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BUG: Expr.__array_ufunc__ can't handle 0-dim array #1219

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BUG: Expr.__array_ufunc__ can't handle 0-dim array #1219

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e80d30e
Handle 0-dim numpy arrays in expr ufuncs
Zeroto521 May 12, 2026
4e3f0a9
Add test for np.float64 comparisons with Expr
Zeroto521 May 12, 2026
833b4d4
Changelog: note Expr __array_ufunc__ 0-dim fix
Zeroto521 May 12, 2026
8d80bd7
Add more numpy comparison tests for Expr
Zeroto521 May 13, 2026
3c4b524
Clarify numpy comparison comments in tests
Zeroto521 May 13, 2026
7dfc027
Test <= with 0-dim Variable array raises TypeError
Zeroto521 May 13, 2026
e4c6749
Clarify comment on numpy scalar/0-dim handling
Zeroto521 May 13, 2026
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1 change: 1 addition & 0 deletions CHANGELOG.md
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Original file line number Diff line number Diff line change
Expand Up @@ -3,6 +3,7 @@
## Unreleased
### Added
### Fixed
- `Expr.__array_ufunc__` can't handle 0-dim array
### Changed
### Removed

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17 changes: 13 additions & 4 deletions src/pyscipopt/expr.pxi
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Expand Up @@ -203,16 +203,25 @@ cdef class ExprLike:
)

if method == "__call__":
if arrays := [a for a in args if isinstance(a, np.ndarray)]:
if arrays := [a for a in args if isinstance(a, np.ndarray) and a.ndim >= 1]:
if any(a.dtype.kind not in "fiub" for a in arrays):
return NotImplemented
# If the np.ndarray is of numeric type, all arguments are converted to
# MatrixExpr or MatrixGenExpr and then the ufunc is applied.
return ufunc(*[_ensure_matrix(a) for a in args], **kwargs)

# Convert `np.generic` to native Python types to stop __array_ufunc__
# recursion from `np.generic + MatrixExpr`.
args = [a.item() if isinstance(a, np.generic) else a for a in args]
# Convert `np.generic` and 0-dim `np.ndarray` to native Python types to stop
# __array_ufunc__ recursion from `np.generic + MatrixExpr/Expr` or
# `0-dim np.ndarray + MatrixExpr/Expr`.
args = [
a.item()
if (
isinstance(a, np.generic)
or (isinstance(a, np.ndarray) and a.ndim == 0)
)
else a
for a in args
]
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@Zeroto521 Zeroto521 May 13, 2026 •
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In this case, 1 <= np.array(x) gets recursion and crushes the Python kernel.

The 1-d+ path returns NotImplemented for non-numeric dtypes, but the new 0-d unwrap calls .item() unconditionally. So np.array(some_object, dtype=object) <= x would now silently extract the Python
object instead of returning NotImplemented. Probably never hit in practice, but easy to keep consistent:

args = [
    a.item()
    if isinstance(a, np.generic)
    or (isinstance(a, np.ndarray) and a.ndim == 0 and a.dtype.kind in "fiub")
    else a
    for a in args
]


if ufunc is np.add:
return args[0] + args[1]
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28 changes: 28 additions & 0 deletions tests/test_expr.py
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Expand Up @@ -328,6 +328,34 @@ def test_binary_ufunc(model):
assert str(np.greater_equal(a, x)) == "[ExprCons(Expr({Term(x): 1.0}), None, 2.0)]"


def test_np_generic_vs_expr():
# test #1218
m = Model()
x = m.addVar(name="x")
value = np.float64(5.0)

# test <=, np.generic vs Variable
assert str(x <= -value) == "ExprCons(Expr({Term(x): 1.0}), None, -5.0)"
assert str(x <= value) == "ExprCons(Expr({Term(x): 1.0}), None, 5.0)"
assert str(-value <= x) == "ExprCons(Expr({Term(x): 1.0}), -5.0, None)"
assert str(value <= x) == "ExprCons(Expr({Term(x): 1.0}), 5.0, None)"
assert str(np.int64(5) <= x) == "ExprCons(Expr({Term(x): 1.0}), 5.0, None)"

# test >=, np.generic vs Variable
assert str(value >= x) == "ExprCons(Expr({Term(x): 1.0}), None, 5.0)"
assert str(-value >= x) == "ExprCons(Expr({Term(x): 1.0}), None, -5.0)"

# test ==, np.generic vs Variable
assert str(value == x) == "ExprCons(Expr({Term(x): 1.0}), 5.0, 5.0)"

# test <=, 0-ndim int array vs Variable
assert str(np.array(5) <= x) == "ExprCons(Expr({Term(x): 1.0}), 5.0, None)"

# test <=, 0-ndim Variable array vs Variable
with pytest.raises(TypeError):
1 <= np.array(x)


def test_mul():
m = Model()
x = m.addVar(name="x")
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