# test_cuts.py - unit tests for the cuts module # # Copyright 2015 NetworkX developers. # # This file is part of NetworkX. # # NetworkX is distributed under a BSD license; see LICENSE.txt for more # information. """Unit tests for the :mod:`networkx.algorithms.cuts` module.""" from __future__ import division from nose.tools import assert_equal import networkx as nx class TestCutSize(object): """Unit tests for the :func:`~networkx.cut_size` function.""" def test_symmetric(self): """Tests that the cut size is symmetric.""" G = nx.barbell_graph(3, 0) S = {0, 1, 4} T = {2, 3, 5} assert_equal(nx.cut_size(G, S, T), 4) assert_equal(nx.cut_size(G, T, S), 4) def test_single_edge(self): """Tests for a cut of a single edge.""" G = nx.barbell_graph(3, 0) S = {0, 1, 2} T = {3, 4, 5} assert_equal(nx.cut_size(G, S, T), 1) assert_equal(nx.cut_size(G, T, S), 1) def test_directed(self): """Tests that each directed edge is counted once in the cut.""" G = nx.barbell_graph(3, 0).to_directed() S = {0, 1, 2} T = {3, 4, 5} assert_equal(nx.cut_size(G, S, T), 2) assert_equal(nx.cut_size(G, T, S), 2) def test_directed_symmetric(self): """Tests that a cut in a directed graph is symmetric.""" G = nx.barbell_graph(3, 0).to_directed() S = {0, 1, 4} T = {2, 3, 5} assert_equal(nx.cut_size(G, S, T), 8) assert_equal(nx.cut_size(G, T, S), 8) def test_multigraph(self): """Tests that parallel edges are each counted for a cut.""" G = nx.MultiGraph(['ab', 'ab']) assert_equal(nx.cut_size(G, {'a'}, {'b'}), 2) class TestVolume(object): """Unit tests for the :func:`~networkx.volume` function.""" def test_graph(self): G = nx.cycle_graph(4) assert_equal(nx.volume(G, {0, 1}), 4) def test_digraph(self): G = nx.DiGraph([(0, 1), (1, 2), (2, 3), (3, 0)]) assert_equal(nx.volume(G, {0, 1}), 2) def test_multigraph(self): edges = list(nx.cycle_graph(4).edges()) G = nx.MultiGraph(edges * 2) assert_equal(nx.volume(G, {0, 1}), 8) def test_multidigraph(self): edges = [(0, 1), (1, 2), (2, 3), (3, 0)] G = nx.MultiDiGraph(edges * 2) assert_equal(nx.volume(G, {0, 1}), 4) class TestNormalizedCutSize(object): """Unit tests for the :func:`~networkx.normalized_cut_size` function. """ def test_graph(self): G = nx.path_graph(4) S = {1, 2} T = set(G) - S size = nx.normalized_cut_size(G, S, T) # The cut looks like this: o-{-o--o-}-o expected = 2 * ((1 / 4) + (1 / 2)) assert_equal(expected, size) def test_directed(self): G = nx.DiGraph([(0, 1), (1, 2), (2, 3)]) S = {1, 2} T = set(G) - S size = nx.normalized_cut_size(G, S, T) # The cut looks like this: o-{->o-->o-}->o expected = 2 * ((1 / 2) + (1 / 1)) assert_equal(expected, size) class TestConductance(object): """Unit tests for the :func:`~networkx.conductance` function.""" def test_graph(self): G = nx.barbell_graph(5, 0) # Consider the singleton sets containing the "bridge" nodes. # There is only one cut edge, and each set has volume five. S = {4} T = {5} conductance = nx.conductance(G, S, T) expected = 1 / 5 assert_equal(expected, conductance) class TestEdgeExpansion(object): """Unit tests for the :func:`~networkx.edge_expansion` function.""" def test_graph(self): G = nx.barbell_graph(5, 0) S = set(range(5)) T = set(G) - S expansion = nx.edge_expansion(G, S, T) expected = 1 / 5 assert_equal(expected, expansion) class TestNodeExpansion(object): """Unit tests for the :func:`~networkx.node_expansion` function. """ def test_graph(self): G = nx.path_graph(8) S = {3, 4, 5} expansion = nx.node_expansion(G, S) # The neighborhood of S has cardinality five, and S has # cardinality three. expected = 5 / 3 assert_equal(expected, expansion) class TestBoundaryExpansion(object): """Unit tests for the :func:`~networkx.boundary_expansion` function. """ def test_graph(self): G = nx.complete_graph(10) S = set(range(4)) expansion = nx.boundary_expansion(G, S) # The node boundary of S has cardinality six, and S has # cardinality three. expected = 6 / 4 assert_equal(expected, expansion) class TestMixingExpansion(object): """Unit tests for the :func:`~networkx.mixing_expansion` function. """ def test_graph(self): G = nx.barbell_graph(5, 0) S = set(range(5)) T = set(G) - S expansion = nx.mixing_expansion(G, S, T) # There is one cut edge, and the total number of edges in the # graph is twice the total number of edges in a clique of size # five, plus one more for the bridge. expected = 1 / (2 * (5 * 4 + 1)) assert_equal(expected, expansion)