如何计算凝聚聚类的精度

嗨,我在python中使用了aggregativeclustering示例,我试图估计性能,但它切换了原始标签 我试图通过makeblobs比较预测的标签y\uhc和原始标签y返回

import scipy.cluster.hierarchy as sch
from sklearn.cluster import AgglomerativeClustering
from sklearn.datasets import make_blobs
import numpy as np
import matplotlib.pyplot as plt
data,y = make_blobs(n_samples=300, n_features=2, centers=4, cluster_std=2, random_state=50)
plt.figure(2)
# create dendrogram
dendrogram = sch.dendrogram(sch.linkage(data, method='ward'))
plt.title('dendrogram')

# create clusters linkage="average", affinity=metric , linkage = 'ward' affinity = 'euclidean'
hc = AgglomerativeClustering(n_clusters=4, linkage="average", affinity='euclidean')

# save clusters for chart
y_hc = hc.fit_predict(data,y)

plt.figure(3)

# create scatter plot
plt.scatter(data[y==0,0], data[y==0,1], c='red', s=50)
plt.scatter(data[y==1, 0], data[y==1, 1], c='black', s=50)
plt.scatter(data[y==2, 0], data[y==2, 1], c='blue', s=50)
plt.scatter(data[y==3, 0], data[y==3, 1], c='cyan', s=50)

plt.xlim(-15,15)
plt.ylim(-15,15)


plt.scatter(data[y_hc ==0,0], data[y_hc == 0,1], s=10, c='red')
plt.scatter(data[y_hc==1,0], data[y_hc == 1,1], s=10, c='black')
plt.scatter(data[y_hc ==2,0], data[y_hc == 2,1], s=10, c='blue')
plt.scatter(data[y_hc ==3,0], data[y_hc == 3,1], s=10, c='cyan')
for ii in range(4):
        print(ii)
        i0=y_hc==ii
        counts = np.bincount(y[i0])
        valCountAtorgLbl = (np.argmax(counts))
        accuracy0Tp=100*np.max(counts)/y[y==valCountAtorgLbl].shape[0]
        accuracy0Fp = 100 * np.min(counts) / y[y ==valCountAtorgLbl].shape[0]

print([accuracy0Tp,accuracy0Fp])
plt.show()