initial work with a small test; fails for sparse backend as it has not argsort;

Author: cakedev0

src/array_api_extra/__init__.py

@@ -1,6 +1,13 @@
 """Extra array functions built on top of the array API standard."""
 
-from ._delegation import isclose, nan_to_num, one_hot, pad
+from ._delegation import (
+    argpartition,
+    isclose,
+    nan_to_num,
+    one_hot,
+    pad,
+    partition,
+)
 from ._lib._at import at
 from ._lib._funcs import (
     apply_where,
@@ -23,6 +30,7 @@
 __all__ = [
     "__version__",
     "apply_where",
+    "argpartition",
     "at",
     "atleast_nd",
     "broadcast_shapes",
@@ -37,6 +45,7 @@
     "nunique",
     "one_hot",
     "pad",
+    "partition",
     "setdiff1d",
     "sinc",
 ]

src/array_api_extra/_delegation.py

@@ -326,3 +326,110 @@ def pad(
         return xp.nn.functional.pad(x, tuple(pad_width), value=constant_values)  # type: ignore[arg-type]  # pyright: ignore[reportArgumentType]
 
     return _funcs.pad(x, pad_width, constant_values=constant_values, xp=xp)
+
+
+def partition(
+    a: Array,
+    kth: int,
+    *,
+    xp: ModuleType | None = None,
+) -> Array:
+    """
+    Return a partitioned copy of an array.
+
+    Parameters
+    ----------
+    a : 1-dimensional array
+        Input array.
+    kth : int
+        Element index to partition by.
+    xp : array_namespace, optional
+        The standard-compatible namespace for `x`. Default: infer.
+
+    Returns
+    -------
+    partitioned_array
+        Array of the same type and shape as a.
+    """
+    # Validate inputs.
+    if xp is None:
+        xp = array_namespace(a)
+    if a.ndim != 1:
+        msg = "only 1-dimensional arrays are currently supported"
+        raise NotImplementedError(msg)
+
+    # Delegate where possible.
+    if is_numpy_namespace(xp) or is_cupy_namespace(xp):
+        return xp.partition(a, kth)
+    if is_jax_namespace(xp):
+        from jax import numpy
+
+        return numpy.partition(a, kth)
+
+    # Use top-k when possible:
+    if is_torch_namespace(xp):
+        from torch import topk
+
+        a_left, indices_left = topk(a, kth, largest=False, sorted=False)
+        mask_right = xp.ones(a.shape, dtype=bool)
+        mask_right[indices_left] = False
+        return xp.concat((a_left, a[mask_right]))
+    # Note: dask topk/argtopk sort the return values, so it's
+    # not much more efficient than sorting everything when
+    # kth is not small compared to x.size
+
+    return _funcs.partition(a, kth, xp=xp)
+
+
+def argpartition(
+    a: Array,
+    kth: int,
+    *,
+    xp: ModuleType | None = None,
+) -> Array:
+    """
+    Perform an indirect partition along the given axis.
+
+    Parameters
+    ----------
+    a : 1-dimensional array
+        Input array.
+    kth : int
+        Element index to partition by.
+    xp : array_namespace, optional
+        The standard-compatible namespace for `x`. Default: infer.
+
+    Returns
+    -------
+    index_array
+        Array of indices that partition `a` along the specified axis.
+    """
+    # Validate inputs.
+    if xp is None:
+        xp = array_namespace(a)
+    if a.ndim != 1:
+        msg = "only 1-dimensional arrays are currently supported"
+        raise NotImplementedError(msg)
+
+    # Delegate where possible.
+    if is_numpy_namespace(xp) or is_cupy_namespace(xp):
+        return xp.argpartition(a, kth)
+    if is_jax_namespace(xp):
+        from jax import numpy
+
+        return numpy.argpartition(a, kth)
+
+    # Use top-k when possible:
+    if is_torch_namespace(xp):
+        from torch import topk
+
+        _, indices = topk(a, kth, largest=False, sorted=False)
+        mask = xp.ones(a.shape, dtype=bool)
+        mask[indices] = False
+        indices_above = xp.arange(a.shape[0])[mask]
+        return xp.concat((indices, indices_above))
+    # Note: dask topk/argtopk sort the return values, so it's
+    # not much more efficient than sorting everything when
+    # kth is not small compared to x.size
+
+    return _funcs.argpartition(a, kth, xp=xp)

src/array_api_extra/_lib/_funcs.py

@@ -1029,3 +1029,23 @@ def sinc(x: Array, /, *, xp: ModuleType | None = None) -> Array:
         xp.asarray(xp.finfo(x.dtype).eps, dtype=x.dtype, device=_compat.device(x)),
     )
     return xp.sin(y) / y
+
+
+def partition(  # numpydoc ignore=PR01,RT01
+    x: Array,
+    kth: int,  # noqa: ARG001
+    *,
+    xp: ModuleType,
+) -> Array:
+    """See docstring in `array_api_extra._delegation.py`."""
+    return xp.sort(x, stable=False)
+
+
+def argpartition(  # numpydoc ignore=PR01,RT01
+    x: Array,
+    kth: int,  # noqa: ARG001
+    *,
+    xp: ModuleType,
+) -> Array:
+    """See docstring in `array_api_extra._delegation.py`."""
+    return xp.argsort(x, stable=False)

tests/test_funcs.py

@@ -12,6 +12,7 @@
 
 from array_api_extra import (
     apply_where,
+    argpartition,
     at,
     atleast_nd,
     broadcast_shapes,
@@ -25,6 +26,7 @@
     nunique,
     one_hot,
     pad,
+    partition,
     setdiff1d,
     sinc,
 )
@@ -1298,3 +1300,18 @@ def test_device(self, xp: ModuleType, device: Device):
 
     def test_xp(self, xp: ModuleType):
         xp_assert_equal(sinc(xp.asarray(0.0), xp=xp), xp.asarray(1.0))
+
+
+class TestPartition:
+    def test_basic(self, xp: ModuleType):
+        # Using 0-dimensional array
+        rng = np.random.default_rng(2847)
+
+        for _ in range(100):
+            n = rng.integers(1, 1000)
+            x = xp.asarray(rng.random(size=n))
+            k = int(rng.integers(1, n - 1))
+            y = partition(x, k)
+            assert xp.max(y[:k]) <= xp.min(y[k:])
+            y = x[argpartition(x, k)]
+            assert xp.max(y[:k]) <= xp.min(y[k:])