____________________________ test_unsorted_indices _____________________________
    def test_unsorted_indices():
        # test that the result with sorted and unsorted indices in csr is the same
        # we use a subset of digits as iris, blobs or make_classification didn't
        # show the problem
        digits = load_digits()
        X, y = digits.data[:50], digits.target[:50]
        X_test = sparse.csr_matrix(digits.data[50:100])
    
        X_sparse = sparse.csr_matrix(X)
        coef_dense = svm.SVC(kernel='linear', probability=True,
                             random_state=0).fit(X, y).coef_
        sparse_svc = svm.SVC(kernel='linear', probability=True,
                             random_state=0).fit(X_sparse, y)
        coef_sorted = sparse_svc.coef_
        # make sure dense and sparse SVM give the same result
        assert_array_almost_equal(coef_dense, coef_sorted.toarray())
    
        X_sparse_unsorted = X_sparse[np.arange(X.shape[0])]
        X_test_unsorted = X_test[np.arange(X_test.shape[0])]
    
        # make sure we scramble the indices
>       assert not X_sparse_unsorted.has_sorted_indices
E       AssertionError: assert not 1
E        +  where 1 = <50x64 sparse matrix of type '<class 'numpy.float64'>'\n	with 1628 stored elements in Compressed Sparse Row format>.has_sorted_indices
X          = array([[ 0.,  0.,  5., ...,  0.,  0.,  0.],
       [ 0.,  0.,  0., ..., 10.,  0.,  0.],
       [ 0.,  0.,  0., ..., 16... 0.,  0., ...,  6.,  0.,  0.],
       [ 0.,  0.,  2., ...,  6.,  0.,  0.],
       [ 0.,  0.,  1., ...,  8.,  0.,  0.]])
X_sparse   = <50x64 sparse matrix of type '<class 'numpy.float64'>'
	with 1628 stored elements in Compressed Sparse Row format>
X_sparse_unsorted = <50x64 sparse matrix of type '<class 'numpy.float64'>'
	with 1628 stored elements in Compressed Sparse Row format>
X_test     = <50x64 sparse matrix of type '<class 'numpy.float64'>'
	with 1583 stored elements in Compressed Sparse Row format>
X_test_unsorted = <50x64 sparse matrix of type '<class 'numpy.float64'>'
	with 1583 stored elements in Compressed Sparse Row format>
coef_dense = array([[ 0.        ,  0.        ,  0.00134814, ..., -0.002488  ,
         0.        ,  0.        ],
       [ 0.       ...917, -0.00045177],
       [ 0.        , -0.00067068, -0.0021453 , ..., -0.00229777,
        -0.00393698, -0.00026964]])
coef_sorted = <45x64 sparse matrix of type '<class 'numpy.float64'>'
	with 2045 stored elements in Compressed Sparse Row format>
digits     = {'data': array([[ 0.,  0.,  5., ...,  0.,  0.,  0.],
       [ 0.,  0.,  0., ..., 10.,  0.,  0.],
       [ 0.,  0.,  0....versity.\n    2005.\n  - Claudio Gentile. A New Approximate Maximal Margin Classification\n    Algorithm. NIPS. 2000."}
sparse_svc = SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,
    decision_function_shape='ovr', degree=3, gamma='auto_deprecated',
    kernel='linear', max_iter=-1, probability=True, random_state=0,
    shrinking=True, tol=0.001, verbose=False)
y          = array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 0, 1,
       2, 3, 4, 5, 6, 7, 8, 9, 0, 9, 5, 5, 6, 5, 0, 9, 8, 9, 8, 4, 1, 7,
       7, 3, 5, 1, 0, 0])