### view env/lib/python3.9/site-packages/networkx/algorithms/tests/test_cluster.py @ 0:4f3585e2f14bdraftdefaulttip

author shellac Mon, 22 Mar 2021 18:12:50 +0000
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```
import networkx as nx

class TestTriangles:
def test_empty(self):
G = nx.Graph()
assert list(nx.triangles(G).values()) == []

def test_path(self):
G = nx.path_graph(10)
assert list(nx.triangles(G).values()) == [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
assert nx.triangles(G) == {
0: 0,
1: 0,
2: 0,
3: 0,
4: 0,
5: 0,
6: 0,
7: 0,
8: 0,
9: 0,
}

def test_cubical(self):
G = nx.cubical_graph()
assert list(nx.triangles(G).values()) == [0, 0, 0, 0, 0, 0, 0, 0]
assert nx.triangles(G, 1) == 0
assert list(nx.triangles(G, [1, 2]).values()) == [0, 0]
assert nx.triangles(G, 1) == 0
assert nx.triangles(G, [1, 2]) == {1: 0, 2: 0}

def test_k5(self):
G = nx.complete_graph(5)
assert list(nx.triangles(G).values()) == [6, 6, 6, 6, 6]
assert sum(nx.triangles(G).values()) / 3.0 == 10
assert nx.triangles(G, 1) == 6
G.remove_edge(1, 2)
assert list(nx.triangles(G).values()) == [5, 3, 3, 5, 5]
assert nx.triangles(G, 1) == 3

class TestDirectedClustering:
def test_clustering(self):
G = nx.DiGraph()
assert list(nx.clustering(G).values()) == []
assert nx.clustering(G) == {}

def test_path(self):
G = nx.path_graph(10, create_using=nx.DiGraph())
assert list(nx.clustering(G).values()) == [
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
]
assert nx.clustering(G) == {
0: 0.0,
1: 0.0,
2: 0.0,
3: 0.0,
4: 0.0,
5: 0.0,
6: 0.0,
7: 0.0,
8: 0.0,
9: 0.0,
}

def test_k5(self):
G = nx.complete_graph(5, create_using=nx.DiGraph())
assert list(nx.clustering(G).values()) == [1, 1, 1, 1, 1]
assert nx.average_clustering(G) == 1
G.remove_edge(1, 2)
assert list(nx.clustering(G).values()) == [
11.0 / 12.0,
1.0,
1.0,
11.0 / 12.0,
11.0 / 12.0,
]
assert nx.clustering(G, [1, 4]) == {1: 1.0, 4: 11.0 / 12.0}
G.remove_edge(2, 1)
assert list(nx.clustering(G).values()) == [
5.0 / 6.0,
1.0,
1.0,
5.0 / 6.0,
5.0 / 6.0,
]
assert nx.clustering(G, [1, 4]) == {1: 1.0, 4: 0.83333333333333337}

def test_triangle_and_edge(self):
G = nx.cycle_graph(3, create_using=nx.DiGraph())
assert nx.clustering(G)[0] == 1.0 / 6.0

class TestDirectedWeightedClustering:
def test_clustering(self):
G = nx.DiGraph()
assert list(nx.clustering(G, weight="weight").values()) == []
assert nx.clustering(G) == {}

def test_path(self):
G = nx.path_graph(10, create_using=nx.DiGraph())
assert list(nx.clustering(G, weight="weight").values()) == [
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
]
assert nx.clustering(G, weight="weight") == {
0: 0.0,
1: 0.0,
2: 0.0,
3: 0.0,
4: 0.0,
5: 0.0,
6: 0.0,
7: 0.0,
8: 0.0,
9: 0.0,
}

