Mercurial > repos > shellac > sam_consensus_v3
diff env/lib/python3.9/site-packages/networkx/algorithms/centrality/tests/test_load_centrality.py @ 0:4f3585e2f14b draft default tip
"planemo upload commit 60cee0fc7c0cda8592644e1aad72851dec82c959"
author | shellac |
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date | Mon, 22 Mar 2021 18:12:50 +0000 |
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--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/env/lib/python3.9/site-packages/networkx/algorithms/centrality/tests/test_load_centrality.py Mon Mar 22 18:12:50 2021 +0000 @@ -0,0 +1,336 @@ +import networkx as nx +from networkx.testing import almost_equal + + +class TestLoadCentrality: + @classmethod + def setup_class(cls): + + G = nx.Graph() + G.add_edge(0, 1, weight=3) + G.add_edge(0, 2, weight=2) + G.add_edge(0, 3, weight=6) + G.add_edge(0, 4, weight=4) + G.add_edge(1, 3, weight=5) + G.add_edge(1, 5, weight=5) + G.add_edge(2, 4, weight=1) + G.add_edge(3, 4, weight=2) + G.add_edge(3, 5, weight=1) + G.add_edge(4, 5, weight=4) + cls.G = G + cls.exact_weighted = {0: 4.0, 1: 0.0, 2: 8.0, 3: 6.0, 4: 8.0, 5: 0.0} + cls.K = nx.krackhardt_kite_graph() + cls.P3 = nx.path_graph(3) + cls.P4 = nx.path_graph(4) + cls.K5 = nx.complete_graph(5) + + cls.C4 = nx.cycle_graph(4) + cls.T = nx.balanced_tree(r=2, h=2) + cls.Gb = nx.Graph() + cls.Gb.add_edges_from([(0, 1), (0, 2), (1, 3), (2, 3), (2, 4), (4, 5), (3, 5)]) + cls.F = nx.florentine_families_graph() + cls.LM = nx.les_miserables_graph() + cls.D = nx.cycle_graph(3, create_using=nx.DiGraph()) + cls.D.add_edges_from([(3, 0), (4, 3)]) + + def test_not_strongly_connected(self): + b = nx.load_centrality(self.D) + result = {0: 5.0 / 12, 1: 1.0 / 4, 2: 1.0 / 12, 3: 1.0 / 4, 4: 0.000} + for n in sorted(self.D): + assert almost_equal(result[n], b[n], places=3) + assert almost_equal(result[n], nx.load_centrality(self.D, n), places=3) + + def test_weighted_load(self): + b = nx.load_centrality(self.G, weight="weight", normalized=False) + for n in sorted(self.G): + assert b[n] == self.exact_weighted[n] + + def test_k5_load(self): + G = self.K5 + c = nx.load_centrality(G) + d = {0: 0.000, 1: 0.000, 2: 0.000, 3: 0.000, 4: 0.000} + for n in sorted(G): + assert almost_equal(c[n], d[n], places=3) + + def test_p3_load(self): + G = self.P3 + c = nx.load_centrality(G) + d = {0: 0.000, 1: 1.000, 2: 0.000} + for n in sorted(G): + assert almost_equal(c[n], d[n], places=3) + c = nx.load_centrality(G, v=1) + assert almost_equal(c, 1.0) + c = nx.load_centrality(G, v=1, normalized=True) + assert almost_equal(c, 1.0) + + def test_p2_load(self): + G = nx.path_graph(2) + c = nx.load_centrality(G) + d = {0: 0.000, 1: 0.000} + for n in sorted(G): + assert almost_equal(c[n], d[n], places=3) + + def test_krackhardt_load(self): + G = self.K + c = nx.load_centrality(G) + d = { + 0: 0.023, + 1: 0.023, + 2: 0.000, + 3: 0.102, + 4: 0.000, + 5: 0.231, + 6: 0.231, + 7: 0.389, + 8: 0.222, + 9: 0.000, + } + for n in sorted(G): + assert almost_equal(c[n], d[n], places=3) + + def test_florentine_families_load(self): + G = self.F + c = nx.load_centrality(G) + d = { + "Acciaiuoli": 0.000, + "Albizzi": 0.211, + "Barbadori": 0.093, + "Bischeri": 0.104, + "Castellani": 0.055, + "Ginori": 0.000, + "Guadagni": 0.251, + "Lamberteschi": 0.000, + "Medici": 0.522, + "Pazzi": 0.000, + "Peruzzi": 0.022, + "Ridolfi": 0.117, + "Salviati": 0.143, + "Strozzi": 0.106, + "Tornabuoni": 0.090, + } + for n in sorted(G): + assert almost_equal(c[n], d[n], places=3) + + def test_les_miserables_load(self): + G = self.LM + c = nx.load_centrality(G) + d = { + "Napoleon": 0.000, + "Myriel": 0.177, + "MlleBaptistine": 0.000, + "MmeMagloire": 0.000, + "CountessDeLo": 0.000, + "Geborand": 0.000, + "Champtercier": 0.000, + "Cravatte": 0.000, + "Count": 0.000, + "OldMan": 0.000, + "Valjean": 0.567, + "Labarre": 0.000, + "Marguerite": 0.000, + "MmeDeR": 0.000, + "Isabeau": 0.000, + "Gervais": 0.000, + "Listolier": 0.000, + "Tholomyes": 0.043, + "Fameuil": 0.000, + "Blacheville": 0.000, + "Favourite": 0.000, + "Dahlia": 0.000, + "Zephine": 0.000, + "Fantine": 0.128, + "MmeThenardier": 0.029, + "Thenardier": 0.075, + "Cosette": 0.024, + "Javert": 0.054, + "Fauchelevent": 0.026, + "Bamatabois": 0.008, + "Perpetue": 0.000, + "Simplice": 0.009, + "Scaufflaire": 0.000, + "Woman1": 0.000, + "Judge": 0.000, + "Champmathieu": 0.000, + "Brevet": 0.000, + "Chenildieu": 0.000, + "Cochepaille": 0.000, + "Pontmercy": 0.007, + "Boulatruelle": 0.000, + "Eponine": 0.012, + "Anzelma": 0.000, + "Woman2": 0.000, + "MotherInnocent": 0.000, + "Gribier": 0.000, + "MmeBurgon": 0.026, + "Jondrette": 0.000, + "Gavroche": 0.164, + "Gillenormand": 0.021, + "Magnon": 0.000, + "MlleGillenormand": 0.047, + "MmePontmercy": 0.000, + "MlleVaubois": 0.000, + "LtGillenormand": 0.000, + "Marius": 0.133, + "BaronessT": 0.000, + "Mabeuf": 0.028, + "Enjolras": 0.041, + "Combeferre": 0.001, + "Prouvaire": 0.000, + "Feuilly": 0.001, + "Courfeyrac": 0.006, + "Bahorel": 0.002, + "Bossuet": 0.032, + "Joly": 0.002, + "Grantaire": 0.000, + "MotherPlutarch": 0.000, + "Gueulemer": 0.005, + "Babet": 0.005, + "Claquesous": 0.005, + "Montparnasse": 0.004, + "Toussaint": 0.000, + "Child1": 0.000, + "Child2": 0.000, + "Brujon": 0.000, + "MmeHucheloup": 0.000, + } + for n in sorted(G): + assert almost_equal(c[n], d[n], places=3) + + def test_unnormalized_k5_load(self): + G = self.K5 + c = nx.load_centrality(G, normalized=False) + d = {0: 0.000, 1: 0.000, 2: 0.000, 3: 0.000, 4: 0.000} + for n in sorted(G): + assert almost_equal(c[n], d[n], places=3) + + def test_unnormalized_p3_load(self): + G = self.P3 + c = nx.load_centrality(G, normalized=False) + d = {0: 0.000, 1: 2.000, 2: 0.000} + for n in sorted(G): + assert almost_equal(c[n], d[n], places=3) + + def test_unnormalized_krackhardt_load(self): + G = self.K + c = nx.load_centrality(G, normalized=False) + d = { + 0: 1.667, + 1: 1.667, + 2: 0.000, + 3: 7.333, + 4: 0.000, + 5: 16.667, + 6: 16.667, + 7: 28.000, + 8: 16.000, + 9: 0.000, + } + + for n in sorted(G): + assert almost_equal(c[n], d[n], places=3) + + def test_unnormalized_florentine_families_load(self): + G = self.F + c = nx.load_centrality(G, normalized=False) + + d = { + "Acciaiuoli": 0.000, + "Albizzi": 38.333, + "Barbadori": 17.000, + "Bischeri": 19.000, + "Castellani": 10.000, + "Ginori": 0.000, + "Guadagni": 45.667, + "Lamberteschi": 0.000, + "Medici": 95.000, + "Pazzi": 0.000, + "Peruzzi": 4.000, + "Ridolfi": 21.333, + "Salviati": 26.000, + "Strozzi": 19.333, + "Tornabuoni": 16.333, + } + for n in sorted(G): + assert almost_equal(c[n], d[n], places=3) + + def test_load_betweenness_difference(self): + # Difference Between Load and Betweenness + # --------------------------------------- The smallest graph + # that shows the difference between load and betweenness is + # G=ladder_graph(3) (Graph B below) + + # Graph A and B are from Tao Zhou, Jian-Guo Liu, Bing-Hong + # Wang: Comment on "Scientific collaboration + # networks. II. Shortest paths, weighted networks, and + # centrality". https://arxiv.org/pdf/physics/0511084 + + # Notice that unlike here, their calculation adds to 1 to the + # betweennes of every node i for every path from i to every + # other node. This is exactly what it should be, based on + # Eqn. (1) in their paper: the eqn is B(v) = \sum_{s\neq t, + # s\neq v}{\frac{\sigma_{st}(v)}{\sigma_{st}}}, therefore, + # they allow v to be the target node. + + # We follow Brandes 2001, who follows Freeman 1977 that make + # the sum for betweenness of v exclude paths where v is either + # the source or target node. To agree with their numbers, we + # must additionally, remove edge (4,8) from the graph, see AC + # example following (there is a mistake in the figure in their + # paper - personal communication). + + # A = nx.Graph() + # A.add_edges_from([(0,1), (1,2), (1,3), (2,4), + # (3,5), (4,6), (4,7), (4,8), + # (5,8), (6,9), (7,9), (8,9)]) + B = nx.Graph() # ladder_graph(3) + B.add_edges_from([(0, 1), (0, 2), (1, 3), (2, 3), (2, 4), (4, 5), (3, 5)]) + c = nx.load_centrality(B, normalized=False) + d = {0: 1.750, 1: 1.750, 2: 6.500, 3: 6.500, 4: 1.750, 5: 1.750} + for n in sorted(B): + assert almost_equal(c[n], d[n], places=3) + + def test_c4_edge_load(self): + G = self.C4 + c = nx.edge_load_centrality(G) + d = {(0, 1): 6.000, (0, 3): 6.000, (1, 2): 6.000, (2, 3): 6.000} + for n in G.edges(): + assert almost_equal(c[n], d[n], places=3) + + def test_p4_edge_load(self): + G = self.P4 + c = nx.edge_load_centrality(G) + d = {(0, 1): 6.000, (1, 2): 8.000, (2, 3): 6.000} + for n in G.edges(): + assert almost_equal(c[n], d[n], places=3) + + def test_k5_edge_load(self): + G = self.K5 + c = nx.edge_load_centrality(G) + d = { + (0, 1): 5.000, + (0, 2): 5.000, + (0, 3): 5.000, + (0, 4): 5.000, + (1, 2): 5.000, + (1, 3): 5.000, + (1, 4): 5.000, + (2, 3): 5.000, + (2, 4): 5.000, + (3, 4): 5.000, + } + for n in G.edges(): + assert almost_equal(c[n], d[n], places=3) + + def test_tree_edge_load(self): + G = self.T + c = nx.edge_load_centrality(G) + d = { + (0, 1): 24.000, + (0, 2): 24.000, + (1, 3): 12.000, + (1, 4): 12.000, + (2, 5): 12.000, + (2, 6): 12.000, + } + for n in G.edges(): + assert almost_equal(c[n], d[n], places=3)