带标签的sklearn图混淆矩阵 [英] sklearn plot confusion matrix with labels

问题描述

我想绘制一个混淆矩阵以可视化分类器的性能，但它仅显示标签的数字，而不显示标签本身:

I want to plot a confusion matrix to visualize the classifer's performance, but it shows only the numbers of the labels, not the labels themselves:

from sklearn.metrics import confusion_matrix
import pylab as pl
y_test=['business', 'business', 'business', 'business', 'business', 'business', 'business', 'business', 'business', 'business', 'business', 'business', 'business', 'business', 'business', 'business', 'business', 'business', 'business', 'business']

pred=array(['health', 'business', 'business', 'business', 'business',
       'business', 'health', 'health', 'business', 'business', 'business',
       'business', 'business', 'business', 'business', 'business',
       'health', 'health', 'business', 'health'], 
      dtype='|S8')

cm = confusion_matrix(y_test, pred)
pl.matshow(cm)
pl.title('Confusion matrix of the classifier')
pl.colorbar()
pl.show()

如何将标签(健康，业务等)添加到混乱矩阵中?

How can I add the labels (health, business..etc) to the confusion matrix?

推荐答案

如此问题中所述，则必须通过存储以下内容来打开" 下级艺术家API .您调用的matplotlib函数传递的图形和轴对象(下面的fig，ax和cax变量).然后，您可以使用set_xticklabels/set_yticklabels替换默认的x轴和y轴刻度:

As hinted in this question, you have to "open" the lower-level artist API, by storing the figure and axis objects passed by the matplotlib functions you call (the fig, ax and cax variables below). You can then replace the default x- and y-axis ticks using set_xticklabels/set_yticklabels:

from sklearn.metrics import confusion_matrix

labels = ['business', 'health']
cm = confusion_matrix(y_test, pred, labels)
print(cm)
fig = plt.figure()
ax = fig.add_subplot(111)
cax = ax.matshow(cm)
plt.title('Confusion matrix of the classifier')
fig.colorbar(cax)
ax.set_xticklabels([''] + labels)
ax.set_yticklabels([''] + labels)
plt.xlabel('Predicted')
plt.ylabel('True')
plt.show()

请注意，我将labels列表传递给了confusion_matrix函数，以确保其正确排序，与刻度线匹配.

Note that I passed the labels list to the confusion_matrix function to make sure it's properly sorted, matching the ticks.

这将导致下图:

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