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import pytest
np = pytest.importorskip("numpy")
pytest.importorskip("scipy")
import networkx as nx
from .base_test import (
BaseTestAttributeMixing,
BaseTestDegreeMixing,
BaseTestNumericMixing,
)
from networkx.algorithms.assortativity.correlation import attribute_ac
class TestDegreeMixingCorrelation(BaseTestDegreeMixing):
def test_degree_assortativity_undirected(self):
r = nx.degree_assortativity_coefficient(self.P4)
np.testing.assert_almost_equal(r, -1.0 / 2, decimal=4)
def test_degree_assortativity_directed(self):
r = nx.degree_assortativity_coefficient(self.D)
np.testing.assert_almost_equal(r, -0.57735, decimal=4)
def test_degree_assortativity_multigraph(self):
r = nx.degree_assortativity_coefficient(self.M)
np.testing.assert_almost_equal(r, -1.0 / 7.0, decimal=4)
def test_degree_pearson_assortativity_undirected(self):
r = nx.degree_pearson_correlation_coefficient(self.P4)
np.testing.assert_almost_equal(r, -1.0 / 2, decimal=4)
def test_degree_pearson_assortativity_directed(self):
r = nx.degree_pearson_correlation_coefficient(self.D)
np.testing.assert_almost_equal(r, -0.57735, decimal=4)
def test_degree_pearson_assortativity_multigraph(self):
r = nx.degree_pearson_correlation_coefficient(self.M)
np.testing.assert_almost_equal(r, -1.0 / 7.0, decimal=4)
def test_degree_assortativity_weighted(self):
r = nx.degree_assortativity_coefficient(self.W, weight="weight")
np.testing.assert_almost_equal(r, -0.1429, decimal=4)
class TestAttributeMixingCorrelation(BaseTestAttributeMixing):
def test_attribute_assortativity_undirected(self):
r = nx.attribute_assortativity_coefficient(self.G, "fish")
assert r == 6.0 / 22.0
def test_attribute_assortativity_directed(self):
r = nx.attribute_assortativity_coefficient(self.D, "fish")
assert r == 1.0 / 3.0
def test_attribute_assortativity_multigraph(self):
r = nx.attribute_assortativity_coefficient(self.M, "fish")
assert r == 1.0
def test_attribute_assortativity_coefficient(self):
# from "Mixing patterns in networks"
# fmt: off
a = np.array([[0.258, 0.016, 0.035, 0.013],
[0.012, 0.157, 0.058, 0.019],
[0.013, 0.023, 0.306, 0.035],
[0.005, 0.007, 0.024, 0.016]])
# fmt: on
r = attribute_ac(a)
np.testing.assert_almost_equal(r, 0.623, decimal=3)
def test_attribute_assortativity_coefficient2(self):
# fmt: off
a = np.array([[0.18, 0.02, 0.01, 0.03],
[0.02, 0.20, 0.03, 0.02],
[0.01, 0.03, 0.16, 0.01],
[0.03, 0.02, 0.01, 0.22]])
# fmt: on
r = attribute_ac(a)
np.testing.assert_almost_equal(r, 0.68, decimal=2)
def test_attribute_assortativity(self):
a = np.array([[50, 50, 0], [50, 50, 0], [0, 0, 2]])
r = attribute_ac(a)
np.testing.assert_almost_equal(r, 0.029, decimal=3)
class TestNumericMixingCorrelation(BaseTestNumericMixing):
def test_numeric_assortativity_negative(self):
r = nx.numeric_assortativity_coefficient(self.N, "margin")
np.testing.assert_almost_equal(r, -0.2903, decimal=4)
def test_numeric_assortativity_float(self):
r = nx.numeric_assortativity_coefficient(self.F, "margin")
np.testing.assert_almost_equal(r, -0.1429, decimal=4)
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