def test_k5(self):
G = nx.complete_graph(5, create_using=nx.DiGraph())
assert list(nx.clustering(G, weight="weight").values()) == [1, 1, 1, 1, 1]
assert nx.average_clustering(G, weight="weight") == 1
G.remove_edge(1, 2)
assert list(nx.clustering(G, weight="weight").values()) == [
11.0 / 12.0,
1.0,
1.0,
11.0 / 12.0,
11.0 / 12.0,
]
assert nx.clustering(G, [1, 4], weight="weight") == {1: 1.0, 4: 11.0 / 12.0}
G.remove_edge(2, 1)
assert list(nx.clustering(G, weight="weight").values()) == [
5.0 / 6.0,
1.0,
1.0,
5.0 / 6.0,
5.0 / 6.0,
]
assert nx.clustering(G, [1, 4], weight="weight") == {
1: 1.0,
4: 0.83333333333333337,
}

def test_triangle_and_edge(self):
G = nx.cycle_graph(3, create_using=nx.DiGraph())
assert nx.clustering(G)[0] == 1.0 / 6.0
assert nx.clustering(G, weight="weight")[0] == 1.0 / 12.0

class TestWeightedClustering:
def test_clustering(self):
G = nx.Graph()
assert list(nx.clustering(G, weight="weight").values()) == []
assert nx.clustering(G) == {}

def test_path(self):
G = nx.path_graph(10)
assert list(nx.clustering(G, weight="weight").values()) == [
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
]
assert nx.clustering(G, weight="weight") == {
0: 0.0,
1: 0.0,
2: 0.0,
3: 0.0,
4: 0.0,
5: 0.0,
6: 0.0,
7: 0.0,
8: 0.0,
9: 0.0,
}

def test_cubical(self):
G = nx.cubical_graph()
assert list(nx.clustering(G, weight="weight").values()) == [
0,
0,
0,
0,
0,
0,
0,
0,
]
assert nx.clustering(G, 1) == 0
assert list(nx.clustering(G, [1, 2], weight="weight").values()) == [0, 0]
assert nx.clustering(G, 1, weight="weight") == 0
assert nx.clustering(G, [1, 2], weight="weight") == {1: 0, 2: 0}

def test_k5(self):
G = nx.complete_graph(5)
assert list(nx.clustering(G, weight="weight").values()) == [1, 1, 1, 1, 1]
assert nx.average_clustering(G, weight="weight") == 1
G.remove_edge(1, 2)
assert list(nx.clustering(G, weight="weight").values()) == [
5.0 / 6.0,
1.0,
1.0,
5.0 / 6.0,
5.0 / 6.0,
]
assert nx.clustering(G, [1, 4], weight="weight") == {
1: 1.0,
4: 0.83333333333333337,
}

def test_triangle_and_edge(self):
G = nx.cycle_graph(3)
assert nx.clustering(G)[0] == 1.0 / 3.0
assert nx.clustering(G, weight="weight")[0] == 1.0 / 6.0

class TestClustering:
def test_clustering(self):
G = nx.Graph()
assert list(nx.clustering(G).values()) == []
assert nx.clustering(G) == {}

def test_path(self):
G = nx.path_graph(10)
assert list(nx.clustering(G).values()) == [
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
]
assert nx.clustering(G) == {
0: 0.0,
1: 0.0,
2: 0.0,
3: 0.0,
4: 0.0,
5: 0.0,
6: 0.0,
7: 0.0,
8: 0.0,
9: 0.0,
}

def test_cubical(self):
G = nx.cubical_graph()
assert list(nx.clustering(G).values()) == [0, 0, 0, 0, 0, 0, 0, 0]
assert nx.clustering(G, 1) == 0
assert list(nx.clustering(G, [1, 2]).values()) == [0, 0]
assert nx.clustering(G, 1) == 0
assert nx.clustering(G, [1, 2]) == {1: 0, 2: 0}

def test_k5(self):
G = nx.complete_graph(5)
assert list(nx.clustering(G).values()) == [1, 1, 1, 1, 1]
assert nx.average_clustering(G) == 1
G.remove_edge(1, 2)
assert list(nx.clustering(G).values()) == [
5.0 / 6.0,
1.0,
1.0,
5.0 / 6.0,
5.0 / 6.0,
]
assert nx.clustering(G, [1, 4]) == {1: 1.0, 4: 0.83333333333333337}

