Seaborn中的clustermap的标签? [英] Labels for clustermap in seaborn?

问题描述

关于在seaborn中为clustermap进行标签的问题，我有几个问题.首先，可以提取用于层次聚类的距离值，并在树形结构可视化(可能只有前三个级别)上绘制该值.

I have several questions about labeling for clustermap in seaborn. First is it possible to extract the the distance values for the hierarchical clustering, and plot the value on the tree structure visualization (maybe only the first three levels).

这是我创建簇图图的示例代码:

Here is my example code for creating a clustermap plot:

import pandas as pd
import numpy as np
import seaborn as sns
get_ipython().magic(u'matplotlib inline')

m = np.random.rand(50, 50)
df = pd.DataFrame(m, columns=range(4123, 4173), index=range(4123, 4173))
sns.clustermap(df, metric="correlation")

其他两个问题是: -由于y标签重叠在一起，因此如何旋转.
-如何将颜色栏移至底部或右侧. (对于热图，有一个问题，但确实有不适合我的情况，也不能解决颜色 栏位置)

The other two questions are: - How to rotate the y labels since they overlaps together.
- How to move the color bar to the bottom or right. (There was a question for heatmap, but does not work for my case. Also does not address the color bar position)

推荐答案

我在旋转y轴上的标签时遇到了完全相同的问题，并找到了解决方案. 问题是，如果您按照引用的问题中的建议进行plt.yticks(rotation=0)操作，由于ClusterGrid的工作方式，它会旋转结肠上的标签.

I had the exact same issue with the labels on the y-axis being rotated and found a solution. The issue is that if you do plt.yticks(rotation=0) like suggested in the question you referenced, it will rotate the labels on your colobar due to the way ClusterGrid works.

要解决该问题并旋转正确的标签，您需要引用基础Heatmap中的Axes并对其进行旋转:

To solve it and rotate the right labels, you need to reference the Axes from the underlying Heatmap and rotate these:

cg = sns.clustermap(df, metric="correlation")
plt.setp(cg.ax_heatmap.yaxis.get_majorticklabels(), rotation=0)

关于色条放置的其他问题，如此指示，目前我不支持此功能不幸的是，Github问题.

For your other question about the colorbar placement, I don't think this is supported at the moment, as indicated by this Github issue unfortunately.

最后，对于分层聚类距离值，您可以使用以下命令访问行或列的链接矩阵:

And finally for the hierarchical clustering distance values, you can access the linkage matrics for rows or columns with:

cg = sns.clustermap(df, metric="correlation")
cg.dendrogram_col.linkage # linkage matrix for columns
cg.dendrogram_row.linkage # linkage matrix for rows

这篇关于Seaborn中的clustermap的标签?的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持IT屋！