Python 中 SciPy 树状图的自定义簇颜色（link_color_func？）

Question

我想用我以字典形式制作的彩色图（即 {leaf: color}）为我的聚类着色。

我试过按照 https://joernhees.de/blog/2015/08/26/scipy-hierarchical-clustering-and-dendrogram-tutorial/ 进行操作，但由于某种原因颜色弄乱了。默认图看起来不错，我只是想以不同方式分配这些颜色。我看到有一个 link_color_func，但是当我尝试使用我的颜色映射（D_leaf_color 字典）时，我得到了一个错误 b/c 它不是一个函数。我创建了 D_leaf_color 来自定义与特定簇相关联的叶子的颜色。在我的实际数据集中，颜色具有某种意义，因此我避免了任意颜色分配。

我不想在我的实际数据中使用 color_threshold b/c，我有更多的簇并且 SciPy 重复颜色，因此这个问题。 . .

如何使用我的叶色字典自定义树状图簇的颜色？

我提出了一个 GitHub 问题 https://github.com/scipy/scipy/issues/6346 where I further elaborated on the approach to color the leaves in 但我仍然无法弄清楚如何实际要么：(i) 使用树状图输出用我指定的颜色字典重建我的树状图或 (ii ) 为 link_color_func 参数重新格式化我的 D_leaf_color 字典。

# Init
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns; sns.set()

# Load data
from sklearn.datasets import load_diabetes

# Clustering
from scipy.cluster.hierarchy import dendrogram, fcluster, leaves_list
from scipy.spatial import distance
from fastcluster import linkage # You can use SciPy one too

%matplotlib inline

# Dataset
A_data = load_diabetes().data
DF_diabetes = pd.DataFrame(A_data, columns = ["attr_%d" % j for j in range(A_data.shape[1])])

# Absolute value of correlation matrix, then subtract from 1 for disimilarity
DF_dism = 1 - np.abs(DF_diabetes.corr())

# Compute average linkage
A_dist = distance.squareform(DF_dism.as_matrix())
Z = linkage(A_dist,method="average")

# Color mapping
D_leaf_colors = {"attr_1": "#808080", # Unclustered gray

                 "attr_4": "#B061FF", # Cluster 1 indigo
                 "attr_5": "#B061FF",
                 "attr_2": "#B061FF",
                 "attr_8": "#B061FF",
                 "attr_6": "#B061FF",
                 "attr_7": "#B061FF",

                 "attr_0": "#61ffff", # Cluster 2 cyan
                 "attr_3": "#61ffff",
                 "attr_9": "#61ffff",
                 }

# Dendrogram
# To get this dendrogram coloring below  `color_threshold=0.7`
D = dendrogram(Z=Z, labels=DF_dism.index, color_threshold=None, leaf_font_size=12, leaf_rotation=45, link_color_func=D_leaf_colors)
# TypeError: 'dict' object is not callable

我也试过了how do I get the subtrees of dendrogram made by scipy.cluster.hierarchy

Answer 1

我找到了一个 hackish 解决方案，并且确实需要使用颜色阈值（但我需要使用它以获得相同的原始着色，否则颜色与 OP 中呈现的颜色不同），但是可以引导您找到解决方案。但是，您可能没有足够的信息来了解如何设置调色板顺序。

# Init
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns; sns.set()

# Load data
from sklearn.datasets import load_diabetes

# Clustering
from scipy.cluster.hierarchy import dendrogram, fcluster, leaves_list, set_link_color_palette
from scipy.spatial import distance
from fastcluster import linkage # You can use SciPy one too

%matplotlib inline
# Dataset
A_data = load_diabetes().data
DF_diabetes = pd.DataFrame(A_data, columns = ["attr_%d" % j for j in range(A_data.shape[1])])

# Absolute value of correlation matrix, then subtract from 1 for disimilarity
DF_dism = 1 - np.abs(DF_diabetes.corr())

# Compute average linkage
A_dist = distance.squareform(DF_dism.as_matrix())
Z = linkage(A_dist,method="average")

# Color mapping dict not relevant in this case
# Dendrogram
# To get this dendrogram coloring below  `color_threshold=0.7`
#Change the color palette, I did not include the grey, which is used above the threshold
set_link_color_palette(["#B061FF", "#61ffff"])
D = dendrogram(Z=Z, labels=DF_dism.index, color_threshold=.7, leaf_font_size=12, leaf_rotation=45, 
               above_threshold_color="grey")

结果：

Answer 2

这里有一个解决方案，它使用 linkage() 的 return 矩阵 Z（描述较早，但在 docs 中有点隐藏）和 link_color_func：

# see question for code prior to "color mapping"

# Color mapping
dflt_col = "#808080"   # Unclustered gray
D_leaf_colors = {"attr_1": dflt_col,

                 "attr_4": "#B061FF", # Cluster 1 indigo
                 "attr_5": "#B061FF",
                 "attr_2": "#B061FF",
                 "attr_8": "#B061FF",
                 "attr_6": "#B061FF",
                 "attr_7": "#B061FF",

                 "attr_0": "#61ffff", # Cluster 2 cyan
                 "attr_3": "#61ffff",
                 "attr_9": "#61ffff",
                 }

# notes:
# * rows in Z correspond to "inverted U" links that connect clusters
# * rows are ordered by increasing distance
# * if the colors of the connected clusters match, use that color for link
link_cols = {}
for i, i12 in enumerate(Z[:,:2].astype(int)):
  c1, c2 = (link_cols[x] if x > len(Z) else D_leaf_colors["attr_%d"%x]
    for x in i12)
  link_cols[i+1+len(Z)] = c1 if c1 == c2 else dflt_col

# Dendrogram
D = dendrogram(Z=Z, labels=DF_dism.index, color_threshold=None,
  leaf_font_size=12, leaf_rotation=45, link_color_func=lambda x: link_cols[x])

这里是输出：

Answer 3

Two-liner 用于将自定义颜色映射应用于集群分支：

import matplotlib as mpl
from matplotlib.pyplot import cm
from scipy.cluster import hierarchy

cmap = cm.rainbow(np.linspace(0, 1, 10))
hierarchy.set_link_color_palette([mpl.colors.rgb2hex(rgb[:3]) for rgb in cmap])

然后您可以用任何 cmap 替换 rainbow 并将 10 更改为您想要的集群数。

Python 中 SciPy 树状图的自定义簇颜色（link_color_func？）

Custom cluster colors of SciPy dendrogram in Python (link_color_func?)

python

machine-learning

hierarchical-clustering

dendrogram

scipy