Vehicle-cpp/hkpc/software/networkx/linalg/tests/test_graphmatrix.py

import pytest

np = pytest.importorskip("numpy")
pytest.importorskip("scipy")

import networkx as nx
from networkx.exception import NetworkXError
from networkx.generators.degree_seq import havel_hakimi_graph


def test_incidence_matrix_simple():
    deg = [3, 2, 2, 1, 0]
    G = 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).todense().astype(int)
    # 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).todense().astype(int)
    # 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 = 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 = 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,
            )
            .todense()
            .astype(int)
        )
        np.testing.assert_equal(I, self.OI)

        I = (
            nx.incidence_matrix(
                self.G,
                nodelist=sorted(self.G),
                edgelist=sorted(self.G.edges()),
                oriented=False,
            )
            .todense()
            .astype(int)
        )
        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,
            )
            .todense()
            .astype(int)
        )
        np.testing.assert_equal(I, self.OI)

        I = (
            nx.incidence_matrix(
                self.MG,
                nodelist=sorted(self.MG),
                edgelist=sorted(self.MG.edges()),
                oriented=False,
            )
            .todense()
            .astype(int)
        )
        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,
            )
            .todense()
            .astype(int)
        )
        np.testing.assert_equal(I, self.MGOI)

        I = (
            nx.incidence_matrix(
                self.MG2,
                nodelist=sorted(self.MG),
                edgelist=sorted(self.MG2.edges()),
                oriented=False,
            )
            .todense()
            .astype(int)
        )
        np.testing.assert_equal(I, np.abs(self.MGOI))

    def test_weighted_incidence_matrix(self):
        I = (
            nx.incidence_matrix(
                self.WG,
                nodelist=sorted(self.WG),
                edgelist=sorted(self.WG.edges()),
                oriented=True,
            )
            .todense()
            .astype(int)
        )
        np.testing.assert_equal(I, self.OI)

        I = (
            nx.incidence_matrix(
                self.WG,
                nodelist=sorted(self.WG),
                edgelist=sorted(self.WG.edges()),
                oriented=False,
            )
            .todense()
            .astype(int)
        )
        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,
        )