混淆矩阵代码

混淆矩阵对数据可视化很有用，我在做轴承故障诊断时，也用到了这个混淆矩阵，我在下面贴出来两种代码，供大家使用借鉴，我也是从网上找的改的

# 开发时间：2022/8/4  19:35
#  绘制混淆矩阵
import torch
from matplotlib import pyplot as plt
import numpy as np
import itertools




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`.
    Input
    - 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("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, cm[i, j].numpy(),
                 horizontalalignment="center",
                 color="white" if cm[i, j] > thresh else "black")
    plt.tight_layout()
    plt.ylabel('True label')
    plt.xlabel('Predicted label')


# 计算混淆矩阵
def confusion_matrix(preds, labels, conf_matrix):
    preds = torch.argmax(preds, 1)
    for p, t in zip(preds, labels):
        conf_matrix[t, p] += 1

    return conf_matrix

# 混淆矩阵定义
def plot_confusion_matrix(cm, classes,title='Confusion matrix',cmap=plt.cm.jet):
    cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]
    plt.imshow(cm, interpolation='nearest', cmap=cmap)
    plt.rcParams['font.sans-serif'] = ['SimHei']  # 用来正常显示中文标签
    plt.rcParams['axes.unicode_minus'] = False  # 用来正常显示负号
    plt.colorbar()
    tick_marks = np.arange(len(classes))
    plt.xticks(tick_marks,('good','bad'))
    plt.yticks(tick_marks,('good','bad'))
    thresh = cm.max() / 2.
    for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):
        plt.text(j, i, '{:.2f}'.format(cm[i, j]), horizontalalignment="center",color="white" if cm[i, j] > thresh else "black")
    plt.tight_layout()
    plt.ylabel('真实类别')
    plt.xlabel('预测类别')
    plt.savefig('test_xx.png', dpi=200, bbox_inches='tight', transparent=False)
    plt.show()
 
# 显示混淆矩阵
def plot_confuse(model, x_val, y_val):
    predictions = model.predict_classes(x_val)
    truelabel = y_val.argmax(axis=-1)   # 将one-hot转化为label
    conf_mat = confusion_matrix(y_true=truelabel, y_pred=predictions)
    plt.figure()
    plot_confusion_matrix(conf_mat, range(np.max(truelabel)+1))