python 混淆矩阵的画法

from sklearn.metrics import confusion_matrix
import matplotlib.pyplot as plt
import numpy as np
import torch

def plot_confusion_matrix(cm,savename,classes,title='Confusion Matrix',normalize=True):

    plt.figure(figsize=(12,8),dpi=100)
    np.set_printoptions(precision=2)

    # 在混淆矩阵中每格的概率值
    ind_array = np.arange(len(classes))
    x,y = np.meshgrid(ind_array,ind_array)

    if normalize:
        #显示百分比
        cm = np.array(cm,dtype=float)/np.sum(cm,axis=1)
        for x_val,y_val in zip(x.flatten(),y.flatten()):
            c = cm[y_val][x_val]
            # c = cm[y_val][x_val]/np.sum(cm[y_val])
            if c>0.001:
                plt.text(x_val, y_val, "%0.2f" % (c,), color='black', fontsize=10, va='center', ha='center')
    else:
        #显示数量
        for x_val,y_val in zip(x.flatten(),y.flatten()):
            c = cm[y_val][x_val]
            # c = cm[y_val][x_val]/np.sum(cm[y_val])
            if c>0.001:
                plt.text(x_val, y_val, "%0.2f" % (c,), color='black', fontsize=10, va='center', ha='center')

    # plt.imshow(cm,interpolation='nearest',cmap=plt.cm.binary)
    plt.imshow(cm,interpolation='nearest',cmap=plt.cm.Blues)
    plt.title(title)
    plt.colorbar()
    xlocations = np.array(range(len(classes)))
    plt.xticks(xlocations, classes, rotation=90)
    plt.yticks(xlocations, classes)
    plt.ylabel('Actual label')
    plt.xlabel('Predict label')

    #offset the tick
    # offset the tick
    tick_marks = np.array(range(len(classes))) + 0.5
    plt.gca().set_xticks(tick_marks, minor=True)
    plt.gca().set_yticks(tick_marks, minor=True)
    plt.gca().xaxis.set_ticks_position('none')
    plt.gca().yaxis.set_ticks_position('none')
    plt.grid(True, which='minor', linestyle='-')
    plt.gcf().subplots_adjust(bottom=0.15)

    #show confusion matrix
    # plt.savefig(savename, format='png')
    plt.show()


if __name__ == '__main__':
    # classes表示不同类别的名称，比如这有6个类别
    path="..\\data\\confusion_matrix.npy"
    contents = np.load(path,allow_pickle=True)
    contents = contents.tolist();

    y_true = contents["y_true"]
    y_pred=  contents["y_pre"]

    # y_true = torch.cat(y_true).detach().numpy()
    # y_pre  = torch.cat(y_pre).detach().numpy()


    classes = ['A', 'B', 'C', 'D', 'E', 'F','G','H','I','J']
    # random_numbers = np.random.randint(6, size=50)  # 6个类别，随机生成50个样本
    # y_true = random_numbers.copy()  # 样本实际标签
    # random_numbers[:10] = np.random.randint(6, size=10)  # 将前10个样本的值进行随机更改
    # y_pred = random_numbers  # 样本预测标签
    
    #获取混淆矩阵
    cm = confusion_matrix(y_true, y_pred)
    plot_confusion_matrix(cm,'confusion_matrix.png',classes, title='confusion matrix')