使用scikit学习剪影分数计算scipy的fcluster的剪影分数

Question

我使用scipy.cluster和fcluster在不同的截断条件下进行层次聚类。我还想使用scikit的剪影核心。我看到帖子如何计算scipy的剪影得分'；s fcluster使用scikit学习轮廓分数？然而，我得到了错误"太多的布尔指数"？？

我的代码如下：

import fastcluster
from sklearn import metrics
from scipy.cluster import hierarchy as hac

Temps=[0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9]
Distance=[]
#read the Distance obtained as a list then
Distances=np.array(Distances)
Z=fastcluster.linkage(Distances, "complete", "euclidean")
for Cutoff in Temps:
    results=hac.fcluster(Z,Cutoff,'distance')
    metrics.silhouette_score(Distances, results, metric="euclidean")

错误报告为：

Traceback (most recent call last):
  File "Clustering_2.py", line 93, in <module>
    main(argv)
  File "Clustering_2.py", line 69, in main
    silscore=metrics.silhouette_score(Distances, results,metric='euclidean')
  File "/home/wangz18/site-packages2/sklearn/metrics/cluster/unsupervised.py", line 93, in silhouette_score
    return np.mean(silhouette_samples(X, labels, metric=metric, **kwds))
  File "/home/wangz18/site-packages2/sklearn/metrics/cluster/unsupervised.py", line 157, in silhouette_samples
    for i in range(n)])
  File "/home/wangz18/site-packages2/sklearn/metrics/cluster/unsupervised.py", line 187, in _intra_cluster_distance
    a = np.mean(distances_row[mask])
ValueError: too many boolean indices

怎么了？请告知。感谢

Answer 1

我有同样的问题，请检查：

1. 距离为N*N，N为样本数

2. 结果为N，该值为集群的类别

3. 集群数量应>1

如果#1和#2是正确的，那么它们应该是正确的。