Mercurial > repos > shellac > sam_consensus_v3
diff env/lib/python3.9/site-packages/networkx/linalg/tests/test_graphmatrix.py @ 0:4f3585e2f14b draft default tip
"planemo upload commit 60cee0fc7c0cda8592644e1aad72851dec82c959"
| author | shellac |
|---|---|
| date | Mon, 22 Mar 2021 18:12:50 +0000 |
| parents | |
| children |
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--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/env/lib/python3.9/site-packages/networkx/linalg/tests/test_graphmatrix.py Mon Mar 22 18:12:50 2021 +0000 @@ -0,0 +1,293 @@ +import pytest + +np = pytest.importorskip("numpy") +npt = pytest.importorskip("numpy.testing") +scipy = pytest.importorskip("scipy") + +import networkx as nx +from networkx.generators.degree_seq import havel_hakimi_graph +from networkx.exception import NetworkXError + + +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 + npt.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 + npt.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) + ) + npt.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) + ) + npt.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) + ) + npt.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) + ) + npt.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) + ) + npt.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) + ) + npt.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) + ) + npt.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) + ) + npt.assert_equal(I, np.abs(self.OI)) + + # npt.assert_equal(nx.incidence_matrix(self.WG,oriented=True, + # weight='weight').todense(),0.5*self.OI) + # npt.assert_equal(nx.incidence_matrix(self.WG,weight='weight').todense(), + # np.abs(0.5*self.OI)) + # npt.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() + npt.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() + npt.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() + npt.assert_equal(I, 0.3 * self.OI) + + # WMG=nx.MultiGraph(self.WG) + # WMG.add_edge(0,1,weight=0.5,other=0.3) + # npt.assert_equal(nx.incidence_matrix(WMG,weight='weight').todense(), + # np.abs(0.5*self.MGOI)) + # npt.assert_equal(nx.incidence_matrix(WMG,weight='weight',oriented=True).todense(), + # 0.5*self.MGOI) + # npt.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() + npt.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() + npt.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() + npt.assert_equal(I, 0.3 * self.MGOI) + + def test_adjacency_matrix(self): + "Conversion to adjacency matrix" + npt.assert_equal(nx.adj_matrix(self.G).todense(), self.A) + npt.assert_equal(nx.adj_matrix(self.MG).todense(), self.A) + npt.assert_equal(nx.adj_matrix(self.MG2).todense(), self.MG2A) + npt.assert_equal( + nx.adj_matrix(self.G, nodelist=[0, 1]).todense(), self.A[:2, :2] + ) + npt.assert_equal(nx.adj_matrix(self.WG).todense(), self.WA) + npt.assert_equal(nx.adj_matrix(self.WG, weight=None).todense(), self.A) + npt.assert_equal(nx.adj_matrix(self.MG2, weight=None).todense(), self.MG2A) + npt.assert_equal( + nx.adj_matrix(self.WG, weight="other").todense(), 0.6 * self.WA + ) + npt.assert_equal( + nx.adj_matrix(self.no_edges_G, nodelist=[1, 3]).todense(), self.no_edges_A + )