在距离高度从 SciPy 切割树状图/聚类树

Posted 2023-02-19

【中文标题】在距离高度从 SciPy 切割树状图/聚类树

【英文标题】：Cutting Dendrogram/Clustering Tree from SciPy at distance height

【发布时间】：2016-07-31 03:28:52

【问题描述】：

我正在尝试学习如何在 Python 中使用 dendrograms 使用 SciPy 。我想获得集群并能够可视化它们；我听说hierarchical clustering 和dendrograms 是最好的方法。

如何在特定距离“砍”树？

在这个例子中，我只想在距离1.6 处切割它

我在https://joernhees.de/blog/2015/08/26/scipy-hierarchical-clustering-and-dendrogram-tutorial/#Inconsistency-Method 上查找了一个教程，但是这家伙使用**kwargs 做了一些非常令人困惑的包装函数（他称他的阈值max_d）

这是我的代码和下面的情节；为了重现性，我尝试尽可能地对其进行注释：

from __future__ import print_function
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from scipy.cluster.hierarchy import dendrogram,linkage,fcluster
from scipy.spatial import distance
np.random.seed(424173239) #43984

#Dims
n,m = 20,7

#DataFrame: rows = Samples, cols = Attributes
attributes = ["a" + str(j) for j in range(m)]
DF_data = pd.DataFrame(np.random.random((n, m)), columns = attributes)

A_dist = distance.cdist(DF_data.as_matrix().T, DF_data.as_matrix().T)

#(i) . Do the labels stay in place from DF_data for me to do this? 
DF_dist = pd.DataFrame(A_dist, index = attributes, columns = attributes)

#Create dendrogram
fig, ax = plt.subplots()
Z = linkage(distance.squareform(DF_dist.as_matrix()), method="average")
D_dendro = dendrogram(Z, labels = attributes, ax=ax) #create dendrogram dictionary
threshold = 1.6 #for hline
ax.axhline(y=threshold, c='k')
plt.show()

#(ii) How can I "cut" the tree by giving it a distance threshold?
#i.e. If I cut at 1.6 it would make (a5 : cluster_1 or not in a cluster), (a2,a3 : cluster_2), (a0,a1 : cluster_3), and (a4,a6 : cluster_4)

#link_1 says use fcluster
#This -> fcluster(Z, t=1.5, criterion='inconsistent', depth=2, R=None, monocrit=None)
#gives me -> array([1, 1, 1, 1, 1, 1, 1], dtype=int32)

print(
     len(set(D_dendro["color_list"])), "^ # of colors from dendrogram",
     len(D_dendro["ivl"]), "^ # of labels",sep="\n")
#3 
#^ # of colors from dendrogram it should be 4 since clearly (a6, a4) and a5 are in different clusers
#7
#^ # of labels

link_1：How to compute cluster assignments from linkage/distance matrices in scipy in Python?

【问题讨论】：

Related question...

@SaulloCastro 对此表示感谢。是的，肯定有关系。一种有趣的方式，只通过水平移动树木。看看实际的图表是如何绘制的也很酷。

【参考方案1】：

color_threshold 是我正在寻找的方法。当color_palette 对于生成的集群数量来说太小时，它并没有真正的帮助。如果有人可以提供帮助，请将下一步迁移到 Bigger color-palette in matplotlib for SciPy's dendrogram (Python)。

【参考方案2】：

对于更大的调色板，这应该可以：

from scipy.cluster import hierarchy as hc
import matplotlib.cm as cm
import matplotlib.colors as col

#get a color spectrum "gist_ncar" from matplotlib cm. 
#When you have a spectrum it begins with 0 and ends with 1. 
#make tinier steps if you need more than 10 colors

colors = cm.gist_ncar(np.arange(0, 1, 0.1)) 

colorlst=[]# empty list where you will put your colors
for i in range(len(colors)): #get for your color hex instead of rgb
    colorlst.append(col.to_hex(colors[i]))

hc.set_link_color_palette(colorlst) #sets the color to use.

把所有这些放在你的代码前面，它应该可以工作