Mercurial > repos > shellac > sam_consensus_v3
diff env/lib/python3.9/site-packages/networkx/algorithms/centrality/tests/test_betweenness_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_betweenness_centrality.py Mon Mar 22 18:12:50 2021 +0000 @@ -0,0 +1,657 @@ +import networkx as nx +from networkx.testing import almost_equal + + +def weighted_G(): + 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) + return G + + +class TestBetweennessCentrality: + def test_K5(self): + """Betweenness centrality: K5""" + G = nx.complete_graph(5) + b = nx.betweenness_centrality(G, weight=None, normalized=False) + b_answer = {0: 0.0, 1: 0.0, 2: 0.0, 3: 0.0, 4: 0.0} + for n in sorted(G): + assert almost_equal(b[n], b_answer[n]) + + def test_K5_endpoints(self): + """Betweenness centrality: K5 endpoints""" + G = nx.complete_graph(5) + b = nx.betweenness_centrality(G, weight=None, normalized=False, endpoints=True) + b_answer = {0: 4.0, 1: 4.0, 2: 4.0, 3: 4.0, 4: 4.0} + for n in sorted(G): + assert almost_equal(b[n], b_answer[n]) + # normalized = True case + b = nx.betweenness_centrality(G, weight=None, normalized=True, endpoints=True) + b_answer = {0: 0.4, 1: 0.4, 2: 0.4, 3: 0.4, 4: 0.4} + for n in sorted(G): + assert almost_equal(b[n], b_answer[n]) + + def test_P3_normalized(self): + """Betweenness centrality: P3 normalized""" + G = nx.path_graph(3) + b = nx.betweenness_centrality(G, weight=None, normalized=True) + b_answer = {0: 0.0, 1: 1.0, 2: 0.0} + for n in sorted(G): + assert almost_equal(b[n], b_answer[n]) + + def test_P3(self): + """Betweenness centrality: P3""" + G = nx.path_graph(3) + b_answer = {0: 0.0, 1: 1.0, 2: 0.0} + b = nx.betweenness_centrality(G, weight=None, normalized=False) + for n in sorted(G): + assert almost_equal(b[n], b_answer[n]) + + def test_sample_from_P3(self): + G = nx.path_graph(3) + b_answer = {0: 0.0, 1: 1.0, 2: 0.0} + b = nx.betweenness_centrality(G, k=3, weight=None, normalized=False, seed=1) + for n in sorted(G): + assert almost_equal(b[n], b_answer[n]) + b = nx.betweenness_centrality(G, k=2, weight=None, normalized=False, seed=1) + # python versions give different results with same seed + b_approx1 = {0: 0.0, 1: 1.5, 2: 0.0} + b_approx2 = {0: 0.0, 1: 0.75, 2: 0.0} + for n in sorted(G): + assert b[n] in (b_approx1[n], b_approx2[n]) + + def test_P3_endpoints(self): + """Betweenness centrality: P3 endpoints""" + G = nx.path_graph(3) + b_answer = {0: 2.0, 1: 3.0, 2: 2.0} + b = nx.betweenness_centrality(G, weight=None, normalized=False, endpoints=True) + for n in sorted(G): + assert almost_equal(b[n], b_answer[n]) + # normalized = True case + b_answer = {0: 2 / 3, 1: 1.0, 2: 2 / 3} + b = nx.betweenness_centrality(G, weight=None, normalized=True, endpoints=True) + for n in sorted(G): + assert almost_equal(b[n], b_answer[n]) + + def test_krackhardt_kite_graph(self): + """Betweenness centrality: Krackhardt kite graph""" + G = nx.krackhardt_kite_graph() + b_answer = { + 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 b in b_answer: + b_answer[b] /= 2 + b = nx.betweenness_centrality(G, weight=None, normalized=False) + for n in sorted(G): + assert almost_equal(b[n], b_answer[n], places=3) + + def test_krackhardt_kite_graph_normalized(self): + """Betweenness centrality: Krackhardt kite graph normalized""" + G = nx.krackhardt_kite_graph() + b_answer = { + 