Merge pull request #1008 from helmholtz-analytics/api/1006-rename-kee…

…pdim-keepdims

API: Rename `keepdim` kwarg to `keepdims`

heat/cluster/_kcluster.py

@@ -204,7 +204,7 @@ def _assign_to_cluster(self, x: DNDarray):
         """
         # calculate the distance matrix and determine the closest centroid
         distances = self._metric(x, self._cluster_centers)
-        matching_centroids = distances.argmin(axis=1, keepdim=True)
+        matching_centroids = distances.argmin(axis=1, keepdims=True)
 
         return matching_centroids
 

heat/cluster/kmeans.py

@@ -88,13 +88,13 @@ def _update_centroids(self, x: DNDarray, matching_centroids: DNDarray):
             selection = (matching_centroids == i).astype(ht.int64)
             # accumulate points and total number of points in cluster
             assigned_points = x * selection
-            points_in_cluster = selection.sum(axis=0, keepdim=True).clip(
+            points_in_cluster = selection.sum(axis=0, keepdims=True).clip(
                 1.0, ht.iinfo(ht.int64).max
             )
 
             # compute the new centroids
             new_cluster_centers[i : i + 1, :] = (assigned_points / points_in_cluster).sum(
-                axis=0, keepdim=True
+                axis=0, keepdims=True
             )
 
         return new_cluster_centers

heat/cluster/kmedians.py

@@ -95,7 +95,7 @@ def _update_centroids(self, x: DNDarray, matching_centroids: DNDarray):
             else:
                 if clean.shape[0] <= ht.MPI_WORLD.size:
                     clean.resplit_(axis=None)
-                median = ht.median(clean, axis=0, keepdim=True)
+                median = ht.median(clean, axis=0, keepdims=True)
                 new_cluster_centers[i : i + 1, :] = median
 
         return new_cluster_centers

heat/cluster/kmedoids.py

@@ -94,11 +94,11 @@ def _update_centroids(self, x: DNDarray, matching_centroids: DNDarray):
             else:
                 if clean.shape[0] <= ht.MPI_WORLD.size:
                     clean.resplit_(axis=None)
-                median = ht.median(clean, axis=0, keepdim=True)
+                median = ht.median(clean, axis=0, keepdims=True)
 
                 dist = self._metric(x, median)
                 _, displ, _ = x.comm.counts_displs_shape(shape=x.shape, axis=0)
-                idx = dist.argmin(axis=0, keepdim=False).item()
+                idx = dist.argmin(axis=0, keepdims=False).item()
                 proc = 0
                 for p in range(x.comm.size):
                     if displ[p] > idx:

heat/core/_operations.py

@@ -414,7 +414,7 @@ def __reduce_op(
     axis = stride_tricks.sanitize_axis(x.shape, kwargs.get("axis"))
     if isinstance(axis, int):
         axis = (axis,)
-    keepdim = kwargs.get("keepdim")
+    keepdims = kwargs.get("keepdims")
     out = kwargs.get("out")
     split = x.split
     balanced = x.balanced
@@ -446,7 +446,7 @@ def __reduce_op(
             ):  # no neutral element for max/min
                 partial = partial_op(partial, dim=dim, keepdim=True)
             output_shape = output_shape[:dim] + (1,) + output_shape[dim + 1 :]
-        if not keepdim and not len(partial.shape) == 1:
+        if not keepdims and not len(partial.shape) == 1:
             gshape_losedim = tuple(x.gshape[dim] for dim in range(len(x.gshape)) if dim not in axis)
             lshape_losedim = tuple(x.lshape[dim] for dim in range(len(x.lshape)) if dim not in axis)
             output_shape = gshape_losedim
@@ -464,7 +464,7 @@ def __reduce_op(
             balanced = True
             if x.comm.is_distributed():
                 x.comm.Allreduce(MPI.IN_PLACE, partial, reduction_op)
-        elif axis is not None and not keepdim:
+        elif axis is not None and not keepdims:
             down_dims = len(tuple(dim for dim in axis if dim < x.split))
             split -= down_dims
             balanced = x.balanced

