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Test the from_networkx int32 widening and correct its coverage claim by eriknw · Pull Request #618 · python-graphblas/python-graphblas · GitHub

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Test the from_networkx int32 widening and correct its coverage claim #618

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Test the from_networkx int32 widening and correct its coverage claim #618

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12 changes: 7 additions & 5 deletions graphblas/io/_networkx.py
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Original file line number Diff line number Diff line change
Expand Up @@ -96,11 +96,13 @@ def from_networkx(G, nodelist=None, dtype=None, weight="weight", name=None):
dup_op = plus

values = np.array(vals, dtype=dtype)
if dtype is None and values.dtype == np.int32: # pragma: no cover (win64 numpy < 2)
# numpy < 2 infers the platform C long for a sequence of Python ints, which
# is 32-bit on Windows. values_to_numpy_buffer widens the same way for
# non-numpy input, so this keeps from_networkx agreeing with
# Matrix.from_coo on INT64 for an unweighted graph on every platform.
if dtype is None and values.dtype == np.int32:
# ``vals`` is a Python sequence, and from_coo widens an int32 dtype
# INFERRED from sequence input to int64 (values_to_numpy_buffer): python
# ints infer int32 on win64 with numpy < 2, and np.int32 scalar weights
# infer it on every platform. Widen the same way so from_networkx and
# Matrix.from_coo given the same weights agree on the result dtype.
# An explicit dtype= is applied above and never reaches this branch.
values = values.astype(np.int64)
if values.ndim != 1 or values.dtype.kind not in "biufc":
# Defer to scipy so the error matches the previous behavior exactly.
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22 changes: 22 additions & 0 deletions graphblas/tests/test_io.py
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Expand Up @@ -176,6 +176,28 @@ def test_from_networkx_undirected():
assert M_none.isequal(expected_none, check_dtype=True)


@pytest.mark.skipif("not nx")
def test_from_networkx_int32_weights_widen_like_from_coo():
# np.int32 scalar weights infer an int32 array from the edge sequence on
# every platform (python ints do too on win64 with numpy < 2), and from_coo
# widens int32 inferred from sequence input to INT64. from_networkx must
# widen the same way or identical weights would produce different dtypes
# depending on which constructor they came through.
G = nx.DiGraph()
G.add_weighted_edges_from([(0, 1, np.int32(2)), (1, 0, np.int32(3))])
M = gb.io.from_networkx(G)
expected = gb.Matrix.from_coo([0, 1], [1, 0], [np.int32(2), np.int32(3)])
assert expected.dtype == dtypes.INT64
assert M.isequal(expected, check_dtype=True)
# An explicit dtype bypasses inference and is preserved exactly.
assert gb.io.from_networkx(G, dtype="int32").dtype == dtypes.INT32
# Narrower ints are not special-cased; this also matches from_coo.
H = nx.DiGraph()
H.add_weighted_edges_from([(0, 1, np.int16(2))])
assert gb.io.from_networkx(H).dtype == dtypes.INT16
assert gb.Matrix.from_coo([0], [1], [np.int16(2)]).dtype == dtypes.INT16


@pytest.mark.skipif("not nx or not ss")
@pytest.mark.parametrize(
"graph_cls", [nx.Graph, nx.DiGraph, nx.MultiGraph, nx.MultiDiGraph] if nx else []
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