python制表混淆矩阵 [英] python tabulating confusion matrix

问题描述

在我的 sklearn 逻辑回归模型中，我使用 metrics.confusion_matrix 命令获得了一个混淆矩阵.数组看起来像这样

array([[51, 0],[26, 0]])

忽略模型表现非常糟糕的事实，我试图了解以漂亮方式制表此矩阵的最佳方法是什么

我正在尝试使用

In my sklearn logistic regression model, I obtained a confusion matrix using metrics.confusion_matrix command. The array looks like this

array([[51,  0],
   [26,  0]])

Ignoring the fact that the model did pretty bad, I am trying to understand what is the best way to tabulate this matrix in pretty way

I am trying to use tabulate package and this code partially works for me

print tabulate(cm,headers=['Pred True', 'Pred False']) 

as it gives output

  Pred True    Pred False
-----------  ------------
     51             0
     26             0

EDIT

TO insert row names, I realized inserting elements rather than zip would help

cm_list=cm.tolist()
cm_list[0].insert(0,'Real True')
cm_list[1].insert(0,'Real False')
print tabulate(cm_list,headers=['Real/Pred','Pred True', 'Pred False'])

as it gives

Real/Pred      Pred True    Pred False
-----------  -----------  ------------
Real True             51             0
Real False            26             0

However, would still like to know if there is a quicker or alternate way of beautifying confusion matrix. (I found some plotting examples on web but I do not need that)

Thanks,

解决方案

Have you considered creating a figure rather than a table? Adapting some code from a scikit-learn example you can get a decent looking figure which shows what you want.

import numpy as np
from matplotlib import pyplot as plt

def plot_confusion_matrix(cm, target_names, title='Confusion matrix', cmap=plt.cm.Blues):
    plt.imshow(cm, interpolation='nearest', cmap=cmap)
    plt.title(title)
    plt.colorbar()
    tick_marks = np.arange(len(target_names))
    plt.xticks(tick_marks, target_names, rotation=45)
    plt.yticks(tick_marks, target_names)
    plt.tight_layout()

    width, height = cm.shape

    for x in xrange(width):
        for y in xrange(height):
            plt.annotate(str(cm[x][y]), xy=(y, x), 
                        horizontalalignment='center',
                        verticalalignment='center')
    plt.ylabel('True label')
    plt.xlabel('Predicted label')

cm = np.array([[13,  0,  0],[ 0, 10,  6],[ 0,  0,  9]])
plot_confusion_matrix(cm, ['A', 'B', 'C'])

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