heat/core/arithmetics.py

@@ -871,7 +871,7 @@ def prod(
     a: DNDarray,
     axis: Union[int, Tuple[int, ...]] = None,
     out: DNDarray = None,
-    keepdim: bool = None,
+    keepdims: bool = None,
 ) -> DNDarray:
     """
     Return the product of array elements over a given axis in form of a DNDarray shaped as a but with the specified axis removed.
@@ -888,7 +888,7 @@ def prod(
     out : DNDarray, optional
         Alternative output array in which to place the result. It must have the same shape as the expected output, but
         the datatype of the output values will be cast if necessary.
-    keepdim : bool, optional
+    keepdims : bool, optional
         If this is set to ``True``, the axes which are reduced are left in the result as dimensions with size one. With this
         option, the result will broadcast correctly against the input array.
 
@@ -907,11 +907,11 @@ def prod(
     DNDarray([ 2., 12.], dtype=ht.float32, device=cpu:0, split=None)
     """
     return _operations.__reduce_op(
-        a, torch.prod, MPI.PROD, axis=axis, out=out, neutral=1, keepdim=keepdim
+        a, torch.prod, MPI.PROD, axis=axis, out=out, neutral=1, keepdims=keepdims
     )
 
 
-DNDarray.prod = lambda self, axis=None, out=None, keepdim=None: prod(self, axis, out, keepdim)
+DNDarray.prod = lambda self, axis=None, out=None, keepdims=None: prod(self, axis, out, keepdims)
 DNDarray.prod.__doc__ = prod.__doc__
 
 
@@ -961,7 +961,7 @@ def sum(
     a: DNDarray,
     axis: Union[int, Tuple[int, ...]] = None,
     out: DNDarray = None,
-    keepdim: bool = None,
+    keepdims: bool = None,
 ) -> DNDarray:
     """
     Sum of array elements over a given axis. An array with the same shape as ``self.__array`` except for the specified
@@ -978,7 +978,7 @@ def sum(
     out : DNDarray, optional
         Alternative output array in which to place the result. It must have the same shape as the expected output, but
         the datatype of the output values will be cast if necessary.
-    keepdim : bool, optional
+    keepdims : bool, optional
         If this is set to ``True``, the axes which are reduced are left in the result as dimensions with size one. With this
         option, the result will broadcast correctly against the input array.
 
@@ -996,8 +996,8 @@ def sum(
     """
     # TODO: make me more numpy API complete Issue #101
     return _operations.__reduce_op(
-        a, torch.sum, MPI.SUM, axis=axis, out=out, neutral=0, keepdim=keepdim
+        a, torch.sum, MPI.SUM, axis=axis, out=out, neutral=0, keepdims=keepdims
     )
 
 
-DNDarray.sum = lambda self, axis=None, out=None, keepdim=None: sum(self, axis, out, keepdim)
+DNDarray.sum = lambda self, axis=None, out=None, keepdims=None: sum(self, axis, out, keepdims)

heat/core/linalg/basics.py

@@ -1185,17 +1185,17 @@ def matrix_norm(
         if col_axis > row_axis and not keepdims:
             col_axis -= 1
         return statistics.max(
-            arithmetics.sum(rounding.abs(x), axis=row_axis, keepdim=keepdims),
+            arithmetics.sum(rounding.abs(x), axis=row_axis, keepdims=keepdims),
             axis=col_axis,
-            keepdim=keepdims,
+            keepdims=keepdims,
         )
     elif ord == -1:
         if col_axis > row_axis and not keepdims:
             col_axis -= 1
         return statistics.min(
-            arithmetics.sum(rounding.abs(x), axis=row_axis, keepdim=keepdims),
+            arithmetics.sum(rounding.abs(x), axis=row_axis, keepdims=keepdims),
             axis=col_axis,
-            keepdim=keepdims,
+            keepdims=keepdims,
         )
     elif ord == 2:
         raise NotImplementedError("The largest singular value can't be computed yet.")
@@ -1205,21 +1205,21 @@ def matrix_norm(
         if row_axis > col_axis and not keepdims:
             row_axis -= 1
         return statistics.max(
-            arithmetics.sum(rounding.abs(x), axis=col_axis, keepdim=keepdims),
+            arithmetics.sum(rounding.abs(x), axis=col_axis, keepdims=keepdims),
             axis=row_axis,
-            keepdim=keepdims,
+            keepdims=keepdims,
         )
     elif ord == -constants.inf:
         if row_axis > col_axis and not keepdims:
             row_axis -= 1
         return statistics.min(
-            arithmetics.sum(rounding.abs(x), axis=col_axis, keepdim=keepdims),
+            arithmetics.sum(rounding.abs(x), axis=col_axis, keepdims=keepdims),
             axis=row_axis,
-            keepdim=keepdims,
+            keepdims=keepdims,
         )
     elif ord in [None, "fro"]:
         return exponential.sqrt(
-            arithmetics.sum((complex_math.conj(x) * x).real, axis=axis, keepdim=keepdims)
+            arithmetics.sum((complex_math.conj(x) * x).real, axis=axis, keepdims=keepdims)
         )
     elif ord == "nuc":
         raise NotImplementedError("The nuclear norm can't be computed yet.")
@@ -2277,7 +2277,7 @@ def vdot(x1: DNDarray, x2: DNDarray) -> DNDarray:
 
