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authorS. Solomon Darnell2025-03-28 21:52:21 -0500
committerS. Solomon Darnell2025-03-28 21:52:21 -0500
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treeee3dc5af3b6313e921cd920906356f5d4febc4ed /.venv/lib/python3.12/site-packages/networkx/algorithms/approximation/tests/test_steinertree.py
parentcc961e04ba734dd72309fb548a2f97d67d578813 (diff)
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+import pytest
+
+import networkx as nx
+from networkx.algorithms.approximation.steinertree import (
+ _remove_nonterminal_leaves,
+ metric_closure,
+ steiner_tree,
+)
+from networkx.utils import edges_equal
+
+
+class TestSteinerTree:
+ @classmethod
+ def setup_class(cls):
+ G1 = nx.Graph()
+ G1.add_edge(1, 2, weight=10)
+ G1.add_edge(2, 3, weight=10)
+ G1.add_edge(3, 4, weight=10)
+ G1.add_edge(4, 5, weight=10)
+ G1.add_edge(5, 6, weight=10)
+ G1.add_edge(2, 7, weight=1)
+ G1.add_edge(7, 5, weight=1)
+
+ G2 = nx.Graph()
+ G2.add_edge(0, 5, weight=6)
+ G2.add_edge(1, 2, weight=2)
+ G2.add_edge(1, 5, weight=3)
+ G2.add_edge(2, 4, weight=4)
+ G2.add_edge(3, 5, weight=5)
+ G2.add_edge(4, 5, weight=1)
+
+ G3 = nx.Graph()
+ G3.add_edge(1, 2, weight=8)
+ G3.add_edge(1, 9, weight=3)
+ G3.add_edge(1, 8, weight=6)
+ G3.add_edge(1, 10, weight=2)
+ G3.add_edge(1, 14, weight=3)
+ G3.add_edge(2, 3, weight=6)
+ G3.add_edge(3, 4, weight=3)
+ G3.add_edge(3, 10, weight=2)
+ G3.add_edge(3, 11, weight=1)
+ G3.add_edge(4, 5, weight=1)
+ G3.add_edge(4, 11, weight=1)
+ G3.add_edge(5, 6, weight=4)
+ G3.add_edge(5, 11, weight=2)
+ G3.add_edge(5, 12, weight=1)
+ G3.add_edge(5, 13, weight=3)
+ G3.add_edge(6, 7, weight=2)
+ G3.add_edge(6, 12, weight=3)
+ G3.add_edge(6, 13, weight=1)
+ G3.add_edge(7, 8, weight=3)
+ G3.add_edge(7, 9, weight=3)
+ G3.add_edge(7, 11, weight=5)
+ G3.add_edge(7, 13, weight=2)
+ G3.add_edge(7, 14, weight=4)
+ G3.add_edge(8, 9, weight=2)
+ G3.add_edge(9, 14, weight=1)
+ G3.add_edge(10, 11, weight=2)
+ G3.add_edge(10, 14, weight=1)
+ G3.add_edge(11, 12, weight=1)
+ G3.add_edge(11, 14, weight=7)
+ G3.add_edge(12, 14, weight=3)
+ G3.add_edge(12, 15, weight=1)
+ G3.add_edge(13, 14, weight=4)
+ G3.add_edge(13, 15, weight=1)
+ G3.add_edge(14, 15, weight=2)
+
+ cls.G1 = G1
+ cls.G2 = G2
+ cls.G3 = G3
+ cls.G1_term_nodes = [1, 2, 3, 4, 5]
+ cls.G2_term_nodes = [0, 2, 3]
+ cls.G3_term_nodes = [1, 3, 5, 6, 8, 10, 11, 12, 13]
+
+ cls.methods = ["kou", "mehlhorn"]
+
+ def test_connected_metric_closure(self):
+ G = self.G1.copy()
+ G.add_node(100)
+ pytest.raises(nx.NetworkXError, metric_closure, G)
+
+ def test_metric_closure(self):
+ M = metric_closure(self.G1)
+ mc = [
+ (1, 2, {"distance": 10, "path": [1, 2]}),
+ (1, 3, {"distance": 20, "path": [1, 2, 3]}),
