85 lines
3.0 KiB
Python
85 lines
3.0 KiB
Python
#!/usr/bin/env python
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import pytest
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np = pytest.importorskip('numpy')
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sp = pytest.importorskip('scipy')
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sparse = pytest.importorskip('scipy.sparse')
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import networkx as nx
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from networkx.algorithms import bipartite
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from networkx.testing.utils import assert_edges_equal
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class TestBiadjacencyMatrix:
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def test_biadjacency_matrix_weight(self):
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G = nx.path_graph(5)
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G.add_edge(0, 1, weight=2, other=4)
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X = [1, 3]
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Y = [0, 2, 4]
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M = bipartite.biadjacency_matrix(G, X, weight='weight')
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assert M[0, 0] == 2
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M = bipartite.biadjacency_matrix(G, X, weight='other')
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assert M[0, 0] == 4
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def test_biadjacency_matrix(self):
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tops = [2, 5, 10]
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bots = [5, 10, 15]
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for i in range(len(tops)):
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G = bipartite.random_graph(tops[i], bots[i], 0.2)
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top = [n for n, d in G.nodes(data=True) if d['bipartite'] == 0]
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M = bipartite.biadjacency_matrix(G, top)
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assert M.shape[0] == tops[i]
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assert M.shape[1] == bots[i]
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def test_biadjacency_matrix_order(self):
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G = nx.path_graph(5)
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G.add_edge(0, 1, weight=2)
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X = [3, 1]
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Y = [4, 2, 0]
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M = bipartite.biadjacency_matrix(G, X, Y, weight='weight')
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assert M[1, 2] == 2
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def test_null_graph(self):
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with pytest.raises(nx.NetworkXError):
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bipartite.biadjacency_matrix(nx.Graph(), [])
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def test_empty_graph(self):
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with pytest.raises(nx.NetworkXError):
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bipartite.biadjacency_matrix(nx.Graph([(1, 0)]), [])
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def test_duplicate_row(self):
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with pytest.raises(nx.NetworkXError):
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bipartite.biadjacency_matrix(nx.Graph([(1, 0)]), [1, 1])
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def test_duplicate_col(self):
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with pytest.raises(nx.NetworkXError):
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bipartite.biadjacency_matrix(nx.Graph([(1, 0)]), [0], [1, 1])
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def test_duplicate_col(self):
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with pytest.raises(nx.NetworkXError):
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bipartite.biadjacency_matrix(nx.Graph([(1, 0)]), [0], [1, 1])
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def test_format_keyword(self):
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with pytest.raises(nx.NetworkXError):
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bipartite.biadjacency_matrix(nx.Graph([(1, 0)]), [0], format='foo')
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def test_from_biadjacency_roundtrip(self):
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B1 = nx.path_graph(5)
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M = bipartite.biadjacency_matrix(B1, [0, 2, 4])
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B2 = bipartite.from_biadjacency_matrix(M)
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assert nx.is_isomorphic(B1, B2)
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def test_from_biadjacency_weight(self):
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M = sparse.csc_matrix([[1, 2], [0, 3]])
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B = bipartite.from_biadjacency_matrix(M)
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assert_edges_equal(B.edges(), [(0, 2), (0, 3), (1, 3)])
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B = bipartite.from_biadjacency_matrix(M, edge_attribute='weight')
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e = [(0, 2, {'weight': 1}), (0, 3, {'weight': 2}), (1, 3, {'weight': 3})]
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assert_edges_equal(B.edges(data=True), e)
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def test_from_biadjacency_multigraph(self):
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M = sparse.csc_matrix([[1, 2], [0, 3]])
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B = bipartite.from_biadjacency_matrix(M, create_using=nx.MultiGraph())
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assert_edges_equal(B.edges(), [(0, 2), (0, 3), (0, 3), (1, 3), (1, 3), (1, 3)])
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