利用python中matplotlib打印混淆矩阵

前面说过混淆矩阵是我们在处理分类问题时，很重要的指标，那么如何更好的把混淆矩阵给打印出来呢，直接做表或者是前端可视化，小编曾经就尝试过用前端（D5）做出来，然后截图，显得不那么好看。。

代码：

import itertools
import matplotlib.pyplot as plt
import numpy as np

def plot_confusion_matrix(cm, classes,
                          normalize=False,
                          title='Confusion matrix',
                          cmap=plt.cm.Blues):
    """
    This function prints and plots the confusion matrix.
    Normalization can be applied by setting `normalize=True`.
    """
    if normalize:
        cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]
        print("Normalized confusion matrix")
    else:
        print('Confusion matrix, without normalization')

    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)

    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()
    # plt.savefig('confusion_matrix',dpi=200)

cnf_matrix = np.array([
    [4101, 2, 5, 24, 0],
    [50, 3930, 6, 14, 5],
    [29, 3, 3973, 4, 0],
    [45, 7, 1, 3878, 119],
    [31, 1, 8, 28, 3936],
])

class_names = ['Buildings', 'Farmland', 'Greenbelt', 'Wasteland', 'Water']

# plt.figure()
# plot_confusion_matrix(cnf_matrix, classes=class_names,
#                       title='Confusion matrix, without normalization')

# Plot normalized confusion matrix
plt.figure()
plot_confusion_matrix(cnf_matrix, classes=class_names, normalize=True,
                      title='Normalized confusion matrix')

在放矩阵位置，放一下你的混淆矩阵就可以，当然可视化混淆矩阵这一步也可以直接在模型运行中完成