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"""Unit tests for the :mod:`networkx.generators.random_graphs` module.
"""
import networkx as nx
import pytest
class TestGeneratorsRandom:
def test_random_graph(self):
seed = 42
G = nx.gnp_random_graph(100, 0.25, seed)
G = nx.gnp_random_graph(100, 0.25, seed, directed=True)
G = nx.binomial_graph(100, 0.25, seed)
G = nx.erdos_renyi_graph(100, 0.25, seed)
G = nx.fast_gnp_random_graph(100, 0.25, seed)
G = nx.fast_gnp_random_graph(100, 0.25, seed, directed=True)
G = nx.gnm_random_graph(100, 20, seed)
G = nx.gnm_random_graph(100, 20, seed, directed=True)
G = nx.dense_gnm_random_graph(100, 20, seed)
G = nx.watts_strogatz_graph(10, 2, 0.25, seed)
assert len(G) == 10
assert G.number_of_edges() == 10
G = nx.connected_watts_strogatz_graph(10, 2, 0.1, tries=10, seed=seed)
assert len(G) == 10
assert G.number_of_edges() == 10
pytest.raises(
nx.NetworkXError, nx.connected_watts_strogatz_graph, 10, 2, 0.1, tries=0
)
G = nx.watts_strogatz_graph(10, 4, 0.25, seed)
assert len(G) == 10
assert G.number_of_edges() == 20
G = nx.newman_watts_strogatz_graph(10, 2, 0.0, seed)
assert len(G) == 10
assert G.number_of_edges() == 10
G = nx.newman_watts_strogatz_graph(10, 4, 0.25, seed)
assert len(G) == 10
assert G.number_of_edges() >= 20
G = nx.barabasi_albert_graph(100, 1, seed)
G = nx.barabasi_albert_graph(100, 3, seed)
assert G.number_of_edges() == (97 * 3)
G = nx.barabasi_albert_graph(100, 3, seed, nx.complete_graph(5))
assert G.number_of_edges() == (10 + 95 * 3)
G = nx.extended_barabasi_albert_graph(100, 1, 0, 0, seed)
assert G.number_of_edges() == 99
G = nx.extended_barabasi_albert_graph(100, 3, 0, 0, seed)
assert G.number_of_edges() == 97 * 3
G = nx.extended_barabasi_albert_graph(100, 1, 0, 0.5, seed)
assert G.number_of_edges() == 99
G = nx.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 = nx.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 = nx.powerlaw_cluster_graph(100, 1, 1.0, seed)
G = nx.powerlaw_cluster_graph(100, 3, 0.0, seed)
assert G.number_of_edges() == (97 * 3)
G = nx.random_regular_graph(10, 20, seed)
pytest.raises(nx.NetworkXError, nx.random_regular_graph, 3, 21)
pytest.raises(nx.NetworkXError, nx.random_regular_graph, 33, 21)
constructor = [(10, 20, 0.8), (20, 40, 0.8)]
G = nx.random_shell_graph(constructor, seed)
def is_caterpillar(g):
"""
A tree is a caterpillar iff all nodes of degree >=3 are surrounded
by at most two nodes of degree two or greater.