 
 def vecdot(
-    x1: DNDarray, x2: DNDarray, axis: Optional[int] = None, keepdim: Optional[bool] = None
+    x1: DNDarray, x2: DNDarray, axis: Optional[int] = None, keepdims: Optional[bool] = None
 ) -> DNDarray:
     """
     Computes the (vector) dot product of two DNDarrays.
@@ -2290,7 +2290,7 @@ def vecdot(
         second input array. Must be compatible with x1.
     axis : int, optional
         axis over which to compute the dot product. The last dimension is used if 'None'.
-    keepdim : bool, optional
+    keepdims : bool, optional
         If this is set to 'True', the axes which are reduced are left in the result as dimensions with size one.
 
     See Also
@@ -2310,7 +2310,7 @@ def vecdot(
     if axis is None:
         axis = m.ndim - 1
 
-    return arithmetics.sum(m, axis=axis, keepdim=keepdim)
+    return arithmetics.sum(m, axis=axis, keepdims=keepdims)
 
 
 def vector_norm(
@@ -2386,20 +2386,20 @@ def vector_norm(
             raise TypeError("'axis' must be an integer or 1-tuple for vectors.")
 
     if ord == constants.INF:
-        return statistics.max(rounding.abs(x), axis=axis, keepdim=keepdims)
+        return statistics.max(rounding.abs(x), axis=axis, keepdims=keepdims)
     elif ord == -constants.INF:
-        return statistics.min(rounding.abs(x), axis=axis, keepdim=keepdims)
+        return statistics.min(rounding.abs(x), axis=axis, keepdims=keepdims)
     elif ord == 0:
-        return arithmetics.sum(x != 0, axis=axis, keepdim=keepdims).astype(types.float)
+        return arithmetics.sum(x != 0, axis=axis, keepdims=keepdims).astype(types.float)
     elif ord == 1:
-        return arithmetics.sum(rounding.abs(x), axis=axis, keepdim=keepdims)
+        return arithmetics.sum(rounding.abs(x), axis=axis, keepdims=keepdims)
     elif ord is None or ord == 2:
         s = (complex_math.conj(x) * x).real
-        return exponential.sqrt(arithmetics.sum(s, axis=axis, keepdim=keepdims))
+        return exponential.sqrt(arithmetics.sum(s, axis=axis, keepdims=keepdims))
     elif isinstance(ord, str):
         raise ValueError("Norm order {} is invalid for vectors".format(ord))
     else:
         ret = arithmetics.pow(rounding.abs(x), ord)
-        ret = arithmetics.sum(ret, axis=axis, keepdim=keepdims)
+        ret = arithmetics.sum(ret, axis=axis, keepdims=keepdims)
         ret = arithmetics.pow(ret, 1.0 / ord)
         return ret

heat/core/linalg/tests/test_basics.py

@@ -2071,7 +2071,7 @@ def test_vecdot(self):
         a = ht.full((4, 4), 2, split=0)
         b = ht.ones(4)
 