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, + } + b = nx.betweenness_centrality(G, weight=None, normalized=True) + for n in sorted(G): + assert almost_equal(b[n], b_answer[n], places=3) + + def test_florentine_families_graph(self): + """Betweenness centrality: Florentine families graph""" + G = nx.florentine_families_graph() + b_answer = { + "Acciaiuoli": 0.000, + "Albizzi": 0.212, + "Barbadori": 0.093, + "Bischeri": 0.104, + "Castellani": 0.055, + "Ginori": 0.000, + "Guadagni": 0.255, + "Lamberteschi": 0.000, + "Medici": 0.522, + "Pazzi": 0.000, + "Peruzzi": 0.022, + "Ridolfi": 0.114, + "Salviati": 0.143, + "Strozzi": 0.103, + "Tornabuoni": 0.092, + } + + b = nx.betweenness_centrality(G, weight=None, normalized=True) + for n in sorted(G): + assert almost_equal(b[n], b_answer[n], places=3) + + def test_les_miserables_graph(self): + """Betweenness centrality: Les Miserables graph""" + G = nx.les_miserables_graph() + b_answer = { + "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": 0.570, + "Labarre": 0.000, + "Marguerite": 0.000, + "MmeDeR": 0.000, + "Isabeau": 0.000, + "Gervais": 0.000, + "Listolier": 0.000, + "Tholomyes": 0.041, + "Fameuil": 0.000, + "Blacheville": 0.000, + "Favourite": 0.000, + "Dahlia": 0.000, + "Zephine": 0.000, + "Fantine": 0.130, + "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": 0.011, + "Anzelma": 0.000, + "Woman2": 0.000, + "MotherInnocent": 0.000, + "Gribier": 0.000, + "MmeBurgon": 0.026, + "Jondrette": 0.000, + "Gavroche": 0.165, + "Gillenormand": 0.020, + "Magnon": 0.000, + "MlleGillenormand": 0.048, + "MmePontmercy": 0.000, + "MlleVaubois": 0.000, + "LtGillenormand": 0.000, + "Marius": 0.132, + "BaronessT": 0.000, + "Mabeuf": 0.028, + "Enjolras": 0.043, + "Combeferre": 0.001, + "Prouvaire": 0.000, + "Feuilly": 0.001, + "Courfeyrac": 0.005, + "Bahorel": 0.002, + "Bossuet": 0.031, + "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, + } + + b = nx.betweenness_centrality(G, weight=None, normalized=True) + for n in sorted(G): + assert almost_equal(b[n], b_answer[n], places=3) + + def test_ladder_graph(self): + """Betweenness centrality: Ladder graph""" + G = nx.Graph() # ladder_graph(3) + G.add_edges_from([(0, 1), (0, 2), (1, 3), (2, 3), (2, 4), (4, 5), (3, 5)]) + b_answer = {0: 1.667, 1: 1.667, 2: 6.667, 3: 6.667, 4: 1.667, 5: 1.667} + for b in b_answer: + b_answer[b] /= 2 + b = nx.betweenness_centrality(G, weight=None, normalized=False) + for n in sorted(G): + assert almost_equal(b[n], b_answer[n], places=3) + + def test_disconnected_path(self): + """Betweenness centrality: disconnected path""" + G = nx.Graph() + nx.add_path(G, [0, 1, 2]) + nx.add_path(G, [3, 4, 5, 6]) + b_answer = {0: 0, 1: 1, 2: 0, 3: 0, 4: 2, 5: 2, 6: 0} + b = nx.betweenness_centrality(G, weight=None, normalized=False) + for n in sorted(G): + assert almost_equal(b[n], b_answer[n]) + + def test_disconnected_path_endpoints(self): + """Betweenness centrality: disconnected path endpoints""" + G = nx.Graph() + nx.add_path(G, [0, 1, 2]) + nx.add_path(G, [3, 4, 5, 6]) + b_answer = {0: 2, 1: 3, 2: 2, 3: 3, 4: 5, 5: 5, 6: 3} + b = nx.betweenness_centrality(G, weight=None, normalized=False, endpoints=True) + for n in sorted(G): + assert almost_equal(b[n], b_answer[n]) + # normalized = True case + b = nx.betweenness_centrality(G, weight=None, normalized=True, endpoints=True) + for n in sorted(G): + assert