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import pytest
import networkx as nx
def test_random_partition_graph():
G = nx.random_partition_graph([3, 3, 3], 1, 0, seed=42)
C = G.graph["partition"]
assert C == [{0, 1, 2}, {3, 4, 5}, {6, 7, 8}]
assert len(G) == 9
assert len(list(G.edges())) == 9
G = nx.random_partition_graph([3, 3, 3], 0, 1)
C = G.graph["partition"]
assert C == [{0, 1, 2}, {3, 4, 5}, {6, 7, 8}]
assert len(G) == 9
assert len(list(G.edges())) == 27
G = nx.random_partition_graph([3, 3, 3], 1, 0, directed=True)
C = G.graph["partition"]
assert C == [{0, 1, 2}, {3, 4, 5}, {6, 7, 8}]
assert len(G) == 9
assert len(list(G.edges())) == 18
G = nx.random_partition_graph([3, 3, 3], 0, 1, directed=True)
C = G.graph["partition"]
assert C == [{0, 1, 2}, {3, 4, 5}, {6, 7, 8}]
assert len(G) == 9
assert len(list(G.edges())) == 54
G = nx.random_partition_graph([1, 2, 3, 4, 5], 0.5, 0.1)
C = G.graph["partition"]
assert C == [{0}, {1, 2}, {3, 4, 5}, {6, 7, 8, 9}, {10, 11, 12, 13, 14}]
assert len(G) == 15
rpg = nx.random_partition_graph
pytest.raises(nx.NetworkXError, rpg, [1, 2, 3], 1.1, 0.1)
pytest.raises(nx.NetworkXError, rpg, [1, 2, 3], -0.1, 0.1)
pytest.raises(nx.NetworkXError, rpg, [1, 2, 3], 0.1, 1.1)
pytest.raises(nx.NetworkXError, rpg, [1, 2, 3], 0.1, -0.1)
def test_planted_partition_graph():
G = nx.planted_partition_graph(4, 3, 1, 0, seed=42)
C = G.graph["partition"]
assert len(C) == 4
assert len(G) == 12
assert len(list(G.edges())) == 12
G = nx.planted_partition_graph(4, 3, 0, 1)
C = G.graph["partition"]
assert len(C) == 4
assert len(G) == 12
assert len(list(G.edges())) == 54
G = nx.planted_partition_graph(10, 4, 0.5, 0.1, seed=42)
C = G.graph["partition"]
assert len(C) == 10
assert len(G) == 40
G = nx.planted_partition_graph(4, 3, 1, 0, directed=True)
C = G.graph["partition"]
assert len(C) == 4
assert len(G) == 12
assert len(list(G.edges())) == 24
G = nx.planted_partition_graph(4, 3, 0, 1, directed=True)
C = G.graph["partition"]
assert len(C) == 4
assert len(G) == 12
assert len(list(G.edges())) == 108
G = nx.planted_partition_graph(10, 4, 0.5, 0.1, seed=42, directed=True)
C = G.graph["partition"]
assert len(C) == 10
assert len(G) == 40
ppg = nx.planted_partition_graph
pytest.raises(nx.NetworkXError, ppg, 3, 3, 1.1, 0.1)
pytest.raises(nx.NetworkXError, ppg, 3, 3, -0.1, 0.1)
pytest.raises(nx.NetworkXError, ppg, 3, 3, 0.1, 1.1)
pytest.raises(nx.NetworkXError, ppg, 3, 3, 0.1, -0.1)
def test_relaxed_caveman_graph():
G = nx.relaxed_caveman_graph(4, 3, 0)
assert len(G) == 12
G = nx.relaxed_caveman_graph(4, 3, 1)
assert len(G) == 12
G = nx.relaxed_caveman_graph(4, 3, 0.5)
assert len(G) == 12
G = nx.relaxed_caveman_graph(4, 3, 0.5, seed=42)
assert len(G) == 12
def test_connected_caveman_graph():
G = nx.connected_caveman_graph(4, 3)
assert len(G) == 12
G = nx.connected_caveman_graph(1, 5)
K5 = nx.complete_graph(5)
K5.remove_edge(3, 4)
assert nx.is_isomorphic(G, K5)
# need at least 2 nodes in each clique
pytest.raises(nx.NetworkXError, nx.connected_caveman_graph, 4, 1)
def test_caveman_graph():
G = nx.caveman_graph(4, 3)
assert len(G) == 12
G = nx.caveman_graph(5, 1)
E5 = nx.empty_graph(5)
assert nx.is_isomorphic(G, E5)
G = nx.caveman_graph(1, 5)
K5 = nx.complete_graph(5)
assert nx.is_isomorphic(G, K5)
