Matplotlib绘制混淆矩阵的实现

import itertools
import matplotlib.pyplot as plt
import numpy as np
# 绘制混淆矩阵
def plot_confusion_matrix(cm, classes, normalize=False, title='Confusion matrix', cmap=plt.cm.Blues):
    """
    - cm : 计算出的混淆矩阵的值
    - classes : 混淆矩阵中每一行每一列对应的列
    - normalize : True:显示百分比, False:显示个数
    """
    if normalize:
        cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]
        print("显示百分比：")
        np.set_printoptions(formatter={'float': '{: 0.2f}'.format})
        print(cm)
    else:
        print('显示具体数字：')
        print(cm)
    plt.imshow(cm, interpolation='nearest', cmap=cmap)
    plt.title(title)
    plt.colorbar()
    tick_marks = np.arange(len(classes))
    plt.xticks(tick_marks, classes, rotation=45)
    plt.yticks(tick_marks, classes)
    # matplotlib版本问题，如果不加下面这行代码，则绘制的混淆矩阵上下只能显示一半，有的版本的matplotlib不需要下面的代码，分别试一下即可
    plt.ylim(len(classes) - 0.5, -0.5)
    fmt = '.2f' if normalize else 'd'
    thresh = cm.max() / 2.
    for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):
        plt.text(j, i, format(cm[i, j], fmt),
                 horizontalalignment="center",
                 color="white" if cm[i, j] > thresh else "black")
    plt.tight_layout()
    plt.ylabel('True label')
    plt.xlabel('Predicted label')
    plt.show()

cnf_matrix = np.array([[8707, 64, 731, 164, 45],
                      [1821, 5530, 79, 0, 28],
                      [266, 167, 1982, 4, 2],
                      [691, 0, 107, 1930, 26],
                      [30, 0, 111, 17, 42]])
attack_types = ['Normal', 'DoS', 'Probe', 'R2L', 'U2R']

plot_confusion_matrix(cnf_matrix, classes=attack_types, normalize=True, title='Normalized confusion matrix')

plot_confusion_matrix(cnf_matrix, classes=attack_types, normalize=False, title='Normalized confusion matrix')