正确的样本个数
注意 不支持多标签

 import numpy as np
 from sklearn.metrics import accuracy_score
 y_pred [0, 2, 1, 3]
 y_true [0, 1, 2, 3]
 rate accuracy_score(y_true, y_pred)
 num accuracy_score(y_true, y_pred, normalize False)
 print( rate: , rate)
 print( num: , num)

输出

rate: 0.5
num: 2

sklearn.metrics.classification_report(y_true, y_pred, labels None, target_names None, sample_weight None, digits 2)

labels: 标签的索引
target_names: 标签的名字 与labels对应
digits: 小数点位数

 from sklearn.metrics import classification_report
 y_true [0, 1, 2, 2, 2]
 y_pred [0, 0, 2, 2, 1]
 target_names [ class 0 , class 1 , class 2 ]
 print(classification_report(y_true, y_pred, target_names target_names))

输出

 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
 accuracy 0.60 5
 macro avg 0.50 0.56 0.49 5
weighted avg 0.70 0.60 0.61 5

注意 当labels为None时 target_name中的名字默认将labels视为label_index从小到大排序 并与之对应。当指定labels参数时 target_names中名字与labels中索引对应。将上面代码最后一行改为

print(classification_report(y_true, y_pred, labels [0, 2, 1], target_names target_names))

输出

 precision recall f1-score support
 class 0 0.50 1.00 0.67 1
 class 1 1.00 0.67 0.80 3
 class 2 0.00 0.00 0.00 1
 accuracy 0.60 5
 macro avg 0.50 0.56 0.49 5
weighted avg 0.70 0.60 0.61 5

可以看到class1和class2的输出与上面的例子换了下

sklearn.metrics.f1_score(y_true, y_pred, labels None, pos_label 1, average binary , sample_weight None)

labels: 标签的索引
pos_label: 作用不大
average: 可选[None, ‘binary’ (default), ‘micro’, ‘macro’, ‘samples’, ‘weighted’]之一 如果指定binary结果只返回二分类中的正样本的分数 指定其他分别计算micro-f1 macro-f1 如果是None返回每一个类别的f1-score 是一个列表 若是weighted 先计算每个类别的f1 然后根据每个类别样本数目加权平均

from sklearn.metrics import f1_score, confusion_matrix
y_true [0, 1, 2, 0, 1, 2]
y_pred [0, 2, 1, 0, 0, 1]
matrix confusion_matrix(y_true, y_pred, labels [0, 1, 2])
macro_f1 f1_score(y_true, y_pred, average macro ) 
micro_f1 f1_score(y_true, y_pred, average micro ) 
weight_f1 f1_score(y_true, y_pred, average weighted ) 
all_f1 f1_score(y_true, y_pred, average None)
print( matrix: , matrix)
print( macro: , macro_f1)
print( micro: , micro_f1)
print( weight: , weight_f1)
print( all: , all_f1)

matrix: [[2 0 0]
 [1 0 1]
 [0 2 0]]
macro: 0.26666666666666666
micro: 0.3333333333333333
weight: 0.26666666666666666
all: [0.8 0. 0. ]