如何自动化颜色的数量和从树状图聚集层次结构返回的颜色？

Question

scikit-learn 给出 生成树状图的 python 代码示例。我复制/粘贴下面的代码。此代码生成树状图。该树状图显示 3 种不同的颜色：蓝色、绿色和橙色。

问题：与该树状图代码示例相关的哪些代码可以自动提供：

• 由树状图生成的颜色数量？
• 这些颜色的列表（或其代码）？

import numpy as np

from matplotlib import pyplot as plt
from scipy.cluster.hierarchy import dendrogram
from sklearn.datasets import load_iris
from sklearn.cluster import AgglomerativeClustering


def plot_dendrogram(model, **kwargs):
    # Create linkage matrix and then plot the dendrogram

    # create the counts of samples under each node
    counts = np.zeros(model.children_.shape[0])
    n_samples = len(model.labels_)
    for i, merge in enumerate(model.children_):
        current_count = 0
        for child_idx in merge:
            if child_idx < n_samples:
                current_count += 1  # leaf node
            else:
                current_count += counts[child_idx - n_samples]
        counts[i] = current_count

    linkage_matrix = np.column_stack(
        [model.children_, model.distances_, counts]
    ).astype(float)

    # Plot the corresponding dendrogram
    dendrogram(linkage_matrix, **kwargs)


iris = load_iris()
X = iris.data

# setting distance_threshold=0 ensures we compute the full tree.
model = AgglomerativeClustering(distance_threshold=0, n_clusters=None)

model = model.fit(X)
plt.title("Hierarchical Clustering Dendrogram")
# plot the top three levels of the dendrogram
plot_dendrogram(model, truncate_mode="level", p=3)
plt.xlabel("Number of points in node (or index of point if no parenthesis).")
plt.show()

Answer 1

如果您在此处阅读文档 ，颜色数量由color_threshold决定，默认为0.7*max(Z[:,2])。因此，您只需找到高于该值的合并数量：

首先修改代码以获取链接矩阵：

def get_linkage(model):
    # Create linkage matrix

    # create the counts of samples under each node
    counts = np.zeros(model.children_.shape[0])
    n_samples = len(model.labels_)
    for i, merge in enumerate(model.children_):
        current_count = 0
        for child_idx in merge:
            if child_idx < n_samples:
                current_count += 1  # leaf node
            else:
                current_count += counts[child_idx - n_samples]
        counts[i] = current_count

    linkage_matrix = np.column_stack(
        [model.children_, model.distances_, counts]
    ).astype(float)
    return linkage_matrix


iris = load_iris()
X = iris.data

# setting distance_threshold=0 ensures we compute the full tree.
model = AgglomerativeClustering(distance_threshold=0, n_clusters=None)

model = model.fit(X)
linkage_matrix = get_linkage(model)

然后从中计算颜色数量：

from scipy.cluster.hierarchy import cut_tree
color_threshold = 0.7 * max(linkage_matrix[:, 2])
n_color = 1 + len(np.unique(cut_tree(linkage_matrix, height = color_threshold)))
color_codes = ['C' + str(i) for i in range(n_color)] # this is simply the matplotlib default color code