Hugging Face's logo

anonymous-CAP
/
cap-hatespeech

PyTorch
Model card Files
xet
 Community
cap-hatespeech / venv /lib /python3.12 /site-packages /networkx /linalg /tests /test_graphmatrix.py
anonymous-CAP's picture
anonymous-CAP
Upload folder using huggingface_hub (part 8)
1e321d6 verified
Raw History Blame Contribute Delete
8.66 kB
import pytest
import networkx as nx
from networkx.exception import NetworkXError
np = pytest.importorskip("numpy")
pytest.importorskip("scipy")
def test_incidence_matrix_simple():
deg = [3, 2, 2, 1, 0]
G = nx.havel_hakimi_graph(deg)
deg = [(1, 0), (1, 0), (1, 0), (2, 0), (1, 0), (2, 1), (0, 1), (0, 1)]
MG = nx.random_clustered_graph(deg, seed=42)
I = nx.incidence_matrix(G, dtype=int).todense()
# fmt: off
expected = np.array(
[[1, 1, 1, 0],
[0, 1, 0, 1],
[1, 0, 0, 1],
[0, 0, 1, 0],
[0, 0, 0, 0]]
)
# fmt: on
np.testing.assert_equal(I, expected)
I = nx.incidence_matrix(MG, dtype=int).todense()
# fmt: off
expected = np.array(
[[1, 0, 0, 0, 0, 0, 0],
[1, 0, 0, 0, 0, 0, 0],
[0, 1, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0],
[0, 1, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 1, 1, 0],
[0, 0, 0, 0, 0, 1, 1],
[0, 0, 0, 0, 1, 0, 1]]
)
# fmt: on
np.testing.assert_equal(I, expected)
with pytest.raises(NetworkXError):
nx.incidence_matrix(G, nodelist=[0, 1])
class TestGraphMatrix:
@classmethod
def setup_class(cls):
deg = [3, 2, 2, 1, 0]
cls.G = nx.havel_hakimi_graph(deg)
# fmt: off
cls.OI = np.array(
[[-1, -1, -1, 0],
[1, 0, 0, -1],
[0, 1, 0, 1],
[0, 0, 1, 0],
[0, 0, 0, 0]]
)
cls.A = np.array(
[[0, 1, 1, 1, 0],
[1, 0, 1, 0, 0],
[1, 1, 0, 0, 0],
[1, 0, 0, 0, 0],
[0, 0, 0, 0, 0]]
)
# fmt: on
cls.WG = nx.havel_hakimi_graph(deg)
cls.WG.add_edges_from(
(u, v, {"weight": 0.5, "other": 0.3}) for (u, v) in cls.G.edges()
)
# fmt: off
cls.WA = np.array(
[[0, 0.5, 0.5, 0.5, 0],
[0.5, 0, 0.5, 0, 0],
[0.5, 0.5, 0, 0, 0],
[0.5, 0, 0, 0, 0],
[0, 0, 0, 0, 0]]
)
# fmt: on
cls.MG = nx.MultiGraph(cls.G)
cls.MG2 = cls.MG.copy()
cls.MG2.add_edge(0, 1)
# fmt: off
cls.MG2A = np.array(
[[0, 2, 1, 1, 0],
[2, 0, 1, 0, 0],
[1, 1, 0, 0, 0],
[1, 0, 0, 0, 0],
[0, 0, 0, 0, 0]]
)
cls.MGOI = np.array(
[[-1, -1, -1, -1, 0],
[1, 1, 0, 0, -1],
[0, 0, 1, 0, 1],
[0, 0, 0, 1, 0],
[0, 0, 0, 0, 0]]
)
# fmt: on
cls.no_edges_G = nx.Graph([(1, 2), (3, 2, {"weight": 8})])
cls.no_edges_A = np.array([[0, 0], [0, 0]])
def test_incidence_matrix(self):
"Conversion to incidence matrix"
I = nx.incidence_matrix(
self.G,
nodelist=sorted(self.G),
edgelist=sorted(self.G.edges()),
oriented=True,
dtype=int,
).todense()
np.testing.assert_equal(I, self.OI)
I = nx.incidence_matrix(
self.G,
nodelist=sorted(self.G),
edgelist=sorted(self.G.edges()),
oriented=False,
dtype=int,
).todense()
np.testing.assert_equal(I, np.abs(self.OI))
I = nx.incidence_matrix(
self.MG,
nodelist=sorted(self.MG),
edgelist=sorted(self.MG.edges()),
oriented=True,
dtype=int,
).todense()
np.testing.assert_equal(I, self.OI)
I = nx.incidence_matrix(
self.MG,
nodelist=sorted(self.MG),
edgelist=sorted(self.MG.edges()),
oriented=False,
dtype=int,
).todense()
np.testing.assert_equal(I, np.abs(self.OI))
I = nx.incidence_matrix(
self.MG2,
nodelist=sorted(self.MG2),
edgelist=sorted(self.MG2.edges()),
oriented=True,
dtype=int,
).todense()
np.testing.assert_equal(I, self.MGOI)
I = nx.incidence_matrix(
self.MG2,
