[英]2 confusion matrix, 1 colorbar
I would like to produce two confusion matrix and only show one colorbar. 我想产生两个混淆矩阵,只显示一个色标。 I am basically trying to merge this scikit-learn code with this answer . 我基本上是试图将此scikit-learn代码与此答案合并。
My code looks like this: 我的代码如下所示:
import numpy as np
import matplotlib.pyplot as plt
fig, axes = plt.subplots(nrows=1, ncols=2)
classes = ["A", "B"]
for i, ax in enumerate(axes.flat):
cm = np.random.random((2,2))
im = ax.imshow(cm, vmin=0, vmax=1)
plt.title("Title {}".format(i))
tick_marks = np.arange(2)
plt.xticks(tick_marks, classes, rotation=45)
plt.yticks(tick_marks, classes)
for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):
plt.text(j, i, format(cm[i, j], '.5f'),
horizontalalignment="center",
color="white")
plt.ylabel('True label')
plt.xlabel('Predicted label')
plt.tight_layout()
fig.subplots_adjust(right=0.8)
cbar_ax = fig.add_axes([0.88, 0.15, 0.05, 0.6])
fig.colorbar(im, cax=cbar_ax)
plt.show()
but this is the result: 但这是结果:
So everything is being plotted on the last image. 因此,所有内容都绘制在最后一张图像上。 Two questions: 两个问题:
All your elements are plotted on the last image because you are mixing up the pyplot
( plt.xxxxx()
) interface with the object-oriented interface. 您将所有元素绘制在最后一张图像上,因为您将pyplot
( plt.xxxxx()
)接口与面向对象的接口混合在一起。 Refer to this question or this one for some explanations. 请参考这个问题,或者这个对于一些解释。
For the colorbar, there are many ways to get a properly sized colorbar (eg playing with GridSpec , AxisDivider as suggested by @DavidG). 对于颜色条,有很多方法可以获取适当大小的颜色条 (例如,使用@DavidG建议的GridSpec和AxisDivider进行播放)。 Because you have two axes using imshow
, I would recommend using ImageGrid
instead, as per this answer to a similar question . 因为您有两个使用imshow
轴,所以根据类似问题的答案 ,我建议您改用ImageGrid
。
Your code should read: 您的代码应为:
import itertools
from mpl_toolkits.axes_grid1 import ImageGrid
classes = ["A", "B"]
fig = plt.figure()
grid = ImageGrid(fig, 111, # as in plt.subplot(111)
nrows_ncols=(1,2),
axes_pad=0.15,
cbar_location="right",
cbar_mode="single",
cbar_size="7%",
cbar_pad=0.15,
)
for i, ax in enumerate(grid[:2]):
cm = np.random.random((2,2))
im = ax.imshow(cm, vmin=0, vmax=1)
ax.set_title("Title {}".format(i)) # ax.___ instead of plt.___
tick_marks = np.arange(2)
ax.set_xticks(tick_marks) # Warning: different signature for [x|y]ticks in pyplot and OO interface
ax.set_xticklabels(classes, rotation=45)
ax.set_yticks(tick_marks)
ax.set_yticklabels(classes)
for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):
ax.text(j, i, format(cm[i, j], '.5f'),
horizontalalignment="center",
color="white")
ax.set_ylabel('True label')
ax.set_xlabel('Predicted label')
fig.tight_layout()
fig.subplots_adjust(right=0.8)
fig.colorbar(im, cax=ax.cax)
plt.show()
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