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from sklearn.metrics.cluster import normalized_mutual_info_score as NMI, \
adjusted_mutual_info_score as AMI, adjusted_rand_score as AR, silhouette_score
as SI, calinski_harabasz_score as CH def clustring_indicators (pred, data=None,
labels=None, model_name='cluster', verbose=1): measure_dict = dict() #如果有原始数据
if data is not None: measure_dict['si'] = SI(data, pred) measure_dict['ch'] =
CH(data, pred) #如果数据有标签 if labels is not None: measure_dict['acc'] =
cluster_acc(pred, labels)[0] measure_dict['nmi'] = NMI(labels, pred)
measure_dict['ar'] = AR(labels, pred) measure_dict['ami'] = AMI(labels, pred)
#如果需要打印所有指标 if verbose: char = '' for (key, value) in measure_dict.items():
char += '{}: {:.4f} '.format(key, value) print('{} {}'.format(model_name,
char)) return measure_dict ##参考论文Unsupervised deep embedding for clustering
analysis def cluster_acc(Y_pred, Y): assert Y_pred.size == Y.size D =
max(Y_pred.max(), Y.max()) + 1 w = np.zeros((D, D), dtype=np.int64) for i in
range(Y_pred.size): w[Y_pred[i], Y[i]] += 1 ind = linear_assignment(w.max() -
w) total = 0 for i in range(len(ind[0])): total += w[ind[0][i], ind[1][i]]
return total * 1.0 / Y_pred.size, w