# -*- encoding: utf-8 -*- # test_random_graphs.py - unit tests for random graph generators # # Copyright 2010-2019 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.generators.random_graphs` module. """ import pytest from networkx.exception import NetworkXError from networkx.generators.random_graphs import barabasi_albert_graph from networkx.generators.random_graphs import dual_barabasi_albert_graph from networkx.generators.random_graphs import extended_barabasi_albert_graph from networkx.generators.random_graphs import binomial_graph from networkx.generators.random_graphs import connected_watts_strogatz_graph from networkx.generators.random_graphs import dense_gnm_random_graph from networkx.generators.random_graphs import erdos_renyi_graph from networkx.generators.random_graphs import fast_gnp_random_graph from networkx.generators.random_graphs import gnm_random_graph from networkx.generators.random_graphs import gnp_random_graph from networkx.generators.random_graphs import newman_watts_strogatz_graph from networkx.generators.random_graphs import powerlaw_cluster_graph from networkx.generators.random_graphs import random_kernel_graph from networkx.generators.random_graphs import random_lobster from networkx.generators.random_graphs import random_powerlaw_tree from networkx.generators.random_graphs import random_powerlaw_tree_sequence from networkx.generators.random_graphs import random_regular_graph from networkx.generators.random_graphs import random_shell_graph from networkx.generators.random_graphs import watts_strogatz_graph class TestGeneratorsRandom(object): def smoke_test_random_graph(self): seed = 42 G = gnp_random_graph(100, 0.25, seed) G = gnp_random_graph(100, 0.25, seed, directed=True) G = binomial_graph(100, 0.25, seed) G = erdos_renyi_graph(100, 0.25, seed) G = fast_gnp_random_graph(100, 0.25, seed) G = fast_gnp_random_graph(100, 0.25, seed, directed=True) G = gnm_random_graph(100, 20, seed) G = gnm_random_graph(100, 20, seed, directed=True) G = dense_gnm_random_graph(100, 20, seed) G = watts_strogatz_graph(10, 2, 0.25, seed) assert len(G) == 10 assert G.number_of_edges() == 10 G = connected_watts_strogatz_graph(10, 2, 0.1, tries=10, seed=seed) assert len(G) == 10 assert G.number_of_edges() == 10 pytest.raises(NetworkXError, connected_watts_strogatz_graph, \ 10, 2, 0.1, tries=0) G = watts_strogatz_graph(10, 4, 0.25, seed) assert len(G) == 10 assert G.number_of_edges() == 20 G = newman_watts_strogatz_graph(10, 2, 0.0, seed) assert len(G) == 10 assert G.number_of_edges() == 10 G = newman_watts_strogatz_graph(10, 4, 0.25, seed) assert len(G) == 10 assert G.number_of_edges() >= 20 G = barabasi_albert_graph(100, 1, seed) G = barabasi_albert_graph(100, 3, seed) assert G.number_of_edges() == (97 * 3) G = extended_barabasi_albert_graph(100, 1, 0, 0, seed) assert G.number_of_edges() == 99 G = extended_barabasi_albert_graph(100, 3, 0, 0, seed) assert G.number_of_edges() == 97 * 3 G = extended_barabasi_albert_graph(100, 1, 0, 0.5, seed) assert G.number_of_edges() == 99 G = extended_barabasi_albert_graph(100, 2, 0.5, 0, seed) assert G.number_of_edges() > 100 * 3 assert G.number_of_edges() < 100 * 4 G = extended_barabasi_albert_graph(100, 2, 0.3, 0.3, seed) assert G.number_of_edges() > 100 * 2 assert G.number_of_edges() < 100 * 4 G = powerlaw_cluster_graph(100, 1, 1.0, seed) G = powerlaw_cluster_graph(100, 3, 0.0, seed) assert G.number_of_edges() == (97 * 3) G = random_regular_graph(10, 20, seed) pytest.raises(NetworkXError, random_regular_graph, 3, 21) pytest.raises(NetworkXError, random_regular_graph, 33, 21) constructor = [(10, 20, 0.8), (20, 40, 0.8)] G = random_shell_graph(constructor, seed) G = random_lobster(10, 0.1, 0.5, seed) # difficult to find seed that requires few tries seq = random_powerlaw_tree_sequence(10, 3, seed=14, tries=1) G = random_powerlaw_tree(10, 3, seed=14, tries=1) def test_dual_barabasi_albert(self, m1=1, m2=4, p=0.5): """ Tests that the dual BA random graph generated behaves consistently. Tests the exceptions are raised as expected. The graphs generation are repeated several times to prevent lucky shots """ seed = 42 repeats = 2 while repeats: repeats -= 1 # This should be BA with m = m1 BA1 = barabasi_albert_graph(100, m1, seed) DBA1 = dual_barabasi_albert_graph(100, m1, m2, 1, seed) assert BA1.size() == DBA1.size() # This should be BA with m = m2 BA2 = barabasi_albert_graph(100, m2, seed) DBA2 = dual_barabasi_albert_graph(100, m1, m2, 0, seed) assert BA2.size() == DBA2.size() # Testing exceptions dbag = dual_barabasi_albert_graph pytest.raises(NetworkXError, dbag, m1, m1, m2, 0) pytest.raises(NetworkXError, dbag, m2, m1, m2, 0) pytest.raises(NetworkXError, dbag, 100, m1, m2, -0.5) pytest.raises(NetworkXError, dbag, 100, m1, m2, 1.5) def test_extended_barabasi_albert(self, m=2): """ Tests that the extended BA random graph generated behaves consistently. Tests the exceptions are raised as expected. The graphs generation are repeated several times to prevent lucky-shots """ seed = 42 repeats = 2 BA_model = barabasi_albert_graph(100, m, seed) BA_model_edges = BA_model.number_of_edges() while repeats: repeats -= 1 # This behaves just like BA, the number of edges must be the same G1 = extended_barabasi_albert_graph(100, m, 0, 0, seed) assert G1.size() == BA_model_edges # More than twice more edges should have been added G1 = extended_barabasi_albert_graph(100, m, 0.8, 0, seed) assert G1.size() > BA_model_edges * 2 # Only edge rewiring, so the number of edges less than original G2 = extended_barabasi_albert_graph(100, m, 0, 0.8, seed) assert G2.size() == BA_model_edges # Mixed scenario: less edges than G1 and more edges than G2 G3 = extended_barabasi_albert_graph(100, m, 0.3, 0.3, seed) assert G3.size() > G2.size() assert G3.size() < G1.size() # Testing exceptions ebag = extended_barabasi_albert_graph pytest.raises(NetworkXError, ebag, m, m, 0, 0) pytest.raises(NetworkXError, ebag, 1, 0.5, 0, 0) pytest.raises(NetworkXError, ebag, 100, 2, 0.5, 0.5) def test_random_zero_regular_graph(self): """Tests that a 0-regular graph has the correct number of nodes and edges. """ seed = 42 G = random_regular_graph(0, 10, seed) assert len(G) == 10 assert sum(1 for _ in G.edges()) == 0 def test_gnp(self): for generator in [gnp_random_graph, binomial_graph, erdos_renyi_graph, fast_gnp_random_graph]: G = generator(10, -1.1) assert len(G) == 10 assert sum(1 for _ in G.edges()) == 0 G = generator(10, 0.1) assert len(G) == 10 G = generator(10, 0.1, seed=42) assert len(G) == 10 G = generator(10, 1.1) assert len(G) == 10 assert sum(1 for _ in G.edges()) == 45 G = generator(10, -1.1, directed=True) assert G.is_directed() assert len(G) == 10 assert sum(1 for _ in G.edges()) == 0 G = generator(10, 0.1, directed=True) assert G.is_directed() assert len(G) == 10 G = generator(10, 1.1, directed=True) assert G.is_directed() assert len(G) == 10 assert sum(1 for _ in G.edges()) == 90 # assert that random graphs generate all edges for p close to 1 edges = 0 runs = 100 for i in range(runs): edges += sum(1 for _ in generator(10, 0.99999, directed=True).edges()) assert abs(edges / float(runs) - 90) <= runs * 2.0 / 100 def test_gnm(self): G = gnm_random_graph(10, 3) assert len(G) == 10 assert sum(1 for _ in G.edges()) == 3 G = gnm_random_graph(10, 3, seed=42) assert len(G) == 10 assert sum(1 for _ in G.edges()) == 3 G = gnm_random_graph(10, 100) assert len(G) == 10 assert sum(1 for _ in G.edges()) == 45 G = gnm_random_graph(10, 100, directed=True) assert len(G) == 10 assert sum(1 for _ in G.edges()) == 90 G = gnm_random_graph(10, -1.1) assert len(G) == 10 assert sum(1 for _ in G.edges()) == 0 def test_watts_strogatz_big_k(self): #Test to make sure than n <= k pytest.raises(NetworkXError, watts_strogatz_graph, 10, 11, 0.25) pytest.raises(NetworkXError, newman_watts_strogatz_graph, 10, 11, 0.25) # could create an infinite loop, now doesn't # infinite loop used to occur when a node has degree n-1 and needs to rewire watts_strogatz_graph(10, 9, 0.25, seed=0) newman_watts_strogatz_graph(10, 9, 0.5, seed=0) #Test k==n scenario watts_strogatz_graph(10, 10, 0.25, seed=0) newman_watts_strogatz_graph(10, 10, 0.25, seed=0) def test_random_kernel_graph(self): def integral(u, w, z): return c * (z - w) def root(u, w, r): return r / c + w c = 1 graph = random_kernel_graph(1000, integral, root) graph = random_kernel_graph(1000, integral, root, seed=42) assert len(graph) == 1000