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import pytest
np = pytest.importorskip("numpy")
sp = pytest.importorskip("scipy")
import networkx as nx
from networkx.generators.classic import barbell_graph, cycle_graph, path_graph
from networkx.utils import graphs_equal
class TestConvertScipy:
def setup_method(self):
self.G1 = barbell_graph(10, 3)
self.G2 = cycle_graph(10, create_using=nx.DiGraph)
self.G3 = self.create_weighted(nx.Graph())
self.G4 = self.create_weighted(nx.DiGraph())
def test_exceptions(self):
class G:
format = None
pytest.raises(nx.NetworkXError, nx.to_networkx_graph, G)
def create_weighted(self, G):
g = cycle_graph(4)
e = list(g.edges())
source = [u for u, v in e]
dest = [v for u, v in e]
weight = [s + 10 for s in source]
ex = zip(source, dest, weight)
G.add_weighted_edges_from(ex)
return G
def identity_conversion(self, G, A, create_using):
GG = nx.from_scipy_sparse_array(A, create_using=create_using)
assert nx.is_isomorphic(G, GG)
GW = nx.to_networkx_graph(A, create_using=create_using)
assert nx.is_isomorphic(G, GW)
GI = nx.empty_graph(0, create_using).__class__(A)
assert nx.is_isomorphic(G, GI)
ACSR = A.tocsr()
GI = nx.empty_graph(0, create_using).__class__(ACSR)
assert nx.is_isomorphic(G, GI)
ACOO = A.tocoo()
GI = nx.empty_graph(0, create_using).__class__(ACOO)
assert nx.is_isomorphic(G, GI)
ACSC = A.tocsc()
GI = nx.empty_graph(0, create_using).__class__(ACSC)
assert nx.is_isomorphic(G, GI)
AD = A.todense()
GI = nx.empty_graph(0, create_using).__class__(AD)
assert nx.is_isomorphic(G, GI)
AA = A.toarray()
GI = nx.empty_graph(0, create_using).__class__(AA)
assert nx.is_isomorphic(G, GI)
def test_shape(self):
"Conversion from non-square sparse array."
A = sp.sparse.lil_array([[1, 2, 3], [4, 5, 6]])
pytest.raises(nx.NetworkXError, nx.from_scipy_sparse_array, A)
def test_identity_graph_matrix(self):
"Conversion from graph to sparse matrix to graph."
A = nx.to_scipy_sparse_array(self.G1)
self.identity_conversion(self.G1, A, nx.Graph())
def test_identity_digraph_matrix(self):
"Conversion from digraph to sparse matrix to digraph."
A = nx.to_scipy_sparse_array(self.G2)
self.identity_conversion(self.G2, A, nx.DiGraph())
def test_identity_weighted_graph_matrix(self):
"""Conversion from weighted graph to sparse matrix to weighted graph."""
A = nx.to_scipy_sparse_array(self.G3)
self.identity_conversion(self.G3, A, nx.Graph())
def test_identity_weighted_digraph_matrix(self):
"""Conversion from weighted digraph to sparse matrix to weighted digraph."""
A = nx.to_scipy_sparse_array(self.G4)
self.identity_conversion(self.G4, A, nx.DiGraph())
def test_nodelist(self):
"""Conversion from graph to sparse matrix to graph with nodelist."""
P4 = path_graph(4)
P3 = path_graph(3)
nodelist = list(P3.nodes())
A = nx.to_scipy_sparse_array(P4, nodelist=nodelist)
GA = nx.Graph(A)
assert nx.is_isomorphic(GA, P3)
pytest.raises(nx.NetworkXError, nx.to_scipy_sparse_array, P3, nodelist=[])
# Test nodelist duplicates.
long_nl = nodelist + [0]
pytest.raises(nx.NetworkXError, nx.to_scipy_sparse_array, P3, nodelist=long_nl)
# Test nodelist contains non-nodes
non_nl = [-1, 0, 1, 2]
pytest.raises(nx.NetworkXError, nx.to_scipy_sparse_array, P3, nodelist=non_nl)
def test_weight_keyword(self):
WP4 = nx.Graph()
WP4.add_edges_from((n, n + 1, {"weight": 0.5, "other": 0.3}) for n in range(3))
P4 = path_graph(4)
A = nx.to_scipy_sparse_array(P4)
np.testing.assert_equal(
A.todense(), nx.to_scipy_sparse_array(WP4, weight=None).todense()
)
np.testing.assert_equal(
0.5 * A.todense(), nx.to_scipy_sparse_array(WP4).todense()
)
np.testing.assert_equal(
0.3 * A.todense(), nx.to_scipy_sparse_array(WP4, weight="other").todense()
)
def test_format_keyword(self):
WP4 = nx.Graph()
WP4.add_edges_from((n, n + 1, {"weight": 0.5, "other": 0.3}) for n in range(3))
P4 = path_graph(4)
A = nx.to_scipy_sparse_array(P4, format="csr")
np.testing.assert_equal(
A.todense(), nx.to_scipy_sparse_array(WP4, weight=None).todense()
)
A = nx.to_scipy_sparse_array(P4, format="csc")
np.testing.assert_equal(
A.todense(), nx.to_scipy_sparse_array(WP4, weight=None).todense()
)
A = nx.to_scipy_sparse_array(P4, format="coo")
np.testing.assert_equal(
A.todense(), nx.to_scipy_sparse_array(WP4, weight=None).todense()
)
A = nx.to_scipy_sparse_array(P4, format="bsr")
np.testing.assert_equal(