+ (1, 4, {"distance": 22, "path": [1, 2, 7, 5, 4]}),
+ (1, 5, {"distance": 12, "path": [1, 2, 7, 5]}),
+ (1, 6, {"distance": 22, "path": [1, 2, 7, 5, 6]}),
+ (1, 7, {"distance": 11, "path": [1, 2, 7]}),
+ (2, 3, {"distance": 10, "path": [2, 3]}),
+ (2, 4, {"distance": 12, "path": [2, 7, 5, 4]}),
+ (2, 5, {"distance": 2, "path": [2, 7, 5]}),
+ (2, 6, {"distance": 12, "path": [2, 7, 5, 6]}),
+ (2, 7, {"distance": 1, "path": [2, 7]}),
+ (3, 4, {"distance": 10, "path": [3, 4]}),
+ (3, 5, {"distance": 12, "path": [3, 2, 7, 5]}),
+ (3, 6, {"distance": 22, "path": [3, 2, 7, 5, 6]}),
+ (3, 7, {"distance": 11, "path": [3, 2, 7]}),
+ (4, 5, {"distance": 10, "path": [4, 5]}),
+ (4, 6, {"distance": 20, "path": [4, 5, 6]}),
+ (4, 7, {"distance": 11, "path": [4, 5, 7]}),
+ (5, 6, {"distance": 10, "path": [5, 6]}),
+ (5, 7, {"distance": 1, "path": [5, 7]}),
+ (6, 7, {"distance": 11, "path": [6, 5, 7]}),
+ ]
+ assert edges_equal(list(M.edges(data=True)), mc)
+
+ def test_steiner_tree(self):
+ valid_steiner_trees = [
+ [
+ [
+ (1, 2, {"weight": 10}),
+ (2, 3, {"weight": 10}),
+ (2, 7, {"weight": 1}),
+ (3, 4, {"weight": 10}),
+ (5, 7, {"weight": 1}),
+ ],
+ [
+ (1, 2, {"weight": 10}),
+ (2, 7, {"weight": 1}),
+ (3, 4, {"weight": 10}),
+ (4, 5, {"weight": 10}),
+ (5, 7, {"weight": 1}),
+ ],
+ [
+ (1, 2, {"weight": 10}),
+ (2, 3, {"weight": 10}),
+ (2, 7, {"weight": 1}),
+ (4, 5, {"weight": 10}),
+ (5, 7, {"weight": 1}),
+ ],
+ ],
+ [
+ [
+ (0, 5, {"weight": 6}),
+ (1, 2, {"weight": 2}),
+ (1, 5, {"weight": 3}),
+ (3, 5, {"weight": 5}),
+ ],
+ [
+ (0, 5, {"weight": 6}),
+ (4, 2, {"weight": 4}),
+ (4, 5, {"weight": 1}),
+ (3, 5, {"weight": 5}),
+ ],
+ ],
+ [
+ [
+ (1, 10, {"weight": 2}),
+ (3, 10, {"weight": 2}),
+ (3, 11, {"weight": 1}),
+ (5, 12, {"weight": 1}),
+ (6, 13, {"weight": 1}),
+ (8, 9, {"weight": 2}),
+ (9, 14, {"weight": 1}),
+ (10, 14, {"weight": 1}),
+ (11, 12, {"weight": 1}),
+ (12, 15, {"weight": 1}),
+ (13, 15, {"weight": 1}),
+ ]
+ ],
+ ]
+ for method in self.methods:
+ for G, term_nodes, valid_trees in zip(
+ [self.G1, self.G2, self.G3],
+ [self.G1_term_nodes, self.G2_term_nodes, self.G3_term_nodes],
+ valid_steiner_trees,
+ ):
+ S = steiner_tree(G, term_nodes, method=method)
+ assert any(
+ edges_equal(list(S.edges(data=True)), valid_tree)
+ for valid_tree in valid_trees
+ )
+
+ def test_multigraph_steiner_tree(self):
+ G = nx.MultiGraph()
+ G.add_edges_from(
+ [
+ (1, 2, 0, {"weight": 1}),
+ (2, 3, 0, {"weight": 999}),
+ (2, 3, 1, {"weight": 1}),
+ (3, 4, 0, {"weight": 1}),
+ (3, 5, 0, {"weight": 1}),
+ ]
+ )
+ terminal_nodes = [2, 4, 5]
+ expected_edges = [
+ (2, 3, 1, {"weight": 1}), # edge with key 1 has lower weight
+ (3, 4, 0, {"weight": 1}),
+ (3, 5, 0, {"weight": 1}),
+ ]
+ for method in self.methods:
