146 lines
5 KiB
Python
146 lines
5 KiB
Python
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import numpy as np
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from numpy.testing import assert_array_equal, assert_equal
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import pytest
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from scipy.sparse import csr_matrix, coo_matrix, diags
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from scipy.sparse.csgraph import maximum_bipartite_matching
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def test_raises_on_dense_input():
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with pytest.raises(TypeError):
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graph = np.array([[0, 1], [0, 0]])
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maximum_bipartite_matching(graph)
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def test_empty_graph():
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graph = csr_matrix((0, 0))
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x = maximum_bipartite_matching(graph, perm_type='row')
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y = maximum_bipartite_matching(graph, perm_type='column')
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expected_matching = np.array([])
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assert_array_equal(expected_matching, x)
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assert_array_equal(expected_matching, y)
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def test_empty_left_partition():
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graph = csr_matrix((2, 0))
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x = maximum_bipartite_matching(graph, perm_type='row')
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y = maximum_bipartite_matching(graph, perm_type='column')
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assert_array_equal(np.array([]), x)
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assert_array_equal(np.array([-1, -1]), y)
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def test_empty_right_partition():
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graph = csr_matrix((0, 3))
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x = maximum_bipartite_matching(graph, perm_type='row')
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y = maximum_bipartite_matching(graph, perm_type='column')
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assert_array_equal(np.array([-1, -1, -1]), x)
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assert_array_equal(np.array([]), y)
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def test_graph_with_no_edges():
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graph = csr_matrix((2, 2))
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x = maximum_bipartite_matching(graph, perm_type='row')
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y = maximum_bipartite_matching(graph, perm_type='column')
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assert_array_equal(np.array([-1, -1]), x)
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assert_array_equal(np.array([-1, -1]), y)
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def test_graph_that_causes_augmentation():
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# In this graph, column 1 is initially assigned to row 1, but it should be
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# reassigned to make room for row 2.
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graph = csr_matrix([[1, 1], [1, 0]])
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x = maximum_bipartite_matching(graph, perm_type='column')
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y = maximum_bipartite_matching(graph, perm_type='row')
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expected_matching = np.array([1, 0])
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assert_array_equal(expected_matching, x)
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assert_array_equal(expected_matching, y)
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def test_graph_with_more_rows_than_columns():
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graph = csr_matrix([[1, 1], [1, 0], [0, 1]])
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x = maximum_bipartite_matching(graph, perm_type='column')
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y = maximum_bipartite_matching(graph, perm_type='row')
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assert_array_equal(np.array([0, -1, 1]), x)
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assert_array_equal(np.array([0, 2]), y)
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def test_graph_with_more_columns_than_rows():
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graph = csr_matrix([[1, 1, 0], [0, 0, 1]])
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x = maximum_bipartite_matching(graph, perm_type='column')
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y = maximum_bipartite_matching(graph, perm_type='row')
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assert_array_equal(np.array([0, 2]), x)
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assert_array_equal(np.array([0, -1, 1]), y)
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def test_explicit_zeros_count_as_edges():
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data = [0, 0]
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indices = [1, 0]
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indptr = [0, 1, 2]
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graph = csr_matrix((data, indices, indptr), shape=(2, 2))
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x = maximum_bipartite_matching(graph, perm_type='row')
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y = maximum_bipartite_matching(graph, perm_type='column')
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expected_matching = np.array([1, 0])
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assert_array_equal(expected_matching, x)
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assert_array_equal(expected_matching, y)
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def test_feasibility_of_result():
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# This is a regression test for GitHub issue #11458
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data = np.ones(50, dtype=int)
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indices = [11, 12, 19, 22, 23, 5, 22, 3, 8, 10, 5, 6, 11, 12, 13, 5, 13,
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14, 20, 22, 3, 15, 3, 13, 14, 11, 12, 19, 22, 23, 5, 22, 3, 8,
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10, 5, 6, 11, 12, 13, 5, 13, 14, 20, 22, 3, 15, 3, 13, 14]
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indptr = [0, 5, 7, 10, 10, 15, 20, 22, 22, 23, 25, 30, 32, 35, 35, 40, 45,
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47, 47, 48, 50]
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graph = csr_matrix((data, indices, indptr), shape=(20, 25))
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x = maximum_bipartite_matching(graph, perm_type='row')
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y = maximum_bipartite_matching(graph, perm_type='column')
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assert (x != -1).sum() == 13
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assert (y != -1).sum() == 13
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# Ensure that each element of the matching is in fact an edge in the graph.
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for u, v in zip(range(graph.shape[0]), y):
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if v != -1:
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assert graph[u, v]
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for u, v in zip(x, range(graph.shape[1])):
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if u != -1:
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assert graph[u, v]
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def test_large_random_graph_with_one_edge_incident_to_each_vertex():
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np.random.seed(42)
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A = diags(np.ones(25), offsets=0, format='csr')
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rand_perm = np.random.permutation(25)
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rand_perm2 = np.random.permutation(25)
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Rrow = np.arange(25)
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Rcol = rand_perm
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Rdata = np.ones(25, dtype=int)
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Rmat = coo_matrix((Rdata, (Rrow, Rcol))).tocsr()
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Crow = rand_perm2
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Ccol = np.arange(25)
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Cdata = np.ones(25, dtype=int)
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Cmat = coo_matrix((Cdata, (Crow, Ccol))).tocsr()
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# Randomly permute identity matrix
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B = Rmat * A * Cmat
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# Row permute
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perm = maximum_bipartite_matching(B, perm_type='row')
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Rrow = np.arange(25)
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Rcol = perm
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Rdata = np.ones(25, dtype=int)
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Rmat = coo_matrix((Rdata, (Rrow, Rcol))).tocsr()
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C1 = Rmat * B
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# Column permute
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perm2 = maximum_bipartite_matching(B, perm_type='column')
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Crow = perm2
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Ccol = np.arange(25)
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Cdata = np.ones(25, dtype=int)
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Cmat = coo_matrix((Cdata, (Crow, Ccol))).tocsr()
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C2 = B * Cmat
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# Should get identity matrix back
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assert_equal(any(C1.diagonal() == 0), False)
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assert_equal(any(C2.diagonal() == 0), False)
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