A.todense(), nx.to_scipy_sparse_array(WP4, weight=None).todense()
)
A = nx.to_scipy_sparse_array(P4, format="lil")
np.testing.assert_equal(
A.todense(), nx.to_scipy_sparse_array(WP4, weight=None).todense()
)
A = nx.to_scipy_sparse_array(P4, format="dia")
np.testing.assert_equal(
A.todense(), nx.to_scipy_sparse_array(WP4, weight=None).todense()
)
A = nx.to_scipy_sparse_array(P4, format="dok")
np.testing.assert_equal(
A.todense(), nx.to_scipy_sparse_array(WP4, weight=None).todense()
)
def test_format_keyword_raise(self):
with pytest.raises(nx.NetworkXError):
WP4 = nx.Graph()
WP4.add_edges_from(
(n, n + 1, {"weight": 0.5, "other": 0.3}) for n in range(3)
)
P4 = path_graph(4)
nx.to_scipy_sparse_array(P4, format="any_other")
def test_null_raise(self):
with pytest.raises(nx.NetworkXError):
nx.to_scipy_sparse_array(nx.Graph())
def test_empty(self):
G = nx.Graph()
G.add_node(1)
M = nx.to_scipy_sparse_array(G)
np.testing.assert_equal(M.toarray(), np.array([[0]]))
def test_ordering(self):
G = nx.DiGraph()
G.add_edge(1, 2)
G.add_edge(2, 3)
G.add_edge(3, 1)
M = nx.to_scipy_sparse_array(G, nodelist=[3, 2, 1])
np.testing.assert_equal(
M.toarray(), np.array([[0, 0, 1], [1, 0, 0], [0, 1, 0]])
)
def test_selfloop_graph(self):
G = nx.Graph([(1, 1)])
M = nx.to_scipy_sparse_array(G)
np.testing.assert_equal(M.toarray(), np.array([[1]]))
G.add_edges_from([(2, 3), (3, 4)])
M = nx.to_scipy_sparse_array(G, nodelist=[2, 3, 4])
np.testing.assert_equal(
M.toarray(), np.array([[0, 1, 0], [1, 0, 1], [0, 1, 0]])
)
def test_selfloop_digraph(self):
G = nx.DiGraph([(1, 1)])
M = nx.to_scipy_sparse_array(G)
np.testing.assert_equal(M.toarray(), np.array([[1]]))
G.add_edges_from([(2, 3), (3, 4)])
M = nx.to_scipy_sparse_array(G, nodelist=[2, 3, 4])
np.testing.assert_equal(
M.toarray(), np.array([[0, 1, 0], [0, 0, 1], [0, 0, 0]])
)
def test_from_scipy_sparse_array_parallel_edges(self):
"""Tests that the :func:`networkx.from_scipy_sparse_array` function
interprets integer weights as the number of parallel edges when
creating a multigraph.
"""
A = sp.sparse.csr_array([[1, 1], [1, 2]])
# First, with a simple graph, each integer entry in the adjacency
# matrix is interpreted as the weight of a single edge in the graph.
expected = nx.DiGraph()
edges = [(0, 0), (0, 1), (1, 0)]
expected.add_weighted_edges_from([(u, v, 1) for (u, v) in edges])
expected.add_edge(1, 1, weight=2)
actual = nx.from_scipy_sparse_array(
A, parallel_edges=True, create_using=nx.DiGraph
)
assert graphs_equal(actual, expected)
actual = nx.from_scipy_sparse_array(
A, parallel_edges=False, create_using=nx.DiGraph
)
assert graphs_equal(actual, expected)
# Now each integer entry in the adjacency matrix is interpreted as the
# number of parallel edges in the graph if the appropriate keyword
# argument is specified.
edges = [(0, 0), (0, 1), (1, 0), (1, 1), (1, 1)]
expected = nx.MultiDiGraph()
expected.add_weighted_edges_from([(u, v, 1) for (u, v) in edges])
actual = nx.from_scipy_sparse_array(
A, parallel_edges=True, create_using=nx.MultiDiGraph
)
assert graphs_equal(actual, expected)
expected = nx.MultiDiGraph()
expected.add_edges_from(set(edges), weight=1)
# The sole self-loop (edge 0) on vertex 1 should have weight 2.
expected[1][1][0]["weight"] = 2
actual = nx.from_scipy_sparse_array(
A, parallel_edges=False, create_using=nx.MultiDiGraph
)
assert graphs_equal(actual, expected)
def test_symmetric(self):
"""Tests that a symmetric matrix has edges added only once to an
undirected multigraph when using
:func:`networkx.from_scipy_sparse_array`.
"""
A = sp.sparse.csr_array([[0, 1], [1, 0]])
G = nx.from_scipy_sparse_array(A, create_using=nx.MultiGraph)
expected = nx.MultiGraph()
expected.add_edge(0, 1, weight=1)
assert graphs_equal(G, expected)
@pytest.mark.parametrize("sparse_format", ("csr", "csc", "dok"))
def test_from_scipy_sparse_array_formats(sparse_format):
"""Test all formats supported by _generate_weighted_edges."""
# trinode complete graph with non-uniform edge weights
expected = nx.Graph()
expected.add_edges_from(
[
(0, 1, {"weight": 3}),
(0, 2, {"weight": 2}),
(1, 0, {"weight": 3}),
(1, 2, {"weight": 1}),
(2, 0, {"weight": 2}),
(2, 1, {"weight": 1}),
]
)
A = sp.sparse.coo_array([[0, 3, 2], [3, 0, 1], [2, 1, 0]]).asformat(sparse_format)
assert graphs_equal(expected, nx.from_scipy_sparse_array(A))
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