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我再次遇到使用scikit-learn側身係數的麻煩。 （第一個問題在這裏：silhouette coefficient in python with sklearn）。 我做了一個非常不平衡但具有很多個體的聚類，所以我想使用輪廓係數的採樣參數。我想知道子採樣是否是分層的，這意味着採樣與集羣有關。我以虹膜數據集爲例，但我的數據集要大得多（這就是爲什麼我需要採樣）。 我的代碼是：sklearn中的分層係數子採樣是否分層？
from sklearn import datasets
from sklearn.metrics import *
iris = datasets.load_iris()
col = iris.feature_names
name = iris.target_names
X = pd.DataFrame(iris.data, columns = col)
y = iris.target
s = silhouette_score(X.values, y, metric='euclidean',sample_size=50)
哪些工作。但現在如果我偏向與：
y[0:148] =0
y[148] = 1
y[149] = 2
print y
s = silhouette_score(X.values, y, metric='euclidean',sample_size=50)
我得到：
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, out, keepdims)
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
的錯誤，是因爲我覺得這樣的事實，抽樣是隨機的不分層，因此它沒有考慮到這兩個小集羣。
我正確嗎？