chore: import upstream snapshot with attribution
This commit is contained in:
wehub-resource-sync
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import math
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import unittest
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from itertools import combinations
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import easygraph as eg
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import numpy as np
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from easygraph.utils.exception import EasyGraphError
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class test_assortativity(unittest.TestCase):
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def setUp(self):
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self.g = eg.get_graph_karateclub()
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self.edges = [(8, 9), (1, 2), (8, 4), (3, 6), (1, 3), (6, 4)]
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self.hg = [
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eg.Hypergraph(num_v=10, e_list=self.edges, e_property=None),
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eg.Hypergraph(num_v=2, e_list=[(0, 1)]),
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]
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# Valid uniform hypergraph
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self.hg_uniform = eg.Hypergraph(
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num_v=5,
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e_list=[
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(0, 1, 2),
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(1, 2, 3),
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(2, 3, 4),
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],
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)
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# Non-uniform hypergraph
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self.hg_non_uniform = eg.Hypergraph(
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num_v=4,
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e_list=[
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(0, 1),
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(2, 3, 0),
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],
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)
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# Singleton edge hypergraph (still needs num_v > 0)
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self.hg_singleton = eg.Hypergraph(
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num_v=3,
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e_list=[
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(0,),
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(1, 2),
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],
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)
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# "Empty" hypergraph (has 1 node but no edges)
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self.hg_empty = eg.Hypergraph(
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num_v=1,
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e_list=[],
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)
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def test_dynamical_assortativity(self):
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for i in self.hg:
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degs = i.deg_v
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print(degs)
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k1 = sum(degs) / len(degs)
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print(k1)
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k2 = np.mean(np.array(degs) ** 2)
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print(k2)
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kk1 = np.mean(
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[degs[n1] * degs[n2] for e in i.e[0] for n1, n2 in combinations(e, 2)]
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)
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print(kk1)
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print(eg.dynamical_assortativity(i))
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print()
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def test_degree_assortativity(self):
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for i in self.hg:
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print(eg.degree_assortativity(i))
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def test_dynamical_assortativity_valid(self):
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result = eg.dynamical_assortativity(self.hg_uniform)
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self.assertIsInstance(result, float)
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def test_dynamical_assortativity_raises_on_empty(self):
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with self.assertRaises(EasyGraphError):
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eg.dynamical_assortativity(self.hg_empty)
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def test_dynamical_assortativity_raises_on_singleton(self):
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with self.assertRaises(EasyGraphError):
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eg.dynamical_assortativity(self.hg_singleton)
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def test_dynamical_assortativity_raises_on_nonuniform(self):
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with self.assertRaises(EasyGraphError):
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eg.dynamical_assortativity(self.hg_non_uniform)
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def test_degree_assortativity_raises_on_invalid_kind(self):
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with self.assertRaises(EasyGraphError):
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eg.degree_assortativity(self.hg_uniform, kind="invalid")
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def test_degree_assortativity_raises_on_singleton(self):
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with self.assertRaises(EasyGraphError):
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eg.degree_assortativity(self.hg_singleton)
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def test_degree_assortativity_raises_on_empty(self):
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with self.assertRaises(EasyGraphError):
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eg.degree_assortativity(self.hg_empty)
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if __name__ == "__main__":
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unittest.main()
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@@ -0,0 +1,24 @@
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import easygraph as eg
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import pytest
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@pytest.fixture()
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def g1():
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e_list = [(0, 1, 2, 5), (0, 1), (2, 3, 4), (1, 2, 4)]
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g = eg.Hypergraph(6, e_list=e_list)
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return g
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@pytest.fixture()
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def g2():
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e_list = [(1, 2, 3), (0, 1, 3), (0, 1), (2, 4, 3), (2, 3)]
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e_weight = [0.5, 1, 0.5, 1, 0.5]
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g = eg.Hypergraph(5, e_list=e_list, e_weight=e_weight)
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return g
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def test_degree_centrality(g1, g2):
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print(eg.hyepergraph_degree_centrality(g1))
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print(eg.hyepergraph_degree_centrality(g2))
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assert eg.hyepergraph_degree_centrality(g1) == {0: 2, 1: 3, 2: 3, 3: 1, 4: 2, 5: 1}
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assert eg.hyepergraph_degree_centrality(g2) == {0: 2, 1: 3, 2: 3, 3: 4, 4: 1}
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import unittest
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import easygraph as eg
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from easygraph.utils.exception import EasyGraphError
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class test_hypergraph_operation(unittest.TestCase):
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def setUp(self):
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self.g = eg.get_graph_karateclub()
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self.edges = [(1, 2), (8, 4)]
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self.hg = [
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eg.Hypergraph(num_v=10, e_list=self.edges, e_property=None),
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eg.Hypergraph(num_v=2, e_list=[(0, 1)]),
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]
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def test_hypergraph_clustering_coefficient(self):
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for i in self.hg:
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print(eg.hypergraph_clustering_coefficient(i))
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def test_hypergraph_local_clustering_coefficient(self):
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for i in self.hg:
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print(eg.hypergraph_local_clustering_coefficient(i))
