two version of R2R are hereHEADmaster

1 files changed, 192 insertions, 0 deletions

diff --git a/.venv/lib/python3.12/site-packages/networkx/algorithms/bipartite/tests/test_centrality.py b/.venv/lib/python3.12/site-packages/networkx/algorithms/bipartite/tests/test_centrality.py
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+++ b/.venv/lib/python3.12/site-packages/networkx/algorithms/bipartite/tests/test_centrality.py
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+import pytest
+
+import networkx as nx
+from networkx.algorithms import bipartite
+
+
+class TestBipartiteCentrality:
+    @classmethod
+    def setup_class(cls):
+        cls.P4 = nx.path_graph(4)
+        cls.K3 = nx.complete_bipartite_graph(3, 3)
+        cls.C4 = nx.cycle_graph(4)
+        cls.davis = nx.davis_southern_women_graph()
+        cls.top_nodes = [
+            n for n, d in cls.davis.nodes(data=True) if d["bipartite"] == 0
+        ]
+
+    def test_degree_centrality(self):
+        d = bipartite.degree_centrality(self.P4, [1, 3])
+        answer = {0: 0.5, 1: 1.0, 2: 1.0, 3: 0.5}
+        assert d == answer
+        d = bipartite.degree_centrality(self.K3, [0, 1, 2])
+        answer = {0: 1.0, 1: 1.0, 2: 1.0, 3: 1.0, 4: 1.0, 5: 1.0}
+        assert d == answer
+        d = bipartite.degree_centrality(self.C4, [0, 2])
+        answer = {0: 1.0, 1: 1.0, 2: 1.0, 3: 1.0}
+        assert d == answer
+
+    def test_betweenness_centrality(self):
+        c = bipartite.betweenness_centrality(self.P4, [1, 3])
+        answer = {0: 0.0, 1: 1.0, 2: 1.0, 3: 0.0}
+        assert c == answer
+        c = bipartite.betweenness_centrality(self.K3, [0, 1, 2])
+        answer = {0: 0.125, 1: 0.125, 2: 0.125, 3: 0.125, 4: 0.125, 5: 0.125}
+        assert c == answer
+        c = bipartite.betweenness_centrality(self.C4, [0, 2])
+        answer = {0: 0.25, 1: 0.25, 2: 0.25, 3: 0.25}
+        assert c == answer
+
+    def test_closeness_centrality(self):
+        c = bipartite.closeness_centrality(self.P4, [1, 3])
+        answer = {0: 2.0 / 3, 1: 1.0, 2: 1.0, 3: 2.0 / 3}
+        assert c == answer
+        c = bipartite.closeness_centrality(self.K3, [0, 1, 2])
+        answer = {0: 1.0, 1: 1.0, 2: 1.0, 3: 1.0, 4: 1.0, 5: 1.0}
+        assert c == answer
+        c = bipartite.closeness_centrality(self.C4, [0, 2])
+        answer = {0: 1.0, 1: 1.0, 2: 1.0, 3: 1.0}
+        assert c == answer
+        G = nx.Graph()
+        G.add_node(0)
+        G.add_node(1)
+        c = bipartite.closeness_centrality(G, [0])
+        assert c == {0: 0.0, 1: 0.0}
+        c = bipartite.closeness_centrality(G, [1])
+        assert c == {0: 0.0, 1: 0.0}
+
+    def test_bipartite_closeness_centrality_unconnected(self):
+        G = nx.complete_bipartite_graph(3, 3)
+        G.add_edge(6, 7)
+        c = bipartite.closeness_centrality(G, [0, 2, 4, 6], normalized=False)
+        answer = {
+            0: 10.0 / 7,
+            2: 10.0 / 7,
+            4: 10.0 / 7,
+            6: 10.0,
+            1: 10.0 / 7,
+            3: 10.0 / 7,
+            5: 10.0 / 7,
+            7: 10.0,
+        }
+        assert c == answer
+
+    def test_davis_degree_centrality(self):
+        G = self.davis
+        deg = bipartite.degree_centrality(G, self.top_nodes)
+        answer = {
+            "E8": 0.78,
+            "E9": 0.67,
+            "E7": 0.56,
+            "Nora Fayette": 0.57,
+            "Evelyn Jefferson": 0.57,
+            "Theresa Anderson": 0.57,
+            "E6": 0.44,