def test_gaussian_random_partition_graph():
G = nx.gaussian_random_partition_graph(100, 10, 10, 0.3, 0.01)
assert len(G) == 100
G = nx.gaussian_random_partition_graph(100, 10, 10, 0.3, 0.01, directed=True)
assert len(G) == 100
G = nx.gaussian_random_partition_graph(
100, 10, 10, 0.3, 0.01, directed=False, seed=42
)
assert len(G) == 100
assert not isinstance(G, nx.DiGraph)
G = nx.gaussian_random_partition_graph(
100, 10, 10, 0.3, 0.01, directed=True, seed=42
)
assert len(G) == 100
assert isinstance(G, nx.DiGraph)
pytest.raises(
nx.NetworkXError, nx.gaussian_random_partition_graph, 100, 101, 10, 1, 0
)
# Test when clusters are likely less than 1
G = nx.gaussian_random_partition_graph(10, 0.5, 0.5, 0.5, 0.5, seed=1)
assert len(G) == 10
def test_ring_of_cliques():
for i in range(2, 20, 3):
for j in range(2, 20, 3):
G = nx.ring_of_cliques(i, j)
assert G.number_of_nodes() == i * j
if i != 2 or j != 1:
expected_num_edges = i * (((j * (j - 1)) // 2) + 1)
else:
# the edge that already exists cannot be duplicated
expected_num_edges = i * (((j * (j - 1)) // 2) + 1) - 1
assert G.number_of_edges() == expected_num_edges
with pytest.raises(
nx.NetworkXError, match="A ring of cliques must have at least two cliques"
):
nx.ring_of_cliques(1, 5)
with pytest.raises(
nx.NetworkXError, match="The cliques must have at least two nodes"
):
nx.ring_of_cliques(3, 0)
def test_windmill_graph():
for n in range(2, 20, 3):
for k in range(2, 20, 3):
G = nx.windmill_graph(n, k)
assert G.number_of_nodes() == (k - 1) * n + 1
assert G.number_of_edges() == n * k * (k - 1) / 2
assert G.degree(0) == G.number_of_nodes() - 1
for i in range(1, G.number_of_nodes()):
assert G.degree(i) == k - 1
with pytest.raises(
nx.NetworkXError, match="A windmill graph must have at least two cliques"
):
nx.windmill_graph(1, 3)
with pytest.raises(
nx.NetworkXError, match="The cliques must have at least two nodes"
):
nx.windmill_graph(3, 0)
def test_stochastic_block_model():
sizes = [75, 75, 300]
probs = [[0.25, 0.05, 0.02], [0.05, 0.35, 0.07], [0.02, 0.07, 0.40]]
G = nx.stochastic_block_model(sizes, probs, seed=0)
C = G.graph["partition"]
assert len(C) == 3
assert len(G) == 450
assert G.size() == 22160
GG = nx.stochastic_block_model(sizes, probs, range(450), seed=0)
assert G.nodes == GG.nodes
# Test Exceptions
sbm = nx.stochastic_block_model
badnodelist = list(range(400)) # not enough nodes to match sizes
badprobs1 = [[0.25, 0.05, 1.02], [0.05, 0.35, 0.07], [0.02, 0.07, 0.40]]
badprobs2 = [[0.25, 0.05, 0.02], [0.05, -0.35, 0.07], [0.02, 0.07, 0.40]]
probs_rect1 = [[0.25, 0.05, 0.02], [0.05, -0.35, 0.07]]
probs_rect2 = [[0.25, 0.05], [0.05, -0.35], [0.02, 0.07]]
asymprobs = [[0.25, 0.05, 0.01], [0.05, -0.35, 0.07], [0.02, 0.07, 0.40]]
pytest.raises(nx.NetworkXException, sbm, sizes, badprobs1)
pytest.raises(nx.NetworkXException, sbm, sizes, badprobs2)
pytest.raises(nx.NetworkXException, sbm, sizes, probs_rect1, directed=True)
pytest.raises(nx.NetworkXException, sbm, sizes, probs_rect2, directed=True)
pytest.raises(nx.NetworkXException, sbm, sizes, asymprobs, directed=False)
pytest.raises(nx.NetworkXException, sbm, sizes, probs, badnodelist)
nodelist = [0] + list(range(449)) # repeated node name in nodelist
pytest.raises(nx.NetworkXException, sbm, sizes, probs, nodelist)