almost_equal(b[n], b_answer[n] / 21) + + def test_directed_path(self): + """Betweenness centrality: directed path""" + G = nx.DiGraph() + nx.add_path(G, [0, 1, 2]) + b = nx.betweenness_centrality(G, weight=None, normalized=False) + b_answer = {0: 0.0, 1: 1.0, 2: 0.0} + for n in sorted(G): + assert almost_equal(b[n], b_answer[n]) + + def test_directed_path_normalized(self): + """Betweenness centrality: directed path normalized""" + G = nx.DiGraph() + nx.add_path(G, [0, 1, 2]) + b = nx.betweenness_centrality(G, weight=None, normalized=True) + b_answer = {0: 0.0, 1: 0.5, 2: 0.0} + for n in sorted(G): + assert almost_equal(b[n], b_answer[n]) + + +class TestWeightedBetweennessCentrality: + def test_K5(self): + """Weighted betweenness centrality: K5""" + G = nx.complete_graph(5) + b = nx.betweenness_centrality(G, weight="weight", normalized=False) + b_answer = {0: 0.0, 1: 0.0, 2: 0.0, 3: 0.0, 4: 0.0} + for n in sorted(G): + assert almost_equal(b[n], b_answer[n]) + + def test_P3_normalized(self): + """Weighted betweenness centrality: P3 normalized""" + G = nx.path_graph(3) + b = nx.betweenness_centrality(G, weight="weight", normalized=True) + b_answer = {0: 0.0, 1: 1.0, 2: 0.0} + for n in sorted(G): + assert almost_equal(b[n], b_answer[n]) + + def test_P3(self): + """Weighted betweenness centrality: P3""" + G = nx.path_graph(3) + b_answer = {0: 0.0, 1: 1.0, 2: 0.0} + b = nx.betweenness_centrality(G, weight="weight", normalized=False) + for n in sorted(G): + assert almost_equal(b[n], b_answer[n]) + + def test_krackhardt_kite_graph(self): + """Weighted betweenness centrality: Krackhardt kite graph""" + G = nx.krackhardt_kite_graph() + b_answer = { + 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 b in b_answer: + b_answer[b] /= 2 + + b = nx.betweenness_centrality(G, weight="weight", normalized=False) + + for n in sorted(G): + assert almost_equal(b[n], b_answer[n], places=3) + + def test_krackhardt_kite_graph_normalized(self): + """Weighted betweenness centrality: + Krackhardt kite graph normalized + """ + G = nx.krackhardt_kite_graph() + b_answer = { + 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, + } + b = nx.betweenness_centrality(G, weight="weight", normalized=True) + + for n in sorted(G): + assert almost_equal(b[n], b_answer[n], places=3) + + def test_florentine_families_graph(self): + """Weighted betweenness centrality: + Florentine families graph""" + G = nx.florentine_families_graph() + b_answer = { + "Acciaiuoli": 0.000, + "Albizzi": 0.212, + "Barbadori": 0.093, + "Bischeri": 0.104, + "Castellani": 0.055, + "Ginori": 0.000, + "Guadagni": 0.255, + "Lamberteschi": 0.000, + "Medici": 0.522, + "Pazzi": 0.000, + "Peruzzi": 0.022, + "Ridolfi": 0.114, + "Salviati": 0.143, + "Strozzi": 0.103, + "Tornabuoni": 0.092, + } + + b = nx.betweenness_centrality(G, weight="weight", normalized=True) + for n in sorted(G): + assert almost_equal(b[n], b_answer[n], places=3) + + def test_les_miserables_graph(self): + """Weighted betweenness centrality: Les Miserables graph""" + G = nx.les_miserables_graph() + b_answer = { + "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": 0.454, + "Labarre": 0.000, + "Marguerite": 0.009, + "MmeDeR": 0.000, + "Isabeau": 0.000, + "Gervais": 0.000, + "Listolier": 0.000, + "Tholomyes": 0.066, + "Fameuil": 0.000, + "Blacheville": 0.000, + "Favourite": 0.000, + "Dahlia": 