fix: handle reordered contents in ak.almost_equal

src/awkward/_nplikes/array_module.py

@@ -5,7 +5,13 @@
 
 import numpy
 
-from awkward._nplikes.numpylike import ArrayLike, IndexType, NumpyLike, NumpyMetadata
+from awkward._nplikes.numpylike import (
+    ArrayLike,
+    IndexType,
+    NumpyLike,
+    NumpyMetadata,
+    UniqueAllResult,
+)
 from awkward._nplikes.shape import ShapeItem, unknown_length
 from awkward._typing import Final, Literal
 
@@ -192,6 +198,31 @@ def unique_values(self, x: ArrayLike) -> ArrayLike:
             equal_nan=False,
         )
 
+    def unique_all(self, x: ArrayLike) -> UniqueAllResult:
+        values, indices, inverse_indices, counts = self._module.unique(
+            x, return_counts=True, return_index=True, return_inverse=True
+        )
+        # np.unique() flattens inverse indices, but they need to share x's shape
+        # See https://github.com/numpy/numpy/issues/20638
+        inverse_indices = inverse_indices.reshape(x.shape)
+        return UniqueAllResult(values, indices, inverse_indices, counts)
+
+    def sort(
+        self,
+        x: ArrayLike,
+        *,
+        axis: int = -1,
+        descending: bool = False,
+        stable: bool = True,
+    ) -> ArrayLike:
+        # Note: this keyword argument is different, and the default is different.
+        kind = "stable" if stable else "quicksort"
+        res = self._module.sort(x, axis=axis, kind=kind)
+        if descending:
+            return self._module.flip(res, axis=axis)
+        else:
+            return res
+
     def concat(
         self,
         arrays: list[ArrayLike] | tuple[ArrayLike, ...],

src/awkward/_nplikes/numpylike.py

@@ -9,6 +9,7 @@
 from awkward._singleton import Singleton
 from awkward._typing import (
     Literal,
+    NamedTuple,
     Protocol,
     Self,
     SupportsIndex,
@@ -19,6 +20,13 @@
 IndexType: TypeAlias = "int | ArrayLike"
 
 
+class UniqueAllResult(NamedTuple):
+    values: ArrayLike
+    indices: ArrayLike
+    inverse_indices: ArrayLike
+    counts: ArrayLike
+
+
 class ArrayLike(Protocol):
     @property
     @abstractmethod
@@ -402,6 +410,21 @@ def where(self, condition: ArrayLike, x1: ArrayLike, x2: ArrayLike) -> ArrayLike
     def unique_values(self, x: ArrayLike) -> ArrayLike:
         ...
 
+    @abstractmethod
+    def unique_all(self, x: ArrayLike) -> UniqueAllResult:
+        ...
+
+    @abstractmethod
+    def sort(
+        self,
+        x: ArrayLike,
+        *,
+        axis: int = -1,
+        descending: bool = False,
+        stable: bool = True,
+    ) -> ArrayLike:
+        ...
+
     @abstractmethod
     def concat(
         self,

src/awkward/_nplikes/typetracer.py

@@ -8,7 +8,13 @@
 
 import awkward as ak
 from awkward._nplikes.dispatch import register_nplike
-from awkward._nplikes.numpylike import ArrayLike, IndexType, NumpyLike, NumpyMetadata
+from awkward._nplikes.numpylike import (
+    ArrayLike,
+    IndexType,
+    NumpyLike,
+    NumpyMetadata,
+    UniqueAllResult,
+)
 from awkward._nplikes.shape import ShapeItem, unknown_length
 from awkward._regularize import is_integer, is_non_string_like_sequence
 from awkward._typing import (
@@ -1081,6 +1087,26 @@ def unique_values(self, x: ArrayLike) -> TypeTracerArray:
         try_touch_data(x)
         return TypeTracerArray._new(x.dtype, shape=(unknown_length,))
 
+    def unique_all(self, x: ArrayLike) -> UniqueAllResult:
+        try_touch_data(x)
+        return UniqueAllResult(
+            TypeTracerArray._new(x.dtype, shape=(unknown_length,)),
+            TypeTracerArray._new(np.int64, shape=(unknown_length,)),
+            TypeTracerArray._new(np.int64, shape=x.shape),
+            TypeTracerArray._new(np.int64, shape=(unknown_length,)),
+        )
+
+    def sort(
+        self,
+        x: ArrayLike,
+        *,
+        axis: int = -1,
+        descending: bool = False,
+        stable: bool = True,
+    ) -> ArrayLike:
+        try_touch_data(x)
+        return TypeTracerArray._new(x.dtype, shape=x.shape)
+
     def concat(self, arrays, *, axis: int | None = 0) -> TypeTracerArray:
         if axis is None:
             assert all(x.ndim == 1 for x in arrays)