nodelist=sorted(self.MG),
edgelist=sorted(self.MG2.edges()),
oriented=False,
dtype=int,
).todense()
np.testing.assert_equal(I, np.abs(self.MGOI))
I = nx.incidence_matrix(self.G, dtype=np.uint8)
assert I.dtype == np.uint8
def test_weighted_incidence_matrix(self):
I = nx.incidence_matrix(
self.WG,
nodelist=sorted(self.WG),
edgelist=sorted(self.WG.edges()),
oriented=True,
dtype=int,
).todense()
np.testing.assert_equal(I, self.OI)
I = nx.incidence_matrix(
self.WG,
nodelist=sorted(self.WG),
edgelist=sorted(self.WG.edges()),
oriented=False,
dtype=int,
).todense()
np.testing.assert_equal(I, np.abs(self.OI))
# np.testing.assert_equal(nx.incidence_matrix(self.WG,oriented=True,
# weight='weight').todense(),0.5*self.OI)
# np.testing.assert_equal(nx.incidence_matrix(self.WG,weight='weight').todense(),
# np.abs(0.5*self.OI))
# np.testing.assert_equal(nx.incidence_matrix(self.WG,oriented=True,weight='other').todense(),
# 0.3*self.OI)
I = nx.incidence_matrix(
self.WG,
nodelist=sorted(self.WG),
edgelist=sorted(self.WG.edges()),
oriented=True,
weight="weight",
).todense()
np.testing.assert_equal(I, 0.5 * self.OI)
I = nx.incidence_matrix(
self.WG,
nodelist=sorted(self.WG),
edgelist=sorted(self.WG.edges()),
oriented=False,
weight="weight",
).todense()
np.testing.assert_equal(I, np.abs(0.5 * self.OI))
I = nx.incidence_matrix(
self.WG,
nodelist=sorted(self.WG),
edgelist=sorted(self.WG.edges()),
oriented=True,
weight="other",
).todense()
np.testing.assert_equal(I, 0.3 * self.OI)
# WMG=nx.MultiGraph(self.WG)
# WMG.add_edge(0,1,weight=0.5,other=0.3)
# np.testing.assert_equal(nx.incidence_matrix(WMG,weight='weight').todense(),
# np.abs(0.5*self.MGOI))
# np.testing.assert_equal(nx.incidence_matrix(WMG,weight='weight',oriented=True).todense(),
# 0.5*self.MGOI)
# np.testing.assert_equal(nx.incidence_matrix(WMG,weight='other',oriented=True).todense(),
# 0.3*self.MGOI)
WMG = nx.MultiGraph(self.WG)
WMG.add_edge(0, 1, weight=0.5, other=0.3)
I = nx.incidence_matrix(
WMG,
nodelist=sorted(WMG),
edgelist=sorted(WMG.edges(keys=True)),
oriented=True,
weight="weight",
).todense()
np.testing.assert_equal(I, 0.5 * self.MGOI)
I = nx.incidence_matrix(
WMG,
nodelist=sorted(WMG),
edgelist=sorted(WMG.edges(keys=True)),
oriented=False,
weight="weight",
).todense()
np.testing.assert_equal(I, np.abs(0.5 * self.MGOI))
I = nx.incidence_matrix(
WMG,
nodelist=sorted(WMG),
edgelist=sorted(WMG.edges(keys=True)),
oriented=True,
weight="other",
).todense()
np.testing.assert_equal(I, 0.3 * self.MGOI)
def test_adjacency_matrix(self):
"Conversion to adjacency matrix"
np.testing.assert_equal(nx.adjacency_matrix(self.G).todense(), self.A)
np.testing.assert_equal(nx.adjacency_matrix(self.MG).todense(), self.A)
np.testing.assert_equal(nx.adjacency_matrix(self.MG2).todense(), self.MG2A)
np.testing.assert_equal(
nx.adjacency_matrix(self.G, nodelist=[0, 1]).todense(), self.A[:2, :2]
)
np.testing.assert_equal(nx.adjacency_matrix(self.WG).todense(), self.WA)
np.testing.assert_equal(
nx.adjacency_matrix(self.WG, weight=None).todense(), self.A
)
np.testing.assert_equal(
nx.adjacency_matrix(self.MG2, weight=None).todense(), self.MG2A
)
np.testing.assert_equal(
nx.adjacency_matrix(self.WG, weight="other").todense(), 0.6 * self.WA
)
np.testing.assert_equal(
nx.adjacency_matrix(self.no_edges_G, nodelist=[1, 3]).todense(),
self.no_edges_A,
)