


#y_true代表真实的label值 y_pred代表预测得到的lavel值


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

# load labels.
labels = ['A', 'B', 'C', 'F']

y_true = [2, 0, 2, 2]
y_pred = [0, 0, 2, 2]
# load true and predict labels.

tick_marks = np.array(range(len(labels))) + 0.5


def plot_confusion_matrix(cm, title='Confusion Matrix', cmap=plt.cm.binary):
    plt.imshow(cm, interpolation='nearest', cmap=cmap)
    plt.title(title)
    plt.colorbar()
    xlocations = np.array(range(len(labels)))
    plt.xticks(xlocations, labels, rotation=90)
    plt.yticks(xlocations, labels)
    plt.ylabel('True label')
    plt.xlabel('Predicted label')


cm = confusion_matrix(y_true, y_pred)
print(cm)
np.set_printoptions(precision=2)
cm_normalized = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]
print(cm_normalized)
plt.figure(figsize=(12, 8), dpi=120)
# set the fontsize of label.
# for label in plt.gca().xaxis.get_ticklabels():
#    label.set_fontsize(8)
# text portion
ind_array = np.arange(len(labels))
x, y = np.meshgrid(ind_array, ind_array)

for x_val, y_val in zip(x.flatten(), y.flatten()):
    c = cm_normalized[y_val][x_val]
    if (c > 0.01):
        plt.text(x_val, y_val, "%0.2f" % (c,), color='red', fontsize=7, va='center', ha='center')
# offset the tick
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)

plot_confusion_matrix(cm_normalized, title='Normalized confusion matrix')
# show confusion matrix
plt.show()