目录

1、混淆矩阵

2、代码实现

2.1、OA

2.2、AA 

2.3、kappa

2.4、用户精度(User Accuracy, UA)

2.5、生产者精度(Producer Accuracy, PA)

2.6、f1_score

2.7、混淆矩阵

2.8、分类报告

---

1、混淆矩阵

以二分类问题为例，混淆矩阵表现形式如下：

• TP(True Positive，真正例)：将真实情况为正例的类型正确地预测为正例
• FN(False Negative，假反例)：将真实情况为正例的类型错误地预测为反例
• FP(False Positive，假正例)：将真实情况为反例的类型错误地预测为正例
• TN(True Negative，真反例)：将真实情况为反例的类型正确地预测为反例

2、代码实现

2.1、OA

• 总体准确度（OA）—表示总测试样本中正确分类的样本数

import numpy as np
from sklearn.metrics import accuracy_score

y_test = np.array([0,1,1,2,2,0,0,1,2,3,3,2,3,1,0,3,1,2,3,3,2,0,])
y_predict = np.array([1,1,0,2,2,2,0,2,0,3,0,2,3,1,0,2,1,2,1,3,1,3,])

# 也叫准确率
OA = accuracy_score(y_test, y_predict)
print(OA)

2.2、AA 

• 平均准确度（AA）—表示分类精度的平均值

import numpy as np
from sklearn.metrics import recall_score

y_test = np.array([0,1,1,2,2,0,0,1,2,3,3,2,3,1,0,3,1,2,3,3,2,0,])
y_predict = np.array([1,1,0,2,2,2,0,2,0,3,0,2,3,1,0,2,1,2,1,3,1,3,])

# 当average=None时，计算的是每一个类别的精度。也相当于生产者精度
# 也叫召回率
AA = recall_score(y_test, y_predict,average='macro')
print(AA)

2.3、kappa

• kappa系数（kappa）提供了关于地面真值图和分类图之间的强一致性的相互信息

import numpy as np
from sklearn.metrics import cohen_kappa_score

y_test = np.array([0,1,1,2,2,0,0,1,2,3,3,2,3,1,0,3,1,2,3,3,2,0,])
y_predict = np.array([1,1,0,2,2,2,0,2,0,3,0,2,3,1,0,2,1,2,1,3,1,3,])

kappa = cohen_kappa_score(y_test, y_predict)
print(kappa)

2.4、用户精度(User Accuracy, UA)

import numpy as np
from sklearn.metrics import precision_score

y_test = np.array([0,1,1,2,2,0,0,1,2,3,3,2,3,1,0,3,1,2,3,3,2,0,])
y_predict = np.array([1,1,0,2,2,2,0,2,0,3,0,2,3,1,0,2,1,2,1,3,1,3,])

# 当average='macro'时，计算的是平均的用户精度（UA）
# 也叫精确率
UA = precision_score(y_test, y_predict,average=None)
print(UA)

2.5、生产者精度(Producer Accuracy, PA)

import numpy as np
from sklearn.metrics import recall_score

y_test = np.array([0,1,1,2,2,0,0,1,2,3,3,2,3,1,0,3,1,2,3,3,2,0,])
y_predict = np.array([1,1,0,2,2,2,0,2,0,3,0,2,3,1,0,2,1,2,1,3,1,3,])

# 当average='macro'时，计算的是平均的生产者精度（PA）。也相当于是每个类别的精度
PA = recall_score(y_test, y_predict,average=None)
print(PA)

2.6、f1_score

import numpy as np
from sklearn.metrics import f1_score

y_test = np.array([0,1,1,2,2,0,0,1,2,3,3,2,3,1,0,3,1,2,3,3,2,0,])
y_predict = np.array([1,1,0,2,2,2,0,2,0,3,0,2,3,1,0,2,1,2,1,3,1,3,])

# 当average=None时，计算的每个类别的f1_score
f1_s = f1_score(y_test, y_predict,average="macro")
print(f1_s)

2.7、混淆矩阵

import numpy as np
from sklearn.metrics import confusion_matrix

y_test = np.array([0,1,1,2,2,0,0,1,2,3,3,2,3,1,0,3,1,2,3,3,2,0,])
y_predict = np.array([1,1,0,2,2,2,0,2,0,3,0,2,3,1,0,2,1,2,1,3,1,3,])

matrix = confusion_matrix(y_test, y_predict)
print(matrix)

2.8、分类报告

import numpy as np
from sklearn.metrics import classification_report

y_test = np.array([0,1,1,2,2,0,0,1,2,3,3,2,3,1,0,3,1,2,3,3,2,0,])
y_predict = np.array([1,1,0,2,2,2,0,2,0,3,0,2,3,1,0,2,1,2,1,3,1,3,])

report = classification_report(y_test, y_predict, digits=4)
print(report)