ref: http://mathworld.wolfram.com/CaterpillarGraph.html
"""
deg_over_3 = [n for n in g if g.degree(n) >= 3]
for n in deg_over_3:
nbh_deg_over_2 = [nbh for nbh in g.neighbors(n) if g.degree(nbh) >= 2]
if not len(nbh_deg_over_2) <= 2:
return False
return True
def is_lobster(g):
"""
A tree is a lobster if it has the property that the removal of leaf
nodes leaves a caterpillar graph (Gallian 2007)
ref: http://mathworld.wolfram.com/LobsterGraph.html
"""
non_leafs = [n for n in g if g.degree(n) > 1]
return is_caterpillar(g.subgraph(non_leafs))
G = nx.random_lobster(10, 0.1, 0.5, seed)
assert max([G.degree(n) for n in G.nodes()]) > 3
assert is_lobster(G)
pytest.raises(nx.NetworkXError, nx.random_lobster, 10, 0.1, 1, seed)
pytest.raises(nx.NetworkXError, nx.random_lobster, 10, 1, 1, seed)
pytest.raises(nx.NetworkXError, nx.random_lobster, 10, 1, 0.5, seed)
# docstring says this should be a caterpillar
G = nx.random_lobster(10, 0.1, 0.0, seed)
assert is_caterpillar(G)
# difficult to find seed that requires few tries
seq = nx.random_powerlaw_tree_sequence(10, 3, seed=14, tries=1)
G = nx.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
"""
seeds = [42, 314, 2718]
initial_graph = nx.complete_graph(10)
for seed in seeds:
# This should be BA with m = m1
BA1 = nx.barabasi_albert_graph(100, m1, seed)
DBA1 = nx.dual_barabasi_albert_graph(100, m1, m2, 1, seed)
assert BA1.edges() == DBA1.edges()
# This should be BA with m = m2
BA2 = nx.barabasi_albert_graph(100, m2, seed)
DBA2 = nx.dual_barabasi_albert_graph(100, m1, m2, 0, seed)
assert BA2.edges() == DBA2.edges()
BA3 = nx.barabasi_albert_graph(100, m1, seed)
DBA3 = nx.dual_barabasi_albert_graph(100, m1, m1, p, seed)
# We can't compare edges here since randomness is "consumed" when drawing
# between m1 and m2
assert BA3.size() == DBA3.size()
DBA = nx.dual_barabasi_albert_graph(100, m1, m2, p, seed, initial_graph)
BA1 = nx.barabasi_albert_graph(100, m1, seed, initial_graph)
BA2 = nx.barabasi_albert_graph(100, m2, seed, initial_graph)
assert (
min(BA1.size(), BA2.size()) <= DBA.size() <= max(BA1.size(), BA2.size())
)
# Testing exceptions
dbag = nx.dual_barabasi_albert_graph
pytest.raises(nx.NetworkXError, dbag, m1, m1, m2, 0)
pytest.raises(nx.NetworkXError, dbag, m2, m1, m2, 0)
pytest.raises(nx.NetworkXError, dbag, 100, m1, m2, -0.5)
pytest.raises(nx.NetworkXError, dbag, 100, m1, m2, 1.5)
initial = nx.complete_graph(max(m1, m2) - 1)
pytest.raises(nx.NetworkXError, dbag, 100, m1, m2, p, initial_graph=initial)
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
"""
seeds = [42, 314, 2718]
for seed in seeds:
BA_model = nx.barabasi_albert_graph(100, m, seed)
BA_model_edges = BA_model.number_of_edges()
# This behaves just like BA, the number of edges must be the same
G1 = nx.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 = nx.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 = nx.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 = nx.extended_barabasi_albert_graph(100, m, 0.3, 0.3, seed)
assert G3.size() > G2.size()
assert G3.size() < G1.size()
# Testing exceptions
ebag = nx.extended_barabasi_albert_graph
pytest.raises(nx.NetworkXError, ebag, m, m, 0, 0)
pytest.raises(nx.NetworkXError, ebag, 1, 0.5, 0, 0)
pytest.raises(nx.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 = nx.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 [
nx.gnp_random_graph,
nx.binomial_graph,
nx.erdos_renyi_graph,
nx.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 = nx.gnm_random_graph(10, 3)
assert len(G) == 10
assert sum(1 for _ in G.edges()) == 3
G = nx.gnm_random_graph(10, 3, seed=42)
assert len(G) == 10
assert sum(1 for _ in G.edges()) == 3
G = nx.gnm_random_graph(10, 100)
assert len(G) == 10
assert sum(1 for _ in G.edges()) == 45
G = nx.gnm_random_graph(10, 100, directed=True)
assert len(G) == 10
assert sum(1 for _ in G.edges()) == 90
G = nx.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(nx.NetworkXError, nx.watts_strogatz_graph, 10, 11, 0.25)
pytest.raises(nx.NetworkXError, nx.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
nx.watts_strogatz_graph(10, 9, 0.25, seed=0)
nx.newman_watts_strogatz_graph(10, 9, 0.5, seed=0)
# Test k==n scenario
nx.watts_strogatz_graph(10, 10, 0.25, seed=0)
nx.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 = nx.random_kernel_graph(1000, integral, root)
graph = nx.random_kernel_graph(1000, integral, root, seed=42)
assert len(graph) == 1000
|