-        c = ht.linalg.vecdot(a, b, axis=0, keepdim=True)
+        c = ht.linalg.vecdot(a, b, axis=0, keepdims=True)
         self.assertEqual(c.dtype, ht.float32)
         self.assertEqual(c.device, a.device)
         self.assertTrue(ht.equal(c, ht.array([[8, 8, 8, 8]])))
@@ -2118,7 +2118,7 @@ def test_vector_norm(self):
         self.assertEqual(vn.device, b0.device)
         self.assertTrue(ht.allclose(vn, ht.array([5.38516481, 1.41421356, 5.38516481], split=0)))
 
-        # split matrix axis keepdim norm 3
+        # split matrix axis keepdims norm 3
         vn = ht.vector_norm(b1, axis=1, keepdims=True, ord=3)
         self.assertEqual(vn.split, None)
         self.assertEqual(vn.dtype, b1.dtype)

heat/core/logical.py

@@ -39,7 +39,7 @@ def all(
     x: DNDarray,
     axis: Union[int, Tuple[int], None] = None,
     out: Optional[DNDarray] = None,
-    keepdim: bool = False,
+    keepdims: bool = False,
 ) -> Union[DNDarray, bool]:
     """
     Test whether all array elements along a given axis evaluate to ``True``.
@@ -57,7 +57,7 @@ def all(
     out : DNDarray, optional
         Alternate output array in which to place the result. It must have the same shape as the expected output
         and its type is preserved.
-    keepdim : bool, optional
+    keepdims : bool, optional
         If this is set to ``True``, the axes which are reduced are left in the result as dimensions with size one.
         With this option, the result will broadcast correctly against the original array.
 
@@ -91,17 +91,17 @@ def all(
     def local_all(t, *args, **kwargs):
         return torch.all(t != 0, *args, **kwargs)
 
-    if keepdim and axis is None:
+    if keepdims and axis is None:
         axis = tuple(range(x.ndim))
 
     return _operations.__reduce_op(
-        x, local_all, MPI.LAND, axis=axis, out=out, neutral=1, keepdim=keepdim
+        x, local_all, MPI.LAND, axis=axis, out=out, neutral=1, keepdims=keepdims
     )
 
 
 DNDarray.all: Callable[
     [Union[int, Tuple[int], None], Optional[DNDarray], bool], Union[DNDarray, bool]
-] = lambda self, axis=None, out=None, keepdim=False: all(self, axis, out, keepdim)
+] = lambda self, axis=None, out=None, keepdims=False: all(self, axis, out, keepdims)
 DNDarray.all.__doc__ = all.__doc__
 
 
@@ -170,7 +170,7 @@ def allclose(
 
 
 def any(
-    x, axis: Optional[int] = None, out: Optional[DNDarray] = None, keepdim: bool = False
+    x, axis: Optional[int] = None, out: Optional[DNDarray] = None, keepdims: bool = False
 ) -> DNDarray:
     """
     Returns a :class:`~heat.core.dndarray.DNDarray` containing the result of the test whether any array elements along a
@@ -187,7 +187,7 @@ def any(
     out : DNDarray, optional
         Alternative output tensor in which to place the result. It must have the same shape as the expected output.
         The output is a array with ``datatype=bool``.
-    keepdim : bool, optional
+    keepdims : bool, optional
         If this is set to ``True``, the axes which are reduced are left in the result as dimensions with size one.
         With this option, the result will broadcast correctly against the original array.
 
@@ -211,17 +211,17 @@ def any(
     def local_any(t, *args, **kwargs):
         return torch.any(t != 0, *args, **kwargs)
 
-    if keepdim and axis is None:
+    if keepdims and axis is None:
         axis = tuple(range(x.ndim))
 
     return _operations.__reduce_op(
-        x, local_any, MPI.LOR, axis=axis, out=out, neutral=0, keepdim=keepdim
+        x, local_any, MPI.LOR, axis=axis, out=out, neutral=0, keepdims=keepdims
     )
 
 
 DNDarray.any: Callable[
     [DNDarray, Optional[int], Optional[DNDarray], bool], DNDarray
-] = lambda self, axis=None, out=None, keepdim=False: any(self, axis, out, keepdim)
+] = lambda self, axis=None, out=None, keepdims=False: any(self, axis, out, keepdims)
 DNDarray.any.__doc__ = any.__doc__