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Build a weight=None from_networkx from the adjacency alone #645

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Build a weight=None from_networkx from the adjacency alone #645

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11 changes: 11 additions & 0 deletions graphblas/io/_networkx.py
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Original file line number Diff line number Diff line change
Expand Up @@ -68,6 +68,17 @@ def from_networkx(G, nodelist=None, dtype=None, weight="weight", name=None):
G = G.subgraph(nodelist)

index = dict(zip(nodelist, range(nlen), strict=True))
if weight is None and not G.is_multigraph() and (dtype is None or dtype.kind in "biufc"):
# Every entry is 1, so read only the coordinates: row by row in nodelist
# order, straight from the adjacency, which for an undirected graph already
# holds both directions and each self-loop once. No tuple and no value per
# edge makes this two to three times faster than the general path below.
nbrs = [G._adj[u] for u in nodelist]
rows = np.repeat(np.arange(nlen, dtype=np.uint64), [len(nbr) for nbr in nbrs])
if rows.size: # with no edge, the general path gives the dtype (scipy's float64)
cols = np.fromiter((index[v] for nbr in nbrs for v in nbr), np.uint64, rows.size)
dtype = np.int64 if dtype is None else dtype # as the general path infers the 1s
return Matrix.from_coo(rows, cols, 1, dtype=dtype, nrows=nlen, ncols=nlen, name=name)
coefficients = zip(
*((index[u], index[v], wt) for u, v, wt in G.edges(data=weight, default=1)),
strict=True,
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30 changes: 30 additions & 0 deletions graphblas/tests/test_io.py
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Expand Up @@ -262,6 +262,36 @@ def test_from_networkx_uniform_weights_are_iso(graph_cls, selfloop, weight):
assert M.ss.is_iso


@pytest.mark.skipif("not nx")
@pytest.mark.parametrize("graph_cls", [nx.Graph, nx.DiGraph] if nx else [])
@pytest.mark.parametrize("dtype", [None, bool, "float32"])
def test_from_networkx_structure_matches_the_general_path(graph_cls, dtype):
# weight=None reads only the coordinates. An attribute no edge carries takes
# the general path and gives every entry the default 1, so the two agree.
G = graph_cls()
G.add_edges_from([("a", "b"), ("b", ("t", 1)), (("t", 1), ("t", 1)), ("b", "a")])
G.add_node(frozenset({1}))
edgeless = graph_cls()
edgeless.add_nodes_from(range(3))
cases = [
(G, None),
(G, list(G)[::-1]),
(G, [("t", 1), "a"]),
(G, [frozenset({1})]),
(edgeless, None),
]
for H, nodelist in cases:
M = gb.io.from_networkx(H, nodelist=nodelist, weight=None, dtype=dtype)
expected = gb.io.from_networkx(H, nodelist=nodelist, weight="absent", dtype=dtype)
assert M.isequal(expected, check_dtype=True)
if suitesparse and M.nvals > 0:
assert M.ss.is_iso
with pytest.raises(nx.NetworkXError, match="no nodes"):
gb.io.from_networkx(graph_cls(), weight=None)
with pytest.raises(nx.NetworkXError, match="not in G"):
gb.io.from_networkx(G, nodelist=["a", "missing"], weight=None)


@pytest.mark.skipif("not nx")
@pytest.mark.parametrize("wt", [np.inf, -np.inf, 1e308])
def test_from_networkx_undirected_selfloop_keeps_its_weight(wt):
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