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| from sklearn.metrics import confusion_matrix
import matplotlib.pyplot as plt
import numpy as np
def plot_confusion_matrix(cm, labels, title='Confusion Matrix', cmap=plt.cm.Blues):
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')
def showmatrix(y_true, y_pred, labels, titlename):
tick_marks = np.array(range(len(labels))) + 0.5
plt.rcParams['font.sans-serif'] = ['SimHei']
plt.rcParams['axes.unicode_minus'] = False
cm = confusion_matrix(y_true, y_pred)
np.set_printoptions(precision=2)
cm_normalized = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]
print(cm_normalized)
dd = np.zeros(len(cm_normalized))
for i in range(len(dd)):
dd[i] = cm_normalized[i, i]
print(dd)
max_c = cm_normalized.max()
min_c = cm_normalized.min()
print(sum(dd)/len(dd))
np.save('result/matrixdata.npy', dd)
plt.figure(figsize=(12, 8), dpi=120)
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:
if c > 0.5*(max_c+min_c):
plt.text(x_val, y_val, "%0.2f" % (c,), color='white', fontsize=10, va='center', ha='center')
else:
plt.text(x_val, y_val, "%0.2f" % (c,), color='black', fontsize=10, va='center', ha='center')
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=cm_normalized, title=titlename, labels=labels)
plt.savefig('result/confusionmatrix.svg', format='svg')
plt.show()
|