99 lines
2.9 KiB
Python
99 lines
2.9 KiB
Python
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import numpy as np
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from numpy.testing import assert_array_almost_equal, assert_
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from scipy.sparse import csr_matrix, csc_matrix, lil_matrix
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import pytest
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def test_csc_getrow():
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N = 10
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np.random.seed(0)
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X = np.random.random((N, N))
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X[X > 0.7] = 0
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Xcsc = csc_matrix(X)
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for i in range(N):
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arr_row = X[i:i + 1, :]
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csc_row = Xcsc.getrow(i)
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assert_array_almost_equal(arr_row, csc_row.toarray())
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assert_(type(csc_row) is csr_matrix)
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def test_csc_getcol():
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N = 10
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np.random.seed(0)
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X = np.random.random((N, N))
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X[X > 0.7] = 0
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Xcsc = csc_matrix(X)
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for i in range(N):
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arr_col = X[:, i:i + 1]
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csc_col = Xcsc.getcol(i)
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assert_array_almost_equal(arr_col, csc_col.toarray())
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assert_(type(csc_col) is csc_matrix)
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@pytest.mark.parametrize("matrix_input, axis, expected_shape",
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[(csc_matrix([[1, 0],
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[0, 0],
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[0, 2]]),
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0, (0, 2)),
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(csc_matrix([[1, 0],
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[0, 0],
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[0, 2]]),
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1, (3, 0)),
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(csc_matrix([[1, 0],
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[0, 0],
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[0, 2]]),
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'both', (0, 0)),
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(csc_matrix([[0, 1, 0, 0, 0, 0],
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[0, 0, 0, 0, 0, 0],
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[0, 0, 2, 3, 0, 1]]),
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0, (0, 6))])
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def test_csc_empty_slices(matrix_input, axis, expected_shape):
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# see gh-11127 for related discussion
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slice_1 = matrix_input.toarray().shape[0] - 1
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slice_2 = slice_1
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slice_3 = slice_2 - 1
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if axis == 0:
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actual_shape_1 = matrix_input[slice_1:slice_2, :].toarray().shape
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actual_shape_2 = matrix_input[slice_1:slice_3, :].toarray().shape
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elif axis == 1:
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actual_shape_1 = matrix_input[:, slice_1:slice_2].toarray().shape
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actual_shape_2 = matrix_input[:, slice_1:slice_3].toarray().shape
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elif axis == 'both':
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actual_shape_1 = matrix_input[slice_1:slice_2, slice_1:slice_2].toarray().shape
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actual_shape_2 = matrix_input[slice_1:slice_3, slice_1:slice_3].toarray().shape
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assert actual_shape_1 == expected_shape
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assert actual_shape_1 == actual_shape_2
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@pytest.mark.parametrize('ax', (-2, -1, 0, 1, None))
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def test_argmax_overflow(ax):
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# See gh-13646: Windows integer overflow for large sparse matrices.
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dim = (100000, 100000)
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A = lil_matrix(dim)
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A[-2, -2] = 42
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A[-3, -3] = 0.1234
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A = csc_matrix(A)
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idx = A.argmax(axis=ax)
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if ax is None:
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# idx is a single flattened index
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# that we need to convert to a 2d index pair;
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# can't do this with np.unravel_index because
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# the dimensions are too large
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ii = idx % dim[0]
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jj = idx // dim[0]
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else:
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# idx is an array of size of A.shape[ax];
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# check the max index to make sure no overflows
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# we encountered
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assert np.count_nonzero(idx) == A.nnz
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ii, jj = np.max(idx), np.argmax(idx)
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assert A[ii, jj] == A[-2, -2]
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