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def test_hypergraph_two_node_clustering_coefficient(self):
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for i in self.hg:
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print(eg.hypergraph_two_node_clustering_coefficient(i))
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class TestHypergraphClustering(unittest.TestCase):
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def setUp(self):
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self.edges = [(0, 1), (1, 2), (2, 3), (3, 0)]
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self.hg = eg.Hypergraph(num_v=4, e_list=self.edges)
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def test_hypergraph_clustering_coefficient_basic(self):
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cc = eg.hypergraph_clustering_coefficient(self.hg)
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self.assertIsInstance(cc, dict)
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for k, v in cc.items():
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self.assertIn(k, self.hg.v)
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self.assertGreaterEqual(v, 0)
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def test_hypergraph_local_clustering_coefficient_basic(self):
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cc = eg.hypergraph_local_clustering_coefficient(self.hg)
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self.assertIsInstance(cc, dict)
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for k, v in cc.items():
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self.assertIn(k, self.hg.v)
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self.assertGreaterEqual(v, 0)
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def test_hypergraph_two_node_clustering_union(self):
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cc = eg.hypergraph_two_node_clustering_coefficient(self.hg, kind="union")
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self.assertIsInstance(cc, dict)
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def test_hypergraph_two_node_clustering_min(self):
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cc = eg.hypergraph_two_node_clustering_coefficient(self.hg, kind="min")
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self.assertIsInstance(cc, dict)
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def test_hypergraph_two_node_clustering_max(self):
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cc = eg.hypergraph_two_node_clustering_coefficient(self.hg, kind="max")
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self.assertIsInstance(cc, dict)
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def test_hypergraph_two_node_clustering_invalid_kind(self):
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with self.assertRaises(EasyGraphError):
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eg.hypergraph_two_node_clustering_coefficient(self.hg, kind="invalid")
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def test_single_edge(self):
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hg = eg.Hypergraph(num_v=2, e_list=[(0, 1)])
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cc = eg.hypergraph_clustering_coefficient(hg)
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self.assertTrue(all(k in cc for k in hg.v))
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def test_disconnected_nodes(self):
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hg = eg.Hypergraph(num_v=4, e_list=[(0, 1)])
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cc = eg.hypergraph_clustering_coefficient(hg)
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for v in [2, 3]:
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self.assertEqual(cc[v], 0)
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def test_fully_connected_hyperedge(self):
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hg = eg.Hypergraph(num_v=3, e_list=[(0, 1, 2)])
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cc = eg.hypergraph_clustering_coefficient(hg)
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for v in cc.values():
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self.assertEqual(v, 1.0)
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def test_nan_safety_in_two_node_coefficient(self):
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hg = eg.Hypergraph(num_v=1, e_list=[(0,)])
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result = eg.hypergraph_two_node_clustering_coefficient(hg)
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self.assertEqual(result[0], 0.0)
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if __name__ == "__main__":
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unittest.main()
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@@ -0,0 +1,72 @@
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import math
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import unittest
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import easygraph as eg
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from easygraph.utils.exception import EasyGraphError
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class test_hypergraph_operation(unittest.TestCase):
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def setUp(self):
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self.g = eg.get_graph_karateclub()
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self.edges = [(1, 2), (8, 4)]
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self.hg = [
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eg.Hypergraph(num_v=10, e_list=self.edges, e_property=None),
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eg.Hypergraph(num_v=2, e_list=[(0, 1)]),
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]
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# checked -- num_v cannot be set to negative number
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def test_hypergraph_operation(self):
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for i in self.hg:
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print(eg.hypergraph_density(i))
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i.draw(v_color="#e6928f", e_color="#4e9595")
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def test_basic_density(self):
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hg = eg.Hypergraph(num_v=3, e_list=[(0, 1), (1, 2)])
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expected = 2 / (2**3 - 1)
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self.assertAlmostEqual(eg.hypergraph_density(hg), expected)
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def test_density_ignore_singletons(self):
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hg = eg.Hypergraph(num_v=3, e_list=[(0,), (1, 2)])
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expected = 2 / ((2**3 - 1) - 3)
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self.assertAlmostEqual(
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eg.hypergraph_density(hg, ignore_singletons=True), expected
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)
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def test_density_all_singletons(self):
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hg = eg.Hypergraph(num_v=3, e_list=[(0,), (1,), (2,)])
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expected = 3 / (2**3 - 1)
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self.assertAlmostEqual(eg.hypergraph_density(hg), expected)
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expected_ignoring = 3 / ((2**3 - 1) - 3)
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self.assertAlmostEqual(
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eg.hypergraph_density(hg, ignore_singletons=True), expected_ignoring
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)
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def test_no_edges_returns_zero(self):
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hg = eg.Hypergraph(num_v=5, e_list=[])
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self.assertEqual(eg.hypergraph_density(hg), 0.0)
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def test_single_node_single_edge(self):
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hg = eg.Hypergraph(num_v=1, e_list=[(0,)])
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self.assertEqual(eg.hypergraph_density(hg), 1.0)
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def test_density_max_possible_edges(self):
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n = 4
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from itertools import chain
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from itertools import combinations
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powerset = list(
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chain.from_iterable(combinations(range(n), r) for r in range(1, n + 1))
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)
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hg = eg.Hypergraph(num_v=n, e_list=powerset)
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self.assertAlmostEqual(eg.hypergraph_density(hg), 1.0)
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def test_density_zero_division_guard(self):
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# Singleton ignored in n=1 graph should not divide by zero
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hg = eg.Hypergraph(num_v=1, e_list=[(0,)])
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result = eg.hypergraph_density(hg, ignore_singletons=True)
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self.assertEqual(result, 0.0)
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if __name__ == "__main__":
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unittest.main()
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