0.000, + "Zephine": 0.000, + "Fantine": 0.114, + "MmeThenardier": 0.046, + "Thenardier": 0.129, + "Cosette": 0.075, + "Javert": 0.193, + "Fauchelevent": 0.026, + "Bamatabois": 0.080, + "Perpetue": 0.000, + "Simplice": 0.001, + "Scaufflaire": 0.000, + "Woman1": 0.000, + "Judge": 0.000, + "Champmathieu": 0.000, + "Brevet": 0.000, + "Chenildieu": 0.000, + "Cochepaille": 0.000, + "Pontmercy": 0.023, + "Boulatruelle": 0.000, + "Eponine": 0.023, + "Anzelma": 0.000, + "Woman2": 0.000, + "MotherInnocent": 0.000, + "Gribier": 0.000, + "MmeBurgon": 0.026, + "Jondrette": 0.000, + "Gavroche": 0.285, + "Gillenormand": 0.024, + "Magnon": 0.005, + "MlleGillenormand": 0.036, + "MmePontmercy": 0.005, + "MlleVaubois": 0.000, + "LtGillenormand": 0.015, + "Marius": 0.072, + "BaronessT": 0.004, + "Mabeuf": 0.089, + "Enjolras": 0.003, + "Combeferre": 0.000, + "Prouvaire": 0.000, + "Feuilly": 0.004, + "Courfeyrac": 0.001, + "Bahorel": 0.007, + "Bossuet": 0.028, + "Joly": 0.000, + "Grantaire": 0.036, + "MotherPlutarch": 0.000, + "Gueulemer": 0.025, + "Babet": 0.015, + "Claquesous": 0.042, + "Montparnasse": 0.050, + "Toussaint": 0.011, + "Child1": 0.000, + "Child2": 0.000, + "Brujon": 0.002, + "MmeHucheloup": 0.034, + } + + b = nx.betweenness_centrality(G, weight="weight", normalized=True) + for n in sorted(G): + assert almost_equal(b[n], b_answer[n], places=3) + + def test_ladder_graph(self): + """Weighted betweenness centrality: Ladder graph""" + G = nx.Graph() # ladder_graph(3) + G.add_edges_from([(0, 1), (0, 2), (1, 3), (2, 3), (2, 4), (4, 5), (3, 5)]) + b_answer = {0: 1.667, 1: 1.667, 2: 6.667, 3: 6.667, 4: 1.667, 5: 1.667} + for b in b_answer: + b_answer[b] /= 2 + b = nx.betweenness_centrality(G, weight="weight", normalized=False) + for n in sorted(G): + assert almost_equal(b[n], b_answer[n], places=3) + + def test_G(self): + """Weighted betweenness centrality: G""" + G = weighted_G() + b_answer = {0: 2.0, 1: 0.0, 2: 4.0, 3: 3.0, 4: 4.0, 5: 0.0} + b = nx.betweenness_centrality(G, weight="weight", normalized=False) + for n in sorted(G): + assert almost_equal(b[n], b_answer[n]) + + def test_G2(self): + """Weighted betweenness centrality: G2""" + G = nx.DiGraph() + G.add_weighted_edges_from( + [ + ("s", "u", 10), + ("s", "x", 5), + ("u", "v", 1), + ("u", "x", 2), + ("v", "y", 1), + ("x", "u", 3), + ("x", "v", 5), + ("x", "y", 2), + ("y", "s", 7), + ("y", "v", 6), + ] + ) + + b_answer = {"y": 5.0, "x": 5.0, "s": 4.0, "u": 2.0, "v": 2.0} + + b = nx.betweenness_centrality(G, weight="weight", normalized=False) + for n in sorted(G): + assert almost_equal(b[n], b_answer[n]) + + +class TestEdgeBetweennessCentrality: + def test_K5(self): + """Edge betweenness centrality: K5""" + G = nx.complete_graph(5) + b = nx.edge_betweenness_centrality(G, weight=None, normalized=False) + b_answer = dict.fromkeys(G.edges(), 1) + for n in sorted(G.edges()): + assert almost_equal(b[n], b_answer[n]) + + def test_normalized_K5(self): + """Edge betweenness centrality: K5""" + G = nx.complete_graph(5) + b = nx.edge_betweenness_centrality(G, weight=None, normalized=True) + b_answer = dict.fromkeys(G.edges(), 1 / 10) + for n in sorted(G.edges()): + assert almost_equal(b[n], b_answer[n]) + + def test_C4(self): + """Edge betweenness centrality: C4""" + G = nx.cycle_graph(4) + b = nx.edge_betweenness_centrality(G, weight=None, normalized=True) + b_answer = {(0, 1): 2, (0, 3): 2, (1, 2): 2, (2, 3): 2} + for n in sorted(G.edges()): + assert almost_equal(b[n], b_answer[n] / 6) + + def test_P4(self): + """Edge betweenness centrality: P4""" + G = nx.path_graph(4) + b = nx.edge_betweenness_centrality(G, weight=None, normalized=False) + b_answer = {(0, 1): 3, (1, 2): 4, (2, 3): 3} + for n in sorted(G.edges()): + assert almost_equal(b[n], b_answer[n]) + + def test_normalized_P4(self): + """Edge betweenness centrality: P4""" + G = nx.path_graph(4) + b = nx.edge_betweenness_centrality(G, weight=None, normalized=True) + b_answer = {(0, 1): 3, (1, 2): 4, (2, 3): 3} + for n in sorted(G.edges()): + assert almost_equal(b[n], b_answer[n] / 6) + + def test_balanced_tree(self): + """Edge betweenness centrality: balanced tree""" + G = nx.balanced_tree(r=2, h=2) + b = nx.edge_betweenness_centrality(G, weight=None, normalized=False) + b_answer = {(0, 1): 12, (0, 2): 12, (1, 3): 6, (1, 4): 6, (2, 5): 6, (2, 6): 6} + for n in sorted(G.edges()): + assert almost_equal(b[n], b_answer[n]) + + +class TestWeightedEdgeBetweennessCentrality: + def test_K5(self): + """Edge betweenness centrality: K5""" + G = nx.complete_graph(5) + b = nx.edge_betweenness_centrality(G, weight="weight", normalized=False) + b_answer = dict.fromkeys(G.edges(), 1) + for n in sorted(G.edges()): + assert almost_equal(b[n], b_answer[n]) + + def test_C4(self): + """Edge betweenness centrality: C4""" + G = nx.cycle_graph(4) + b = nx.edge_betweenness_centrality(G, weight="weight", normalized=False) + b_answer = {(0, 1): 2, (0, 3): 2, (1, 2): 2, (2, 3): 2} + for n in sorted(G.edges()): + assert almost_equal(b[n], b_answer[n]) + + def test_P4(self): + """Edge betweenness centrality: P4""" + G = nx.path_graph(4) + b = nx.edge_betweenness_centrality(G, weight="weight", normalized=False) + b_answer = {(0, 1): 3, (1, 2): 4, (2, 3): 3} + for n in sorted(G.edges()): + assert almost_equal(b[n], b_answer[n]) + + def test_balanced_tree(self): + """Edge betweenness centrality: balanced tree""" + G = nx.balanced_tree(r=2, h=2) + b = nx.edge_betweenness_centrality(G, weight="weight", normalized=False) + b_answer = {(0, 1): 12, (0, 2): 12, (1, 3): 6, (1, 4): 6, (2, 5): 6, (2, 6): 6} + for n in sorted(G.edges()): + assert almost_equal(b[n], b_answer[n]) + + def test_weighted_graph(self): + eList = [ + (0, 1, 5), + (0, 2, 4), + (0, 3, 3), + (0, 4, 2), + (1, 2, 4), + (1, 3, 1), + (1, 4, 3), + (2, 4, 5), + (3, 4, 4), + ] + G = nx.Graph() + G.add_weighted_edges_from(eList) + b = nx.edge_betweenness_centrality(G, weight="weight", normalized=False) + b_answer = { + (0, 1): 0.0, + (0, 2): 1.0, + (0, 3): 2.0, + (0, 4): 1.0, + (1, 2): 2.0, + (1, 3): 3.5, + (1, 4): 1.5, + (2, 4): 1.0, + (3, 4): 0.5, + } + for n in sorted(G.edges()): + assert almost_equal(b[n], b_answer[n]) + + def test_normalized_weighted_graph(self): + eList = [ + (0, 1, 5), + (0, 2, 4), + (0, 3, 3), + (0, 4, 2), + (1, 2, 4), + (1, 3, 1), + (1, 4, 3), + (2, 4, 5), + (3, 4, 4), + ] + G = nx.Graph() + G.add_weighted_edges_from(eList) + b = nx.edge_betweenness_centrality(G, weight="weight", normalized=True) + b_answer = { + (0, 1): 0.0, + (0, 2): 1.0, + (0, 3): 2.0, + (0, 4): 1.0, + (1, 2): 2.0, + (1, 3): 3.5, + (1, 4): 1.5, + (2, 4): 1.0, + (3, 4): 0.5, + } + norm = len(G) * (len(G) - 1) / 2 + for n in sorted(G.edges()): + assert almost_equal(b[n], b_answer[n] / norm)