241 lines
9.2 KiB
Python
241 lines
9.2 KiB
Python
import pytest
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import networkx as nx
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from networkx.testing import assert_graphs_equal
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from networkx.generators.classic import barbell_graph, cycle_graph, path_graph
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class TestConvertNumpy(object):
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@classmethod
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def setup_class(cls):
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global np, sp, sparse, np_assert_equal
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np = pytest.importorskip('numpy')
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sp = pytest.importorskip('scipy')
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sparse = sp.sparse
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np_assert_equal = np.testing.assert_equal
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def setup_method(self):
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self.G1 = barbell_graph(10, 3)
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self.G2 = cycle_graph(10, create_using=nx.DiGraph)
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self.G3 = self.create_weighted(nx.Graph())
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self.G4 = self.create_weighted(nx.DiGraph())
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def test_exceptions(self):
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class G(object):
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format = None
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pytest.raises(nx.NetworkXError, nx.to_networkx_graph, G)
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def create_weighted(self, G):
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g = cycle_graph(4)
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e = list(g.edges())
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source = [u for u, v in e]
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dest = [v for u, v in e]
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weight = [s + 10 for s in source]
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ex = zip(source, dest, weight)
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G.add_weighted_edges_from(ex)
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return G
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def assert_isomorphic(self, G1, G2):
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assert nx.is_isomorphic(G1, G2)
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def identity_conversion(self, G, A, create_using):
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GG = nx.from_scipy_sparse_matrix(A, create_using=create_using)
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self.assert_isomorphic(G, GG)
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GW = nx.to_networkx_graph(A, create_using=create_using)
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self.assert_isomorphic(G, GW)
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GI = nx.empty_graph(0, create_using).__class__(A)
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self.assert_isomorphic(G, GI)
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ACSR = A.tocsr()
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GI = nx.empty_graph(0, create_using).__class__(ACSR)
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self.assert_isomorphic(G, GI)
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ACOO = A.tocoo()
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GI = nx.empty_graph(0, create_using).__class__(ACOO)
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self.assert_isomorphic(G, GI)
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ACSC = A.tocsc()
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GI = nx.empty_graph(0, create_using).__class__(ACSC)
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self.assert_isomorphic(G, GI)
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AD = A.todense()
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GI = nx.empty_graph(0, create_using).__class__(AD)
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self.assert_isomorphic(G, GI)
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AA = A.toarray()
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GI = nx.empty_graph(0, create_using).__class__(AA)
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self.assert_isomorphic(G, GI)
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def test_shape(self):
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"Conversion from non-square sparse array."
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A = sp.sparse.lil_matrix([[1, 2, 3], [4, 5, 6]])
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pytest.raises(nx.NetworkXError, nx.from_scipy_sparse_matrix, A)
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def test_identity_graph_matrix(self):
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"Conversion from graph to sparse matrix to graph."
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A = nx.to_scipy_sparse_matrix(self.G1)
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self.identity_conversion(self.G1, A, nx.Graph())
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def test_identity_digraph_matrix(self):
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"Conversion from digraph to sparse matrix to digraph."
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A = nx.to_scipy_sparse_matrix(self.G2)
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self.identity_conversion(self.G2, A, nx.DiGraph())
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def test_identity_weighted_graph_matrix(self):
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"""Conversion from weighted graph to sparse matrix to weighted graph."""
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A = nx.to_scipy_sparse_matrix(self.G3)
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self.identity_conversion(self.G3, A, nx.Graph())
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def test_identity_weighted_digraph_matrix(self):
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"""Conversion from weighted digraph to sparse matrix to weighted digraph."""
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A = nx.to_scipy_sparse_matrix(self.G4)
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self.identity_conversion(self.G4, A, nx.DiGraph())
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def test_nodelist(self):
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"""Conversion from graph to sparse matrix to graph with nodelist."""
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P4 = path_graph(4)
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P3 = path_graph(3)
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nodelist = list(P3.nodes())
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A = nx.to_scipy_sparse_matrix(P4, nodelist=nodelist)
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GA = nx.Graph(A)
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self.assert_isomorphic(GA, P3)
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# Make nodelist ambiguous by containing duplicates.
