使用matplot绘制混淆矩阵

使用matplot绘制混淆矩阵.py

import itertools
import matplotlib.pyplot as plt
import numpy as np
plt.rcParams['font.family'] = 'sans-serif'
plt.rcParams['font.sans-serif']=['Tahoma', 'DejaVu Sans',
                               'Lucida Grande', 'Verdana']
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()
    
  if __name__ == '__main__':
    cnf_matrix = np.load('/data2/guoyu/workspace/horizon_classfication/results/confusion_matrix.npy')
    # cnf_matrix.shape == (3,3) in this example    
    class_names = ['run','stand', 'walk']
    # Plot normalized confusion matrix
    plt.figure()
    plot_confusion_matrix(cnf_matrix, classes=class_names, normalize=True,
                          title='Normalized confusion matrix')