# Extra keyword arguments test
GG = nx.stochastic_block_model(sizes, probs, seed=0, selfloops=True)
assert G.nodes == GG.nodes
GG = nx.stochastic_block_model(sizes, probs, selfloops=True, directed=True)
assert G.nodes == GG.nodes
GG = nx.stochastic_block_model(sizes, probs, seed=0, sparse=False)
assert G.nodes == GG.nodes
def test_generator():
n = 250
tau1 = 3
tau2 = 1.5
mu = 0.1
G = nx.LFR_benchmark_graph(
n, tau1, tau2, mu, average_degree=5, min_community=20, seed=10
)
assert len(G) == 250
C = {frozenset(G.nodes[v]["community"]) for v in G}
assert nx.community.is_partition(G.nodes(), C)
def test_invalid_tau1():
with pytest.raises(nx.NetworkXError, match="tau2 must be greater than one"):
n = 100
tau1 = 2
tau2 = 1
mu = 0.1
nx.LFR_benchmark_graph(n, tau1, tau2, mu, min_degree=2)
def test_invalid_tau2():
with pytest.raises(nx.NetworkXError, match="tau1 must be greater than one"):
n = 100
tau1 = 1
tau2 = 2
mu = 0.1
nx.LFR_benchmark_graph(n, tau1, tau2, mu, min_degree=2)
def test_mu_too_large():
with pytest.raises(nx.NetworkXError, match="mu must be in the interval \\[0, 1\\]"):
n = 100
tau1 = 2
tau2 = 2
mu = 1.1
nx.LFR_benchmark_graph(n, tau1, tau2, mu, min_degree=2)
def test_mu_too_small():
with pytest.raises(nx.NetworkXError, match="mu must be in the interval \\[0, 1\\]"):
n = 100
tau1 = 2
tau2 = 2
mu = -1
nx.LFR_benchmark_graph(n, tau1, tau2, mu, min_degree=2)
def test_both_degrees_none():
with pytest.raises(
nx.NetworkXError,
match="Must assign exactly one of min_degree and average_degree",
):
n = 100
tau1 = 2
tau2 = 2
mu = 1
nx.LFR_benchmark_graph(n, tau1, tau2, mu)
def test_neither_degrees_none():
with pytest.raises(
nx.NetworkXError,
match="Must assign exactly one of min_degree and average_degree",
):
n = 100
tau1 = 2
tau2 = 2
mu = 1
nx.LFR_benchmark_graph(n, tau1, tau2, mu, min_degree=2, average_degree=5)
def test_max_iters_exceeded():
with pytest.raises(
nx.ExceededMaxIterations,
match="Could not assign communities; try increasing min_community",
):
n = 10
tau1 = 2
tau2 = 2
mu = 0.1
nx.LFR_benchmark_graph(n, tau1, tau2, mu, min_degree=2, max_iters=10, seed=1)
def test_max_deg_out_of_range():
with pytest.raises(
nx.NetworkXError, match="max_degree must be in the interval \\(0, n\\]"
):
n = 10
tau1 = 2
tau2 = 2
mu = 0.1
nx.LFR_benchmark_graph(
n, tau1, tau2, mu, max_degree=n + 1, max_iters=10, seed=1
)
def test_max_community():
n = 250
tau1 = 3
tau2 = 1.5
mu = 0.1
G = nx.LFR_benchmark_graph(
n,
tau1,
tau2,
mu,
average_degree=5,
max_degree=100,
min_community=50,
max_community=200,
seed=10,
)
assert len(G) == 250
C = {frozenset(G.nodes[v]["community"]) for v in G}
assert nx.community.is_partition(G.nodes(), C)
def test_powerlaw_iterations_exceeded():
with pytest.raises(
nx.ExceededMaxIterations, match="Could not create power law sequence"
):
n = 100
tau1 = 2
tau2 = 2
mu = 1
nx.LFR_benchmark_graph(n, tau1, tau2, mu, min_degree=2, max_iters=0)
def test_no_scipy_zeta():
zeta2 = 1.6449340668482264
assert abs(zeta2 - nx.generators.community._hurwitz_zeta(2, 1, 0.0001)) < 0.01
def test_generate_min_degree_itr():
with pytest.raises(
nx.ExceededMaxIterations, match="Could not match average_degree"
):
nx.generators.community._generate_min_degree(2, 2, 1, 0.01, 0)
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