class TestTransitivity:
def test_transitivity(self):
G = nx.Graph()
assert nx.transitivity(G) == 0.0

def test_path(self):
G = nx.path_graph(10)
assert nx.transitivity(G) == 0.0

def test_cubical(self):
G = nx.cubical_graph()
assert nx.transitivity(G) == 0.0

def test_k5(self):
G = nx.complete_graph(5)
assert nx.transitivity(G) == 1.0
G.remove_edge(1, 2)
assert nx.transitivity(G) == 0.875

class TestSquareClustering:
def test_clustering(self):
G = nx.Graph()
assert list(nx.square_clustering(G).values()) == []
assert nx.square_clustering(G) == {}

def test_path(self):
G = nx.path_graph(10)
assert list(nx.square_clustering(G).values()) == [
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
]
assert nx.square_clustering(G) == {
0: 0.0,
1: 0.0,
2: 0.0,
3: 0.0,
4: 0.0,
5: 0.0,
6: 0.0,
7: 0.0,
8: 0.0,
9: 0.0,
}

def test_cubical(self):
G = nx.cubical_graph()
assert list(nx.square_clustering(G).values()) == [
0.5,
0.5,
0.5,
0.5,
0.5,
0.5,
0.5,
0.5,
]
assert list(nx.square_clustering(G, [1, 2]).values()) == [0.5, 0.5]
assert nx.square_clustering(G, [1])[1] == 0.5
assert nx.square_clustering(G, [1, 2]) == {1: 0.5, 2: 0.5}

def test_k5(self):
G = nx.complete_graph(5)
assert list(nx.square_clustering(G).values()) == [1, 1, 1, 1, 1]

def test_bipartite_k5(self):
G = nx.complete_bipartite_graph(5, 5)
assert list(nx.square_clustering(G).values()) == [1, 1, 1, 1, 1, 1, 1, 1, 1, 1]

def test_lind_square_clustering(self):
"""Test C4 for figure 1 Lind et al (2005)"""
G = nx.Graph(
[
(1, 2),
(1, 3),
(1, 6),
(1, 7),
(2, 4),
(2, 5),
(3, 4),
(3, 5),
(6, 7),
(7, 8),
(6, 8),
(7, 9),
(7, 10),
(6, 11),
(6, 12),
(2, 13),
(2, 14),
(3, 15),
(3, 16),
]
)
G1 = G.subgraph([1, 2, 3, 4, 5, 13, 14, 15, 16])
G2 = G.subgraph([1, 6, 7, 8, 9, 10, 11, 12])
assert nx.square_clustering(G, [1])[1] == 3 / 75.0
assert nx.square_clustering(G1, [1])[1] == 2 / 6.0
assert nx.square_clustering(G2, [1])[1] == 1 / 5.0

def test_average_clustering():
G = nx.cycle_graph(3)
assert nx.average_clustering(G) == (1 + 1 + 1 / 3.0) / 4.0
assert nx.average_clustering(G, count_zeros=True) == (1 + 1 + 1 / 3.0) / 4.0
assert nx.average_clustering(G, count_zeros=False) == (1 + 1 + 1 / 3.0) / 3.0

class TestGeneralizedDegree:
def test_generalized_degree(self):
G = nx.Graph()
assert nx.generalized_degree(G) == {}

def test_path(self):
G = nx.path_graph(5)
assert nx.generalized_degree(G, 0) == {0: 1}
assert nx.generalized_degree(G, 1) == {0: 2}

def test_cubical(self):
G = nx.cubical_graph()
assert nx.generalized_degree(G, 0) == {0: 3}

def test_k5(self):
G = nx.complete_graph(5)
assert nx.generalized_degree(G, 0) == {3: 4}
G.remove_edge(0, 1)
assert nx.generalized_degree(G, 0) == {2: 3}
```