trainning.py 文件源码

def get_confusion(y_test, y_pred):
    cm = confusion_matrix(y_test, y_pred)
    cm_normalized = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]

    label_unique = y_test.unique()
#    #Graph Confusion Matrix
    tick_marks = np.arange(len(label_unique))
#    plt.figure(figsize=(8,6))
    sns.heatmap(cm_normalized, cmap='Greens',annot=True,linewidths=.5)
#    plt.title('confusion matrix')
    plt.xlabel('Predicted label')
    plt.ylabel('True label')
    plt.xticks(tick_marks + 0.5, list(label_unique))
    plt.yticks(tick_marks + 0.5,list(reversed(list(label_unique))) , rotation=0)
#    
#    plt.imshow(cm_normalized, interpolation='nearest', cmap='Greens')
#    plt.title('confusion matrix')
#    plt.colorbar()
#    tick_marks = np.arange(len(label_unique))
#    plt.xticks(tick_marks + 0.5, list(reversed(list(label_unique))))
#    plt.yticks(tick_marks + 0.5,list(label_unique) , rotation=0)
#    plt.tight_layout()
#    plt.ylabel('True label')
#    plt.xlabel('Predicted label')