src/awkward/operations/ak_almost_equal.py

@@ -105,35 +105,80 @@ def visitor(left, right) -> bool:
         elif left.is_regular:
             return (left.size == right.size) and visitor(left.content, right.content)
         elif left.is_numpy:
-            return (
-                is_approx_dtype(left.dtype, right.dtype)
-                and backend.nplike.all(
-                    backend.nplike.isclose(
-                        left.data, right.data, rtol=rtol, atol=atol, equal_nan=False
+            # Timelike types must be exactly compared, including their units
+            if (
+                np.issubdtype(left.dtype, np.datetime64)
+                or np.issubdtype(right.dtype, np.datetime64)
+                or np.issubdtype(left.dtype, np.timedelta64)
+                or np.issubdtype(right.dtype, np.timedelta64)
+            ):
+                return (
+                    (left.dtype == right.dtype)
+                    and backend.nplike.all(left.data == right.data)
+                    and left.shape == right.shape
+                )
+            else:
+                return (
+                    is_approx_dtype(left.dtype, right.dtype)
+                    and backend.nplike.all(
+                        backend.nplike.isclose(
+                            left.data, right.data, rtol=rtol, atol=atol, equal_nan=False
+                        )
                     )
+                    and left.shape == right.shape
                 )
-                and left.shape == right.shape
-            )
-
         elif left.is_option:
             return backend.index_nplike.array_equal(
                 left.index.data < 0, right.index.data < 0
             ) and visitor(left.project().to_packed(), right.project().to_packed())
         elif left.is_union:
-            return (len(left.contents) == len(right.contents)) and all(
-                visitor(left.project(i).to_packed(), right.project(i).to_packed())
-                for i, _ in enumerate(left.contents)
+            # For two unions with different content orderings to match, the tags should be equal at each index
+            # Therefore, we can order the contents by index appearance
+            def ordered_unique_values(values):
+                # First, find unique values and their appearance (from smallest to largest)
+                # unique_index is in ascending order of `unique` value
+                (
+                    unique,
+                    unique_index,
+                    *_,
+                ) = backend.index_nplike.unique_all(values)
+                # Now re-order `unique` by order of appearance (`unique_index`)
+                return values[backend.index_nplike.sort(unique_index)]
+
+            # Find order of appearance for each union tags, and assume these are one-to-one maps
+            left_tag_order = ordered_unique_values(left.tags.data)
+            right_tag_order = ordered_unique_values(right.tags.data)
+
+            # Create map from left tags to right tags
+            left_tag_to_right_tag = backend.index_nplike.empty(
+                left_tag_order.size, dtype=np.int64
             )
+            left_tag_to_right_tag[left_tag_order] = right_tag_order
+
+            # Map left tags onto right, such that the result should equal right.tags
+            # if the two tag arrays are equivalent
+            new_left_tag = left_tag_to_right_tag[left.tags.data]
+            if not backend.index_nplike.all(new_left_tag == right.tags.data):
+                return False
+
+            # Now project out the contents, and check for equality
+            for i, j in zip(left_tag_order, right_tag_order):
+                if not visitor(
+                    left.project(i).to_packed(), right.project(j).to_packed()
+                ):
+                    return False
+            return True
+
         elif left.is_record:
             return (
                 (
                     get_record_class(left, left_behavior)
                     is get_record_class(right, right_behavior)
                     or not check_parameters
                 )
-                and (left.fields == right.fields)
-                and (left.is_tuple == right.is_tuple)
-                and all(visitor(x, y) for x, y in zip(left.contents, right.contents))
+                and left.is_tuple == right.is_tuple
+                and (left.is_tuple or (len(left.fields) == len(right.fields)))
+                and all(visitor(left.content(f), right.content(f)) for f in left.fields)
             )
         elif left.is_unknown:
             return True

tests/test_2424_almost_equal_union_record.py

@@ -0,0 +1,49 @@
+# BSD 3-Clause License; see https://github.com/scikit-hep/awkward-1.0/blob/main/LICENSE
+
+import numpy as np
+import pytest  # noqa: F401
+
+import awkward as ak
+
+
+def test_records_almost_equal():
+    first = ak.contents.RecordArray(
+        [
+            ak.contents.NumpyArray(np.array([1, 2, 3], dtype=np.int64)),
+            ak.contents.NumpyArray(np.array([0], dtype=np.dtype("<M8[s]"))),
+        ],
+        ["x", "y"],
+    )
+
+    second = ak.contents.RecordArray(
+        [
+            ak.contents.NumpyArray(np.array([0], dtype=np.dtype("<M8[s]"))),
+            ak.contents.NumpyArray(np.array([1, 2, 3], dtype=np.int64)),
+        ],
+        ["y", "x"],
+    )
+
+    assert ak.almost_equal(first, second)
+
+
+def test_unions_almost_equal():
+    # Check unions agree!
+    first = ak.contents.UnionArray(
+        ak.index.Index8([0, 0, 2, 1, 1]),
+        ak.index.Index64([0, 1, 0, 0, 1]),
+        [
+            ak.contents.NumpyArray(np.array([1, 2, 3], dtype=np.int64)),
+            ak.contents.NumpyArray(np.array([0, 1], dtype=np.dtype("<M8[s]"))),
+            ak.contents.NumpyArray(np.array([0, 1, 0, 1], dtype=np.bool_)),
+        ],
+    )
+    second = ak.contents.UnionArray(
+        ak.index.Index8([1, 1, 0, 2, 2]),
+        ak.index.Index64([0, 1, 0, 0, 1]),
+        [
+            ak.contents.NumpyArray(np.array([0, 1, 0, 1], dtype=np.bool_)),
+            ak.contents.NumpyArray(np.array([1, 2, 3], dtype=np.int64)),
+            ak.contents.NumpyArray(np.array([0, 1], dtype=np.dtype("<M8[s]"))),
+        ],
+    )
+    assert ak.almost_equal(first, second)