from sklearn.metrics import confusion_matrix

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

def plot_confusion_matrix(cm,
                          target_names,
                          title='Confusion matrix',
                          cmap=plt.cm.Reds,#设置混淆矩阵的颜色主题
                          normalize=True):
   
 
    accuracy = np.trace(cm) / float(np.sum(cm))
    misclass = 1 - accuracy

    if cmap is None:
        cmap = plt.get_cmap('Blues')

    plt.figure(figsize=(15, 12))
    plt.imshow(cm, interpolation='nearest', cmap=cmap)
    plt.title(title)
    plt.colorbar()

    if target_names is not None:
        tick_marks = np.arange(len(target_names))
        plt.xticks(tick_marks, target_names, rotation=45)
        plt.yticks(tick_marks, target_names)

    if normalize:
        cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]


    thresh = cm.max() / 1.5 if normalize else cm.max() / 2
    for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):
        if normalize:
            plt.text(j, i, "{:0.4f}".format(cm[i, j]),
                     horizontalalignment="center",
                     color="white" if cm[i, j] > thresh else "black")
        else:
            plt.text(j, i, "{:,}".format(cm[i, j]),
                     horizontalalignment="center",
                     color="white" if cm[i, j] > thresh else "black")


    plt.tight_layout()
    plt.ylabel('True label')
    plt.xlabel('Predicted label\naccuracy={:0.4f}; misclass={:0.4f}'.format(accuracy, misclass))
   
    plt.show()

def plot_confuse(model, x_val, y_val, labels):
    predictions = model.predict_classes(x_val,batch_size=batch)
    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, normalize=False,target_names=labels,title='Confusion Matrix')