如何在子图中显示所有标签。我有 12 个标签，我必须绘制一个混淆矩阵。但其中只有 6 个可见

Question

我想绘制一个包含 12 个数据的混淆矩阵，所以我制作了 12 个标签来绘制混淆矩阵，12 个数据的绘图正确，但 x 标签和 y 标签只显示了一半。

我用过这个sn-p--：

import matplotlib.pyplot as plt

labels = ['1','2','3','4','5','6','7','8','9','10','11','12']
cm = confusion_matrix(actualList, predictList, labels)
print(cm)
fig = plt.figure()
fig.set_figheight(10)
fig.set_figwidth(10)
ax = fig.add_subplot()
cax = ax.matshow(cm)
plt.title('Confusion matrix of the classifier',pad=-570)
fig.colorbar(cax)
ax.set_xticklabels([''] + labels)
ax.set_yticklabels([''] + labels)
plt.setp(ax.get_xticklabels(), rotation=30, ha="left",
         rotation_mode="anchor")
plt.xlabel('Predicted')
plt.ylabel('True')
plt.show()

得到这个输出：

Answer 1

当您有多个类别时，matplotlib 将错误地标记轴。为了解决这个问题，您可以从 matplotlib.ticker 导入 MultipleLocator 来强制标记每个单元格。

import matplotlib.pyplot as plt
from matplotlib.ticker import MultipleLocator;

# the same values in your confusion matrix
labels = ['1','2','3','4','5','6','7','8','9','10','11','12']
cm = [[0, 0, 61, 0, 0, 0, 0, 0, 0, 0, 0, 0],
    [0, 0, 16, 0, 0, 0, 0, 0, 0, 0, 0, 0],
    [0, 0, 1099, 0, 0, 0, 0, 0, 0, 0, 0, 0],
    [0, 0, 131, 23, 0, 0, 0, 0, 0, 0, 0, 0],
    [0, 0, 36, 0, 0, 0, 0, 0, 0, 0, 0, 0],
    [0, 0, 40, 0, 0, 3, 0, 0, 0, 0, 0, 0],
    [0, 0, 43, 0, 0, 0, 31, 0, 0, 0, 0, 0],
    [0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0],
    [0, 0, 269, 0, 0, 0, 0, 0, 86, 0, 0, 6],
    [0, 0, 101, 0, 0, 0, 0, 0, 0, 45, 0, 1],
    [0, 0, 10, 0, 0, 0, 0, 0, 0, 0, 0, 0],
    [0, 0, 283, 0, 0, 0, 0, 0, 0, 0, 0, 204]]

fig = plt.figure()
fig.set_figheight(10)
fig.set_figwidth(10)
ax = fig.add_subplot()
cax = ax.matshow(cm)
plt.title('Confusion matrix of the classifier',pad=-570)
fig.colorbar(cax)

ax.xaxis.set_major_locator(MultipleLocator(1))
ax.yaxis.set_major_locator(MultipleLocator(1))

ax.set_xticklabels([''] + labels)
ax.set_yticklabels([''] + labels)
plt.setp(ax.get_xticklabels(), rotation=30, ha="left",
         rotation_mode="anchor")
plt.xlabel('Predicted')
plt.ylabel('True')
plt.show()