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import pytest
import networkx as nx
np = pytest.importorskip("numpy")
pytest.importorskip("scipy")
from networkx.testing import almost_equal
from networkx.algorithms.link_analysis.hits_alg import _hits_python
# Example from
# A. Langville and C. Meyer, "A survey of eigenvector methods of web
# information retrieval." http://citeseer.ist.psu.edu/713792.html
class TestHITS:
@classmethod
def setup_class(cls):
G = nx.DiGraph()
edges = [(1, 3), (1, 5), (2, 1), (3, 5), (5, 4), (5, 3), (6, 5)]
G.add_edges_from(edges, weight=1)
cls.G = G
cls.G.a = dict(
zip(sorted(G), [0.000000, 0.000000, 0.366025, 0.133975, 0.500000, 0.000000])
)
cls.G.h = dict(
zip(sorted(G), [0.366025, 0.000000, 0.211325, 0.000000, 0.211325, 0.211325])
)
def test_hits_numpy(self):
G = self.G
h, a = nx.hits_numpy(G)
for n in G:
assert almost_equal(h[n], G.h[n], places=4)
for n in G:
assert almost_equal(a[n], G.a[n], places=4)
@pytest.mark.parametrize(
"hits_alg",
(nx.hits, nx.hits_scipy, _hits_python),
)
def test_hits(self, hits_alg):
G = self.G
h, a = hits_alg(G, tol=1.0e-08)
for n in G:
assert almost_equal(h[n], G.h[n], places=4)
for n in G:
assert almost_equal(a[n], G.a[n], places=4)
nstart = {i: 1.0 / 2 for i in G}
h, a = hits_alg(G, nstart=nstart)
for n in G:
assert almost_equal(h[n], G.h[n], places=4)
for n in G:
assert almost_equal(a[n], G.a[n], places=4)
def test_empty(self):
G = nx.Graph()
assert nx.hits(G) == ({}, {})
assert nx.hits_numpy(G) == ({}, {})
assert _hits_python(G) == ({}, {})
assert nx.hits_scipy(G) == ({}, {})
assert nx.authority_matrix(G).shape == (0, 0)
assert nx.hub_matrix(G).shape == (0, 0)
def test_hits_not_convergent(self):
with pytest.raises(nx.PowerIterationFailedConvergence):
G = self.G
nx.hits(G, max_iter=0)
@pytest.mark.parametrize(
"hits_alg",
(nx.hits_numpy, nx.hits_scipy),
)
def test_deprecation_warnings(hits_alg):
"""Make sure deprecation warnings are raised.
To be removed when deprecations expire.
"""
G = nx.DiGraph(nx.path_graph(4))
with pytest.warns(DeprecationWarning):
pr = hits_alg(G)
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