如何绘制matplotlib python中每个集群的平均轮廓得分

import matplotlib.pyplot as plt
import numpy as np
from sklearn.metrics import silhouette_samples, silhouette_score

# 假设已经有了数据集X和聚类模型model
X = ...
model = ...

cluster_labels = model.labels_
silhouette_avg = silhouette_score(X, cluster_labels)
sample_silhouette_values = silhouette_samples(X, cluster_labels)

fig, ax = plt.subplots()
y_lower = 10

for i in range(n_clusters):
    ith_cluster_silhouette_values = sample_silhouette_values[cluster_labels == i]
    ith_cluster_silhouette_values.sort()
    
    size_cluster_i = ith_cluster_silhouette_values.shape[0]
    y_upper = y_lower + size_cluster_i
    
    color = plt.cm.get_cmap("Spectral")(float(i) / n_clusters)
    ax.fill_betweenx(np.arange(y_lower, y_upper), 0, ith_cluster_silhouette_values, facecolor=color, edgecolor=color, alpha=0.7)
    
    ax.text(-0.05, y_lower + 0.5 * size_cluster_i, str(i))
    
    y_lower = y_upper + 10

ax.set_xlabel("轮廓系数")
ax.set_ylabel("聚类标签")

ax.axvline(x=silhouette_avg, color="red", linestyle="--")
ax.set_yticks([])
ax.set_xticks([-1, -0.8, -0.6, -0.4, -0.2, 0, 0.2, 0.4, 0.6, 0.8, 1])

plt.show()