ENH: Add support for "isin"

Author: OmarManzoor

src/array_api_extra/__init__.py

@@ -6,6 +6,7 @@
     cov,
     expand_dims,
     isclose,
+    isin,
     nan_to_num,
     one_hot,
     pad,
@@ -39,6 +40,7 @@
     "default_dtype",
     "expand_dims",
     "isclose",
+    "isin",
     "kron",
     "lazy_apply",
     "nan_to_num",

src/array_api_extra/_delegation.py

@@ -836,3 +836,62 @@ def argpartition(
     # kth is not small compared to x.size
 
     return _funcs.argpartition(a, kth, axis=axis, xp=xp)
+
+
+def isin(
+    a: Array,
+    b: Array,
+    /,
+    *,
+    assume_unique: bool = False,
+    invert: bool = False,
+    kind: str | None = None,
+    xp: ModuleType | None = None,
+) -> Array:
+    """
+    Determine whether each element in `a` is present in `b`.
+
+    Return a boolean array of the same shape as `a` that is True for elements
+    that are in `b` and False otherwise.
+
+    Parameters
+    ----------
+    a : array_like
+        Input elements.
+    b : array_like
+        The elements against which to test each element of `a`.
+    assume_unique : bool, optional
+        If True, the input arrays are both assumed to be unique which can speed
+        up the calculation. Default: False.
+    invert : bool, optional
+        If True, the values in the returned array are inverted. Default: False.
+    kind : str | None, optional
+        The algorithm or method to use. This will not affect the final result,
+        but will affect the speed and memory use.
+        For Numpy the options are {None, "sort", "table"}.
+        For Jax the mapped parameter is instead `method` and the options are
+        {"compare_all", "binary_search", "sort", and "auto" (default)}
+        For Cupy, Dask, Torch and the default case this parameter is not present and
+        thus ignored. Default: None.
+    xp : array_namespace, optional
+        The standard-compatible namespace for `a` and `b`. Default: infer.
+
+    Returns
+    -------
+    array
+        An array having the same shape as that of `a` that is True for elements
+        that are in `b` and False otherwise.
+    """
+    if xp is None:
+        xp = array_namespace(a, b)
+
+    if is_numpy_namespace(xp):
+        return xp.isin(a, b, assume_unique=assume_unique, invert=invert, kind=kind)
+    if is_jax_namespace(xp):
+        if kind is None:
+            kind = "auto"
+        return xp.isin(a, b, assume_unique=assume_unique, invert=invert, method=kind)
+    if is_cupy_namespace(xp) or is_torch_namespace(xp) or is_dask_namespace(xp):
+        return xp.isin(a, b, assume_unique=assume_unique, invert=invert)
+
+    return _funcs.isin(a, b, assume_unique=assume_unique, invert=invert, xp=xp)

src/array_api_extra/_lib/_funcs.py

@@ -801,3 +801,25 @@ def argpartition(  # numpydoc ignore=PR01,RT01
 ) -> Array:
     """See docstring in `array_api_extra._delegation.py`."""
     return xp.argsort(x, axis=axis, stable=False)
+
+
+def isin( # numpydoc ignore=PR01,RT01
+    a: Array,
+    b: Array,
+    /,
+    *,
+    assume_unique: bool = False,
+    invert: bool = False,
+    xp: ModuleType | None = None,
+) -> Array:
+    """See docstring in `array_api_extra._delegation.py`."""
+    if xp is None:
+        xp = array_namespace(a, b)
+
+    original_a_shape = a.shape
+    a = xp.reshape(a, (-1,))
+    b = xp.reshape(b, (-1,))
+    return xp.reshape(
+        _helpers.in1d(a, b, assume_unique=assume_unique, invert=invert, xp=xp),
+        original_a_shape,
+    )

tests/test_funcs.py

@@ -22,6 +22,7 @@
     default_dtype,
     expand_dims,
     isclose,
+    isin,
     kron,
     nan_to_num,
     nunique,
@@ -888,7 +889,7 @@ def test_device(self, xp: ModuleType, device: Device, equal_nan: bool):
         b = xp.asarray([1e-9, 1e-4, xp.nan], device=device)
         res = isclose(a, b, equal_nan=equal_nan)
         assert get_device(res) == device
-        
+
     def test_array_on_device_with_scalar(self, xp: ModuleType, device: Device):
         a = xp.asarray([0.01, 0.5, 0.8, 0.9, 1.00001], device=device)
         b = 1
@@ -1476,3 +1477,13 @@ def test_nd(self, xp: ModuleType, ndim: int):
     @override
     def test_input_validation(self, xp: ModuleType):
         self._test_input_validation(xp)
+
+
+@pytest.mark.xfail_xp_backend(Backend.SPARSE, reason="no unique_inverse")
+class TestIsIn:
+    def test_simple(self, xp: ModuleType):
+        a = xp.asarray([[0, 2], [4, 6]])
+        b = xp.asarray([1, 2, 3, 4])
+        expected = xp.asarray([[False, True], [True, False]])
+        res = isin(a, b)
+        xp_assert_equal(res, expected)