+ S = steiner_tree(G, terminal_nodes, method=method)
+ assert edges_equal(S.edges(data=True, keys=True), expected_edges)
+
+ def test_remove_nonterminal_leaves(self):
+ G = nx.path_graph(10)
+ _remove_nonterminal_leaves(G, [4, 5, 6])
+
+ assert list(G) == [4, 5, 6] # only the terminal nodes are left
+
+
+@pytest.mark.parametrize("method", ("kou", "mehlhorn"))
+def test_steiner_tree_weight_attribute(method):
+ G = nx.star_graph(4)
+ # Add an edge attribute that is named something other than "weight"
+ nx.set_edge_attributes(G, {e: 10 for e in G.edges}, name="distance")
+ H = nx.approximation.steiner_tree(G, [1, 3], method=method, weight="distance")
+ assert nx.utils.edges_equal(H.edges, [(0, 1), (0, 3)])
+
+
+@pytest.mark.parametrize("method", ("kou", "mehlhorn"))
+def test_steiner_tree_multigraph_weight_attribute(method):
+ G = nx.cycle_graph(3, create_using=nx.MultiGraph)
+ nx.set_edge_attributes(G, {e: 10 for e in G.edges}, name="distance")
+ G.add_edge(2, 0, distance=5)
+ H = nx.approximation.steiner_tree(G, list(G), method=method, weight="distance")
+ assert len(H.edges) == 2 and H.has_edge(2, 0, key=1)
+ assert sum(dist for *_, dist in H.edges(data="distance")) == 15
+
+
+@pytest.mark.parametrize("method", (None, "mehlhorn", "kou"))
+def test_steiner_tree_methods(method):
+ G = nx.star_graph(4)
+ expected = nx.Graph([(0, 1), (0, 3)])
+ st = nx.approximation.steiner_tree(G, [1, 3], method=method)
+ assert nx.utils.edges_equal(st.edges, expected.edges)
+
+
+def test_steiner_tree_method_invalid():
+ G = nx.star_graph(4)
+ with pytest.raises(
+ ValueError, match="invalid_method is not a valid choice for an algorithm."
+ ):
+ nx.approximation.steiner_tree(G, terminal_nodes=[1, 3], method="invalid_method")
+
+
+def test_steiner_tree_remove_non_terminal_leaves_self_loop_edges():
+ # To verify that the last step of the steiner tree approximation
+ # behaves in the case where a non-terminal leaf has a self loop edge
+ G = nx.path_graph(10)
+
+ # Add self loops to the terminal nodes
+ G.add_edges_from([(2, 2), (3, 3), (4, 4), (7, 7), (8, 8)])
+
+ # Remove non-terminal leaves
+ _remove_nonterminal_leaves(G, [4, 5, 6, 7])
+
+ # The terminal nodes should be left
+ assert list(G) == [4, 5, 6, 7] # only the terminal nodes are left
+
+
+def test_steiner_tree_non_terminal_leaves_multigraph_self_loop_edges():
+ # To verify that the last step of the steiner tree approximation
+ # behaves in the case where a non-terminal leaf has a self loop edge
+ G = nx.MultiGraph()
+ G.add_edges_from([(i, i + 1) for i in range(10)])
+ G.add_edges_from([(2, 2), (3, 3), (4, 4), (4, 4), (7, 7)])
+
+ # Remove non-terminal leaves
+ _remove_nonterminal_leaves(G, [4, 5, 6, 7])
+
+ # Only the terminal nodes should be left
+ assert list(G) == [4, 5, 6, 7]