我再次在使用scikit-learn的轮廓系数时遇到了问题。（我的第一个问题在这里：在Python中使用sklearn计算轮廓系数）。我进行的聚类可能非常不平衡，但样本数量很多，所以我想使用轮廓系数的采样参数。我想知道子采样是否是分层的，意思是根据聚类进行采样。我以鸢尾花数据集为例，但我的数据集要大得多（这就是我需要采样的原因）。我的代码是：

from sklearn import datasetsfrom sklearn.metrics import *iris = datasets.load_iris()col = iris.feature_namesname = iris.target_namesX = pd.DataFrame(iris.data, columns = col)y = iris.targets = silhouette_score(X.values, y, metric='euclidean',sample_size=50)

这个代码是可以运行的。但是如果我这样修改它：

y[0:148] =0y[148] = 1y[149] = 2print ys = silhouette_score(X.values, y, metric='euclidean',sample_size=50)

我得到的错误是：

ValueError                                Traceback (most recent call last)<ipython-input-12-68a7fba49c54> in <module>()      4 y[149] =2      5 print y----> 6 s = silhouette_score(X.values, y, metric='euclidean',sample_size=50)/usr/local/lib/python2.7/dist-packages/sklearn/metrics/cluster/unsupervised.pyc in silhouette_score(X, labels, metric, sample_size, random_state, **kwds)     82         else:     83             X, labels = X[indices], labels[indices]---> 84     return np.mean(silhouette_samples(X, labels, metric=metric, **kwds))     85      86 /usr/local/lib/python2.7/dist-packages/sklearn/metrics/cluster/unsupervised.pyc in silhouette_samples(X, labels, metric, **kwds)    146                   for i in range(n)])    147     B = np.array([_nearest_cluster_distance(distances[i], labels, i)--> 148                   for i in range(n)])    149     sil_samples = (B - A) / np.maximum(A, B)    150     # nan values are for clusters of size 1, and should be 0/usr/local/lib/python2.7/dist-packages/sklearn/metrics/cluster/unsupervised.pyc in _nearest_cluster_distance(distances_row, labels, i)    200     label = labels[i]    201     b = np.min([np.mean(distances_row[labels == cur_label])--> 202                for cur_label in set(labels) if not cur_label == label])    203     return b/usr/lib/python2.7/dist-packages/numpy/core/fromnumeric.pyc in amin(a, axis, out, keepdims)   1980         except AttributeError:   1981             return _methods._amin(a, axis=axis,-> 1982                                 out=out, keepdims=keepdims)   1983         # NOTE: Dropping the keepdims parameter   1984         return amin(axis=axis, out=out)/usr/lib/python2.7/dist-packages/numpy/core/_methods.pyc in _amin(a, axis=None, out=None, keepdims=False)     12 def _amin(a, axis=None, out=None, keepdims=False):     13     return um.minimum.reduce(a, axis=axis,---> 14                             out=out, keepdims=keepdims)     15      16 def _sum(a, axis=None, dtype=None, out=None, keepdims=False):ValueError: zero-size array to reduction operation minimum which has no identity

我认为这个错误是由于采样是随机的而不是分层的，因此没有考虑到那两个较小的聚类。

我的理解正确吗？

回答：

我认为你是对的，当前的实现不支持平衡重采样。