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test_csc.py
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import numpy as np from numpy.testing import assert_array_almost_equal, assert_ from scipy.sparse import csr_matrix, csc_matrix import pytest def test_csc_getrow(): N = 10 np.random.seed(0) X = np.random.random((N, N)) X[X > 0.7] = 0 Xcsc = csc_matrix(X) for i in range(N): arr_row = X[i:i + 1, :] csc_row = Xcsc.getrow(i) assert_array_almost_equal(arr_row, csc_row.toarray()) assert_(type(csc_row) is csr_matrix) def test_csc_getcol(): N = 10 np.random.seed(0) X = np.random.random((N, N)) X[X > 0.7] = 0 Xcsc = csc_matrix(X) for i in range(N): arr_col = X[:, i:i + 1] csc_col = Xcsc.getcol(i) assert_array_almost_equal(arr_col, csc_col.toarray()) assert_(type(csc_col) is csc_matrix) @pytest.mark.parametrize("matrix_input, axis, expected_shape", [(csc_matrix([[1, 0], [0, 0], [0, 2]]), 0, (0, 2)), (csc_matrix([[1, 0], [0, 0], [0, 2]]), 1, (3, 0)), (csc_matrix([[1, 0], [0, 0], [0, 2]]), 'both', (0, 0)), (csc_matrix([[0, 1, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0], [0, 0, 2, 3, 0, 1]]), 0, (0, 6))]) def test_csc_empty_slices(matrix_input, axis, expected_shape): # see gh-11127 for related discussion slice_1 = matrix_input.A.shape[0] - 1 slice_2 = slice_1 slice_3 = slice_2 - 1 if axis == 0: actual_shape_1 = matrix_input[slice_1:slice_2, :].A.shape actual_shape_2 = matrix_input[slice_1:slice_3, :].A.shape elif axis == 1: actual_shape_1 = matrix_input[:, slice_1:slice_2].A.shape actual_shape_2 = matrix_input[:, slice_1:slice_3].A.shape elif axis == 'both': actual_shape_1 = matrix_input[slice_1:slice_2, slice_1:slice_2].A.shape actual_shape_2 = matrix_input[slice_1:slice_3, slice_1:slice_3].A.shape assert actual_shape_1 == expected_shape assert actual_shape_1 == actual_shape_2