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

我试过关注 https://joernhees.de/blog/2015/08/26/scipy-hierarchical-clustering-and-dendrogram-tutorial/但由于某种原因颜色变得困惑。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来自定义我的树状图簇的颜色？

我做了一个 GitHub 问题 https://github.com/scipy/scipy/issues/6346我在 Interpreting the output of SciPy's hierarchical clustering dendrogram? (maybe found a bug...) 中进一步阐述了给叶子上色的方法。但我仍然无法弄清楚实际上如何:(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< 重新格式化我的="">D_leaf_color 字典 参数。

# 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how do I get the subtrees of dendrogram made by scipy.cluster.hierarchy

最佳答案

这里有一个解决方案，它使用 linkage() 的返回矩阵 Z(前面描述过，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])

这里输出:

关于python - Python 中 SciPy 树状图的自定义簇颜色(link_color_func？)，我们在Stack Overflow上找到一个类似的问题： https://stackoverflow.com/questions/38153829/