我在使用sklearn和python计算轮廓系数时遇到了问题。以下是我的代码:
我收到了一个错误信息:
：
from sklearn import datasets
from sklearn.metrics import *
iris = datasets.load_iris()
X = pd.DataFrame(iris.data, columns = col)
y = pd.DataFrame(iris.target,columns = ['cluster'])
s = silhouette_score(X, y, metric='euclidean',sample_size=int(50))
我收到了一个错误信息:
IndexError: indices are out-of-bounds
我希望使用sample_size参数，因为在处理非常大的数据集时，计算轮廓系数需要太长的时间。有人知道这个参数怎么用吗？
完整的回溯信息：
---------------------------------------------------------------------------
IndexError Traceback (most recent call last)
<ipython-input-72-70ff40842503> in <module>()
4 X = pd.DataFrame(iris.data, columns = col)
5 y = pd.DataFrame(iris.target,columns = ['cluster'])
----> 6 s = silhouette_score(X, 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)
81 X, labels = X[indices].T[indices].T, labels[indices]
82 else:
---> 83 X, labels = X[indices], labels[indices]
84 return np.mean(silhouette_samples(X, labels, metric=metric, **kwds))
85
/usr/local/lib/python2.7/dist-packages/pandas/core/frame.pyc in __getitem__(self, key)
1993 if isinstance(key, (np.ndarray, list)):
1994 # either boolean or fancy integer index
-> 1995 return self._getitem_array(key)
1996 elif isinstance(key, DataFrame):
1997 return self._getitem_frame(key)
/usr/local/lib/python2.7/dist-packages/pandas/core/frame.pyc in _getitem_array(self, key)
2030 else:
2031 indexer = self.ix._convert_to_indexer(key, axis=1)
-> 2032 return self.take(indexer, axis=1, convert=True)
2033
2034 def _getitem_multilevel(self, key):
/usr/local/lib/python2.7/dist-packages/pandas/core/frame.pyc in take(self, indices, axis, convert)
2981 if convert:
2982 axis = self._get_axis_number(axis)
-> 2983 indices = _maybe_convert_indices(indices, len(self._get_axis(axis)))
2984
2985 if self._is_mixed_type:
/usr/local/lib/python2.7/dist-packages/pandas/core/indexing.pyc in _maybe_convert_indices(indices, n)
1038 mask = (indices>=n) | (indices<0)
1039 if mask.any():
-> 1040 raise IndexError("indices are out-of-bounds")
1041 return indices
1042
IndexError: indices are out-of-bounds