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116 lines
3.4 KiB
Python
116 lines
3.4 KiB
Python
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
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import pytest
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def _check_csr_rowslice(i, sl, X, Xcsr):
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np_slice = X[i, sl]
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csr_slice = Xcsr[i, sl]
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assert_array_almost_equal(np_slice, csr_slice.toarray()[0])
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assert_(type(csr_slice) is csr_matrix)
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def test_csr_rowslice():
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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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Xcsr = csr_matrix(X)
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slices = [slice(None, None, None),
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slice(None, None, -1),
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slice(1, -2, 2),
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slice(-2, 1, -2)]
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for i in range(N):
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for sl in slices:
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_check_csr_rowslice(i, sl, X, Xcsr)
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def test_csr_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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Xcsr = csr_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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csr_row = Xcsr.getrow(i)
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assert_array_almost_equal(arr_row, csr_row.toarray())
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assert_(type(csr_row) is csr_matrix)
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def test_csr_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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Xcsr = csr_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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csr_col = Xcsr.getcol(i)
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assert_array_almost_equal(arr_col, csr_col.toarray())
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assert_(type(csr_col) is csr_matrix)
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@pytest.mark.parametrize("matrix_input, axis, expected_shape",
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[(csr_matrix([[1, 0, 0, 0],
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[0, 0, 0, 0],
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[0, 2, 3, 0]]),
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0, (0, 4)),
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(csr_matrix([[1, 0, 0, 0],
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[0, 0, 0, 0],
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[0, 2, 3, 0]]),
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1, (3, 0)),
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(csr_matrix([[1, 0, 0, 0],
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[0, 0, 0, 0],
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[0, 2, 3, 0]]),
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'both', (0, 0)),
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(csr_matrix([[0, 1, 0, 0, 0],
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[0, 0, 0, 0, 0],
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[0, 0, 2, 3, 0]]),
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0, (0, 5))])
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def test_csr_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.A.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, :].A.shape
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actual_shape_2 = matrix_input[slice_1:slice_3, :].A.shape
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elif axis == 1:
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actual_shape_1 = matrix_input[:, slice_1:slice_2].A.shape
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actual_shape_2 = matrix_input[:, slice_1:slice_3].A.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].A.shape
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actual_shape_2 = matrix_input[slice_1:slice_3, slice_1:slice_3].A.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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def test_csr_bool_indexing():
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data = csr_matrix([[0, 1, 2], [3, 4, 5], [6, 7, 8]])
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list_indices1 = [False, True, False]
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array_indices1 = np.array(list_indices1)
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list_indices2 = [[False, True, False], [False, True, False], [False, True, False]]
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array_indices2 = np.array(list_indices2)
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list_indices3 = ([False, True, False], [False, True, False])
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array_indices3 = (np.array(list_indices3[0]), np.array(list_indices3[1]))
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slice_list1 = data[list_indices1].toarray()
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slice_array1 = data[array_indices1].toarray()
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slice_list2 = data[list_indices2]
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slice_array2 = data[array_indices2]
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slice_list3 = data[list_indices3]
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slice_array3 = data[array_indices3]
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assert (slice_list1 == slice_array1).all()
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assert (slice_list2 == slice_array2).all()
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assert (slice_list3 == slice_array3).all()
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