Scipy：稀疏矩陣是否支持高級索引？

Question

沒問題：

>>> t = np.array([[1,1,1,1,1],[2,2,2,2,2],[3,3,3,3,3],[4,4,4,4,4],[5,5,5,5,5]]) 
>>> x = np.arange(5).reshape((-1,1)); y = np.arange(5) 
>>> print (t[[x]],t[[y]]) 

大問題：

>>> s = scipy.sparse.csr_matrix(t) 
>>> print (s[[x]].toarray(),s[[y]].toarray()) 
Traceback (most recent call last): 
    File "<pyshell#22>", line 1, in <module> 
:    : 
:    : 
ValueError: data, indices, and indptr should be rank 1 

s.toarray()[[x]]的偉大工程，但違背了使用稀疏矩陣作爲我的數組是太大了我的整個目的。我已經檢查了與某些稀疏矩陣相關的屬性和方法，以查看任何引用「高級索引」的內容，但沒有任何骰子。有任何想法嗎？

Answer 1

稀疏矩陣的索引支持非常有限，可用的取決於矩陣的格式。

例如：

>>> a = scipy.sparse.rand(100,100,format='coo') 
>>> a[2:5, 6:8] 
Traceback (most recent call last): 
    File "<stdin>", line 1, in <module> 
TypeError: 'coo_matrix' object has no attribute '__getitem__' 

但

>>> a = scipy.sparse.rand(100,100,format='csc') 
>>> a[2:5, 6:8] 
<3x2 sparse matrix of type '<type 'numpy.float64'>' 
    with 0 stored elements in Compressed Sparse Column format> 

雖然

>>> a[2:5:2, 6:8:3] 
Traceback (most recent call last): 
... 
ValueError: slicing with step != 1 not supported 

還有

>>> a = scipy.sparse.rand(100,100,format='dok') 
>>> a[2:5:2, 6:8:3] 
Traceback (most recent call last): 
... 
NotImplementedError: fancy indexing supported over one axis only 
>>> a[2:5:2,1] 
<3x1 sparse matrix of type '<type 'numpy.float64'>' 
    with 0 stored elements in Dictionary Of Keys format> 

甚至

>>> a = scipy.sparse.rand(100,100,format='lil') 
>>> a[2:5:2,1] 
<2x1 sparse matrix of type '<type 'numpy.int32'>' 
    with 0 stored elements in LInked List format> 
C:\Python27\lib\site-packages\scipy\sparse\lil.py:230: SparseEfficiencyWarning: Indexing into a lil_matrix with multiple indices is slow. Pre-converting to CSC or CSR beforehand is more efficient. 
    SparseEfficiencyWarning) 
>>> a[2:5:2, 6:8:3] 
<2x1 sparse matrix of type '<type 'numpy.int32'>' 
    with 0 stored elements in LInked List format>