+            "Sylvia Avondale": 0.50,
+            "Laura Mandeville": 0.50,
+            "Brenda Rogers": 0.50,
+            "Katherina Rogers": 0.43,
+            "E5": 0.44,
+            "Helen Lloyd": 0.36,
+            "E3": 0.33,
+            "Ruth DeSand": 0.29,
+            "Verne Sanderson": 0.29,
+            "E12": 0.33,
+            "Myra Liddel": 0.29,
+            "E11": 0.22,
+            "Eleanor Nye": 0.29,
+            "Frances Anderson": 0.29,
+            "Pearl Oglethorpe": 0.21,
+            "E4": 0.22,
+            "Charlotte McDowd": 0.29,
+            "E10": 0.28,
+            "Olivia Carleton": 0.14,
+            "Flora Price": 0.14,
+            "E2": 0.17,
+            "E1": 0.17,
+            "Dorothy Murchison": 0.14,
+            "E13": 0.17,
+            "E14": 0.17,
+        }
+        for node, value in answer.items():
+            assert value == pytest.approx(deg[node], abs=1e-2)
+
+    def test_davis_betweenness_centrality(self):
+        G = self.davis
+        bet = bipartite.betweenness_centrality(G, self.top_nodes)
+        answer = {
+            "E8": 0.24,
+            "E9": 0.23,
+            "E7": 0.13,
+            "Nora Fayette": 0.11,
+            "Evelyn Jefferson": 0.10,
+            "Theresa Anderson": 0.09,
+            "E6": 0.07,
+            "Sylvia Avondale": 0.07,
+            "Laura Mandeville": 0.05,
+            "Brenda Rogers": 0.05,
+            "Katherina Rogers": 0.05,
+            "E5": 0.04,
+            "Helen Lloyd": 0.04,
+            "E3": 0.02,
+            "Ruth DeSand": 0.02,
+            "Verne Sanderson": 0.02,
+            "E12": 0.02,
+            "Myra Liddel": 0.02,
+            "E11": 0.02,
+            "Eleanor Nye": 0.01,
+            "Frances Anderson": 0.01,
+            "Pearl Oglethorpe": 0.01,
+            "E4": 0.01,
+            "Charlotte McDowd": 0.01,
+            "E10": 0.01,
+            "Olivia Carleton": 0.01,
+            "Flora Price": 0.01,
+            "E2": 0.00,
+            "E1": 0.00,
+            "Dorothy Murchison": 0.00,
+            "E13": 0.00,
+            "E14": 0.00,
+        }
+        for node, value in answer.items():
+            assert value == pytest.approx(bet[node], abs=1e-2)
+
+    def test_davis_closeness_centrality(self):
+        G = self.davis
+        clos = bipartite.closeness_centrality(G, self.top_nodes)
+        answer = {
+            "E8": 0.85,
+            "E9": 0.79,
+            "E7": 0.73,
+            "Nora Fayette": 0.80,
+            "Evelyn Jefferson": 0.80,
+            "Theresa Anderson": 0.80,
+            "E6": 0.69,
+            "Sylvia Avondale": 0.77,
+            "Laura Mandeville": 0.73,
+            "Brenda Rogers": 0.73,
+            "Katherina Rogers": 0.73,
+            "E5": 0.59,
+            "Helen Lloyd": 0.73,
+            "E3": 0.56,
+            "Ruth DeSand": 0.71,
+            "Verne Sanderson": 0.71,
+            "E12": 0.56,
+            "Myra Liddel": 0.69,
+            "E11": 0.54,
+            "Eleanor Nye": 0.67,
+            "Frances Anderson": 0.67,
+            "Pearl Oglethorpe": 0.67,
+            "E4": 0.54,
+            "Charlotte McDowd": 0.60,
+            "E10": 0.55,
+            "Olivia Carleton": 0.59,
+            "Flora Price": 0.59,
+            "E2": 0.52,
+            "E1": 0.52,
+            "Dorothy Murchison": 0.65,
+            "E13": 0.52,
+            "E14": 0.52,
+        }
+        for node, value in answer.items():
+            assert value == pytest.approx(clos[node], abs=1e-2)