这里的解决方案使用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])

输出如下：

用于将自定义颜色Map表应用于簇分支的双行代码：

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，您可以将 rainbow 替换为任何cmap，并将您想要的集群数量更改为10。

这个答案很有帮助，但并不是简单地转换为更一般的情况-这里有一个函数运行scipy的凝聚聚类并绘制相应的树状图，使用自定义提供的颜色，对于给定的距离阈值：

def rgb_hex(color):
    '''converts a (r,g,b) color (either 0-1 or 0-255) to its hex representation.
    for ambiguous pure combinations of 0s and 1s e,g, (0,0,1), (1/1/1) is assumed.'''
    message='color must be an iterable of length 3.'
    assert hasattr(color, '__iter__'), message
    assert len(color)==3, message
    if all([(c<=1)&(c>=0) for c in color]): color=[int(round(c*255)) for c in color] # in case provided rgb is 0-1
    color=tuple(color)
    return '#%02x%02x%02x' % color

def get_cluster_colors(n_clusters, my_set_of_20_rgb_colors, alpha=0.8, alpha_outliers=0.05):
    cluster_colors = my_set_of_20_rgb_colors
    cluster_colors = [c+[alpha] for c in cluster_colors]
    outlier_color = [0,0,0,alpha_outliers]
    return [cluster_colors[i%19] for i in range(n_clusters)] + [outlier_color]

def cluster_and_plot_dendrogram(X, threshold, method='ward', metric='euclidean', default_color='black'):

    # perform hierarchical clustering
    Z              = hierarchy.linkage(X, method=method, metric=metric)

    # get cluster labels
    labels         = hierarchy.fcluster(Z, threshold, criterion='distance') - 1
    labels_str     = [f"cluster #{l}: n={c}\n" for (l,c) in zip(*np.unique(labels, return_counts=True))]
    n_clusters     = len(labels_str)

    cluster_colors = [rgb_hex(c[:-1]) for c in get_cluster_colors(n_clusters, alpha=0.8, alpha_outliers=0.05)]
    cluster_colors_array = [cluster_colors[l] for l in labels]
    link_cols = {}
    for i, i12 in enumerate(Z[:,:2].astype(int)):
        c1, c2 = (link_cols[x] if x > len(Z) else cluster_colors_array[x] for x in i12)
        link_cols[i+1+len(Z)] = c1 if c1 == c2 else 'k'

    # plot dendrogram with colored clusters
    fig = plt.figure(figsize=(12, 5))
    plt.title('Hierarchical Clustering Dendrogram')
    plt.xlabel('Data points')
    plt.ylabel('Distance')

    # plot dendrogram based on clustering results
    hierarchy.dendrogram(
        Z,

        labels = labels,

        color_threshold=threshold,

        truncate_mode = 'level',
        p = 5,
        show_leaf_counts = True,
        leaf_rotation=90,
        leaf_font_size=10,
        show_contracted=False,

        link_color_func=lambda x: link_cols[x],
        above_threshold_color=default_color,
        distance_sort='descending',
        ax=plt.gca()
    )
    plt.axhline(threshold, color='k')
    for i, s in enumerate(labels_str):
        plt.text(0.8, 0.95-i*0.04, s,
                transform=plt.gca().transAxes,
                va='top', color=cluster_colors[i])
    
    fig.patch.set_facecolor('white')

    return labels # 0 indexed

这将返回聚类标签，并生成如下所示的图：

希望这对以后的人有帮助。

我找到了一个很简单的解决方案，需要使用颜色阈值（但我需要使用它来获得相同的原始着色，否则颜色与OP中呈现的颜色不一样），但可以引导您找到解决方案。然而，您可能没有足够的信息来知道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")

结果是：