MAINT: bump to sparse >=0.17

src/array_api_extra/_lib/_utils/_helpers.py

@@ -322,26 +322,28 @@ def capabilities(
     dict
         Capabilities of the namespace.
     """
-    if is_pydata_sparse_namespace(xp):
-        # No __array_namespace_info__(); no indexing by sparse arrays
-        return {
-            "boolean indexing": False,
-            "data-dependent shapes": True,
-            "max dimensions": None,
-        }
     out = xp.__array_namespace_info__().capabilities()
-    if is_jax_namespace(xp) and out["boolean indexing"]:
-        # FIXME https://github.com/jax-ml/jax/issues/27418
-        # Fixed in jax >=0.6.0
-        out = out.copy()
-        out["boolean indexing"] = False
-    if is_torch_namespace(xp):
+    if is_pydata_sparse_namespace(xp):
+        if out["boolean indexing"]:
+            # FIXME https://github.com/pydata/sparse/issues/876
+            # boolean indexing is supported, but not when the index is a sparse array.
+            # boolean indexing by list or numpy array is not part of the Array API.
+            out = out.copy()
+            out["boolean indexing"] = False
+    elif is_jax_namespace(xp):
+        if out["boolean indexing"]:  # pragma: no cover
+            # Backwards compatibility with jax <0.6.0
+            # https://github.com/jax-ml/jax/issues/27418
+            out = out.copy()
+            out["boolean indexing"] = False
+    elif is_torch_namespace(xp):
         # FIXME https://github.com/data-apis/array-api/issues/945
         device = xp.get_default_device() if device is None else xp.device(device)
         if device.type == "meta":  # type: ignore[union-attr]  # pyright: ignore[reportAttributeAccessIssue,reportOptionalMemberAccess]
             out = out.copy()
             out["boolean indexing"] = False
             out["data-dependent shapes"] = False
+
     return out
 
 

tests/test_funcs.py

@@ -416,7 +416,7 @@ def test_complex(self, xp: ModuleType):
         expect = xp.asarray([[1.0, -1.0j], [1.0j, 1.0]], dtype=xp.complex128)
         xp_assert_close(actual, expect)
 
-    @pytest.mark.skip_xp_backend(Backend.SPARSE, reason="matmul with nan fillvalue")
+    @pytest.mark.xfail_xp_backend(Backend.SPARSE, reason="matmul with nan fillvalue")
     def test_empty(self, xp: ModuleType):
         with warnings.catch_warnings(record=True):
             warnings.simplefilter("always", RuntimeWarning)
@@ -451,7 +451,7 @@ def test_xp(self, xp: ModuleType):
         )
 
 
-@pytest.mark.skip_xp_backend(Backend.SPARSE, reason="backend doesn't have arange")
+@pytest.mark.xfail_xp_backend(Backend.SPARSE, reason="no arange", strict=False)
 class TestOneHot:
     @pytest.mark.parametrize("n_dim", range(4))
     @pytest.mark.parametrize("num_classes", [1, 3, 10])
@@ -816,7 +816,7 @@ def test_bool_dtype(self, xp: ModuleType):
             isclose(xp.asarray(True), b, atol=1), xp.asarray([True, True, True])
         )
 
-    @pytest.mark.xfail_xp_backend(Backend.SPARSE, reason="index by sparse array")
+    @pytest.mark.skip_xp_backend(Backend.SPARSE, reason="index by sparse array")
     @pytest.mark.skip_xp_backend(Backend.ARRAY_API_STRICTEST, reason="unknown shape")
     def test_none_shape(self, xp: ModuleType):
         a = xp.asarray([1, 5, 0])
@@ -825,7 +825,7 @@ def test_none_shape(self, xp: ModuleType):
         a = a[a < 5]
         xp_assert_equal(isclose(a, b), xp.asarray([True, False]))
 
-    @pytest.mark.xfail_xp_backend(Backend.SPARSE, reason="index by sparse array")
+    @pytest.mark.skip_xp_backend(Backend.SPARSE, reason="index by sparse array")
     @pytest.mark.skip_xp_backend(Backend.ARRAY_API_STRICTEST, reason="unknown shape")
     def test_none_shape_bool(self, xp: ModuleType):
         a = xp.asarray([True, True, False])
@@ -1141,10 +1141,10 @@ def test_xp(self, xp: ModuleType):
 
 
 class TestSinc:
-    @pytest.mark.xfail_xp_backend(Backend.SPARSE, reason="no linspace")
     def test_simple(self, xp: ModuleType):
         xp_assert_equal(sinc(xp.asarray(0.0)), xp.asarray(1.0))
-        w = sinc(xp.linspace(-1, 1, 100))
+        x = xp.asarray(np.linspace(-1, 1, 100))
+        w = sinc(x)
         # check symmetry
         xp_assert_close(w, xp.flip(w, axis=0))
 
@@ -1153,11 +1153,12 @@ def test_dtype(self, xp: ModuleType, x: int | complex):
         with pytest.raises(ValueError, match="real floating data type"):
             _ = sinc(xp.asarray(x))
 
-    @pytest.mark.xfail_xp_backend(Backend.SPARSE, reason="no arange")
     def test_3d(self, xp: ModuleType):
-        x = xp.reshape(xp.arange(18, dtype=xp.float64), (3, 3, 2))
-        expected = xp.zeros((3, 3, 2), dtype=xp.float64)
-        expected = at(expected)[0, 0, 0].set(1.0)
+        x = np.arange(18, dtype=np.float64).reshape((3, 3, 2))
+        expected = np.zeros_like(x)
+        expected[0, 0, 0] = 1
+        x = xp.asarray(x)
+        expected = xp.asarray(expected)
         xp_assert_close(sinc(x), expected, atol=1e-15)
 
     def test_device(self, xp: ModuleType, device: Device):

tests/test_helpers.py

@@ -40,7 +40,7 @@ def override(func):
 lazy_xp_function(in1d, jax_jit=False)
 
 
-@pytest.mark.skip_xp_backend(Backend.SPARSE, reason="no unique_inverse")
+@pytest.mark.xfail_xp_backend(Backend.SPARSE, reason="no unique_inverse")
 @pytest.mark.skip_xp_backend(Backend.ARRAY_API_STRICTEST, reason="no unique_inverse")
 class TestIn1D:
     # cover both code paths

tests/test_testing.py

@@ -140,7 +140,7 @@ def test_assert_less(self, xp: ModuleType):
             xp_assert_less(xp.asarray([1, 1]), xp.asarray([2, 1]))
 
     @pytest.mark.parametrize("func", [xp_assert_equal, xp_assert_close, xp_assert_less])
-    @pytest.mark.xfail_xp_backend(Backend.SPARSE, reason="index by sparse array")
+    @pytest.mark.skip_xp_backend(Backend.SPARSE, reason="index by sparse array")
     @pytest.mark.skip_xp_backend(Backend.ARRAY_API_STRICTEST, reason="boolean indexing")
     def test_none_shape(self, xp: ModuleType, func: Callable[..., None]):
         """On Dask and other lazy backends, test that a shape with NaN's or None's