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nodelist += [nodelist[0]]
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pytest.raises(nx.NetworkXError, nx.to_numpy_matrix, P3,
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nodelist=nodelist)
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def test_weight_keyword(self):
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WP4 = nx.Graph()
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WP4.add_edges_from((n, n + 1, dict(weight=0.5, other=0.3))
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for n in range(3))
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P4 = path_graph(4)
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A = nx.to_scipy_sparse_matrix(P4)
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np_assert_equal(A.todense(),
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nx.to_scipy_sparse_matrix(WP4, weight=None).todense())
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np_assert_equal(0.5 * A.todense(),
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nx.to_scipy_sparse_matrix(WP4).todense())
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np_assert_equal(0.3 * A.todense(),
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nx.to_scipy_sparse_matrix(WP4, weight='other').todense())
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def test_format_keyword(self):
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WP4 = nx.Graph()
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WP4.add_edges_from((n, n + 1, dict(weight=0.5, other=0.3))
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for n in range(3))
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P4 = path_graph(4)
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A = nx.to_scipy_sparse_matrix(P4, format='csr')
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np_assert_equal(A.todense(),
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nx.to_scipy_sparse_matrix(WP4, weight=None).todense())
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A = nx.to_scipy_sparse_matrix(P4, format='csc')
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np_assert_equal(A.todense(),
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nx.to_scipy_sparse_matrix(WP4, weight=None).todense())
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A = nx.to_scipy_sparse_matrix(P4, format='coo')
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np_assert_equal(A.todense(),
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nx.to_scipy_sparse_matrix(WP4, weight=None).todense())
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A = nx.to_scipy_sparse_matrix(P4, format='bsr')
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np_assert_equal(A.todense(),
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nx.to_scipy_sparse_matrix(WP4, weight=None).todense())
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A = nx.to_scipy_sparse_matrix(P4, format='lil')
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np_assert_equal(A.todense(),
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nx.to_scipy_sparse_matrix(WP4, weight=None).todense())
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A = nx.to_scipy_sparse_matrix(P4, format='dia')
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np_assert_equal(A.todense(),
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nx.to_scipy_sparse_matrix(WP4, weight=None).todense())
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A = nx.to_scipy_sparse_matrix(P4, format='dok')
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np_assert_equal(A.todense(),
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nx.to_scipy_sparse_matrix(WP4, weight=None).todense())
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def test_format_keyword_raise(self):
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with pytest.raises(nx.NetworkXError):
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WP4 = nx.Graph()
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WP4.add_edges_from((n, n + 1, dict(weight=0.5, other=0.3))
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for n in range(3))
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P4 = path_graph(4)
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nx.to_scipy_sparse_matrix(P4, format='any_other')
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def test_null_raise(self):
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with pytest.raises(nx.NetworkXError):
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nx.to_scipy_sparse_matrix(nx.Graph())
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def test_empty(self):
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G = nx.Graph()
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G.add_node(1)
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M = nx.to_scipy_sparse_matrix(G)
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np_assert_equal(M.todense(), np.matrix([[0]]))
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def test_ordering(self):
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G = nx.DiGraph()
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G.add_edge(1, 2)
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G.add_edge(2, 3)
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G.add_edge(3, 1)
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M = nx.to_scipy_sparse_matrix(G, nodelist=[3, 2, 1])
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np_assert_equal(M.todense(), np.matrix([[0, 0, 1], [1, 0, 0], [0, 1, 0]]))
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def test_selfloop_graph(self):
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G = nx.Graph([(1, 1)])
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M = nx.to_scipy_sparse_matrix(G)
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np_assert_equal(M.todense(), np.matrix([[1]]))
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def test_selfloop_digraph(self):
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G = nx.DiGraph([(1, 1)])
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M = nx.to_scipy_sparse_matrix(G)
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np_assert_equal(M.todense(), np.matrix([[1]]))
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def test_from_scipy_sparse_matrix_parallel_edges(self):
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"""Tests that the :func:`networkx.from_scipy_sparse_matrix` function
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interprets integer weights as the number of parallel edges when
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creating a multigraph.
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"""
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A = sparse.csr_matrix([[1, 1], [1, 2]])
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# First, with a simple graph, each integer entry in the adjacency
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# matrix is interpreted as the weight of a single edge in the graph.
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expected = nx.DiGraph()
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edges = [(0, 0), (0, 1), (1, 0)]
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expected.add_weighted_edges_from([(u, v, 1) for (u, v) in edges])
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expected.add_edge(1, 1, weight=2)
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actual = nx.from_scipy_sparse_matrix(A, parallel_edges=True,
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create_using=nx.DiGraph)
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assert_graphs_equal(actual, expected)
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actual = nx.from_scipy_sparse_matrix(A, parallel_edges=False,
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create_using=nx.DiGraph)
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assert_graphs_equal(actual, expected)
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# Now each integer entry in the adjacency matrix is interpreted as the
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# number of parallel edges in the graph if the appropriate keyword
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# argument is specified.
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edges = [(0, 0), (0, 1), (1, 0), (1, 1), (1, 1)]
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expected = nx.MultiDiGraph()
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expected.add_weighted_edges_from([(u, v, 1) for (u, v) in edges])
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actual = nx.from_scipy_sparse_matrix(A, parallel_edges=True,
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create_using=nx.MultiDiGraph)
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assert_graphs_equal(actual, expected)
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expected = nx.MultiDiGraph()
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expected.add_edges_from(set(edges), weight=1)
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# The sole self-loop (edge 0) on vertex 1 should have weight 2.
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expected[1][1][0]['weight'] = 2
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actual = nx.from_scipy_sparse_matrix(A, parallel_edges=False,
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create_using=nx.MultiDiGraph)
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assert_graphs_equal(actual, expected)
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def test_symmetric(self):
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"""Tests that a symmetric matrix has edges added only once to an
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undirected multigraph when using
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:func:`networkx.from_scipy_sparse_matrix`.
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"""
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A = sparse.csr_matrix([[0, 1], [1, 0]])
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G = nx.from_scipy_sparse_matrix(A, create_using=nx.MultiGraph)
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expected = nx.MultiGraph()
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expected.add_edge(0, 1, weight=1)
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assert_graphs_equal(G, expected)
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