问题描述
我无法使用sklearn在python中计算轮廓系数。
这是我的代码：
I'm having trouble computing the silhouette coefficient in python with sklearn.Here is my code :
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))
我收到错误消息：
IndexError: indices are out-of-bounds
我想使用sample_size参数，因为在处理非常大的数据集时，轮廓太长而无法计算。任何人都知道此参数如何工作？
I want to use the sample_size parameter because when working with very large datasets, silhouette is too long to compute. Anyone knows how this parameter could work ?
完整的追溯：
---------------------------------------------------------------------------
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
推荐答案
希望将常规的numpy数组作为输入。为什么将数组包装在数据框中？
silhouette_score expects regular numpy arrays as input. Why wrap your arrays in data frames?
>>> silhouette_score(iris.data, iris.target, sample_size=50)
0.52999903616584543
回溯一下，您可以观察到代码在第一个轴上进行了花式索引（二次采样）。默认情况下，索引数据框将索引列而不是行，因此会出现问题。
From the traceback, you can observe that the code is doing fancy indexing (subsampling) on the first axis. By default indexing a dataframe will index the columns and not the rows hence the issue you observe.
这篇关于带有sklearn的python中的轮廓系数的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持！