123 lines
4.1 KiB
Python
123 lines
4.1 KiB
Python
#!/usr/bin/env python
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import math
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import pytest
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np = pytest.importorskip('numpy')
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scipy = pytest.importorskip('scipy')
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import networkx as nx
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from networkx.testing import almost_equal
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class TestEigenvectorCentrality(object):
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def test_K5(self):
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"""Eigenvector centrality: K5"""
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G = nx.complete_graph(5)
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b = nx.eigenvector_centrality(G)
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v = math.sqrt(1 / 5.0)
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b_answer = dict.fromkeys(G, v)
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for n in sorted(G):
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assert almost_equal(b[n], b_answer[n])
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nstart = dict([(n, 1) for n in G])
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b = nx.eigenvector_centrality(G, nstart=nstart)
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for n in sorted(G):
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assert almost_equal(b[n], b_answer[n])
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b = nx.eigenvector_centrality_numpy(G)
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for n in sorted(G):
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assert almost_equal(b[n], b_answer[n], places=3)
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def test_P3(self):
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"""Eigenvector centrality: P3"""
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G = nx.path_graph(3)
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b_answer = {0: 0.5, 1: 0.7071, 2: 0.5}
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b = nx.eigenvector_centrality_numpy(G)
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for n in sorted(G):
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assert almost_equal(b[n], b_answer[n], places=4)
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b = nx.eigenvector_centrality(G)
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for n in sorted(G):
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assert almost_equal(b[n], b_answer[n], places=4)
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def test_P3_unweighted(self):
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"""Eigenvector centrality: P3"""
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G = nx.path_graph(3)
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b_answer = {0: 0.5, 1: 0.7071, 2: 0.5}
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b = nx.eigenvector_centrality_numpy(G, weight=None)
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for n in sorted(G):
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assert almost_equal(b[n], b_answer[n], places=4)
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def test_maxiter(self):
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with pytest.raises(nx.PowerIterationFailedConvergence):
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G = nx.path_graph(3)
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b = nx.eigenvector_centrality(G, max_iter=0)
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class TestEigenvectorCentralityDirected(object):
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@classmethod
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def setup_class(cls):
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G = nx.DiGraph()
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edges = [(1, 2), (1, 3), (2, 4), (3, 2), (3, 5), (4, 2), (4, 5), (4, 6),
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(5, 6), (5, 7), (5, 8), (6, 8), (7, 1), (7, 5),
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(7, 8), (8, 6), (8, 7)]
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G.add_edges_from(edges, weight=2.0)
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cls.G = G.reverse()
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cls.G.evc = [0.25368793, 0.19576478, 0.32817092, 0.40430835,
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0.48199885, 0.15724483, 0.51346196, 0.32475403]
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H = nx.DiGraph()
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edges = [(1, 2), (1, 3), (2, 4), (3, 2), (3, 5), (4, 2), (4, 5), (4, 6),
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(5, 6), (5, 7), (5, 8), (6, 8), (7, 1), (7, 5),
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(7, 8), (8, 6), (8, 7)]
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G.add_edges_from(edges)
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cls.H = G.reverse()
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cls.H.evc = [0.25368793, 0.19576478, 0.32817092, 0.40430835,
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0.48199885, 0.15724483, 0.51346196, 0.32475403]
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def test_eigenvector_centrality_weighted(self):
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G = self.G
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p = nx.eigenvector_centrality(G)
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for (a, b) in zip(list(p.values()), self.G.evc):
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assert almost_equal(a, b, places=4)
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def test_eigenvector_centrality_weighted_numpy(self):
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G = self.G
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p = nx.eigenvector_centrality_numpy(G)
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for (a, b) in zip(list(p.values()), self.G.evc):
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assert almost_equal(a, b)
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def test_eigenvector_centrality_unweighted(self):
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G = self.H
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p = nx.eigenvector_centrality(G)
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for (a, b) in zip(list(p.values()), self.G.evc):
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assert almost_equal(a, b, places=4)
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def test_eigenvector_centrality_unweighted_numpy(self):
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G = self.H
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p = nx.eigenvector_centrality_numpy(G)
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for (a, b) in zip(list(p.values()), self.G.evc):
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assert almost_equal(a, b)
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class TestEigenvectorCentralityExceptions(object):
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def test_multigraph(self):
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with pytest.raises(nx.NetworkXException):
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e = nx.eigenvector_centrality(nx.MultiGraph())
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def test_multigraph_numpy(self):
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with pytest.raises(nx.NetworkXException):
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e = nx.eigenvector_centrality_numpy(nx.MultiGraph())
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def test_empty(self):
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with pytest.raises(nx.NetworkXException):
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e = nx.eigenvector_centrality(nx.Graph())
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def test_empty_numpy(self):
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with pytest.raises(nx.NetworkXException):
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e = nx.eigenvector_centrality_numpy(nx.Graph())
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