TENMAT: Add copy argument to give option to try and optimize views.

pyttb/tenmat.py

@@ -20,12 +20,13 @@ class tenmat:
 
  __slots__ = ("tshape", "rindices", "cindices", "data")
 
- def __init__( # noqa: PLR0912
+ def __init__( # noqa: PLR0912, PLR0913
  self,
  data: Optional[np.ndarray] = None,
  rdims: Optional[np.ndarray] = None,
  cdims: Optional[np.ndarray] = None,
  tshape: Optional[Tuple[int, ...]] = None,
+ copy: bool = True,
  ):
  """
  Construct a :class:`pyttb.tenmat` from explicit components.
@@ -41,6 +42,8 @@ def __init__( # noqa: PLR0912
  Which dimensions of original tensor map to columns.
  tshape:
  Original tensor shape.
+ copy:
+ Whether to make a copy of provided data or just reference it.
 
  Examples
  --------
@@ -158,7 +161,10 @@ def __init__( # noqa: PLR0912
  self.tshape = tshape
  self.rindices = rdims.copy()
  self.cindices = cdims.copy()
- self.data = np.asfortranarray(data.copy())
+ if copy:
+ self.data = data.copy()
+ else:
+ self.data = data
  return
 
  def copy(self) -> tenmat:
@@ -181,19 +187,21 @@ def copy(self) -> tenmat:
  False
  """
  # Create tenmat
- tenmatInstance = tenmat()
- tenmatInstance.tshape = self.tshape
- tenmatInstance.rindices = self.rindices.copy()
- tenmatInstance.cindices = self.cindices.copy()
- tenmatInstance.data = self.data.copy()
- return tenmatInstance
+ return ttb.tenmat(
+ self.data, self.rindices, self.cindices, self.tshape, copy=True
+ )
 
  def __deepcopy__(self, memo):
  return self.copy()
 
- def to_tensor(self) -> ttb.tensor:
+ def to_tensor(self, copy: bool = True) -> ttb.tensor:
  """
- Return copy of :class:`pyttb.tenmat` data as a :class:`pyttb.tensor`.
+ Return :class:`pyttb.tenmat` data as a :class:`pyttb.tensor`.
+
+ Parameters
+ ----------
+ copy:
+ Whether to make a copy of provided data or just reference it.
 
  Examples
  --------
@@ -235,7 +243,10 @@ def to_tensor(self) -> ttb.tensor:
  # it using ipermute.
  shape = self.tshape
  order = np.hstack([self.rindices, self.cindices])
- data = np.reshape(self.data.copy(), np.array(shape)[order], order="F")
+ data = self.data
+ if copy:
+ data = self.data.copy()
+ data = np.reshape(data, np.array(shape)[order], order="F")
  if order.size > 1:
  data = np.transpose(data, np.argsort(order))
  return ttb.tensor(data, shape, copy=False)
@@ -268,10 +279,11 @@ def ctranspose(self) -> tenmat:
  [1. 1.]]
  """
  return tenmat(
- self.data.conj().T.copy(),
- self.cindices.copy(),
- self.rindices.copy(),
+ self.data.conj().T,
+ self.cindices,
+ self.rindices,
  self.tshape,
+ copy=True,
  )
 
  def double(self) -> np.ndarray:
@@ -469,13 +481,13 @@ def __mul__(self, other):
 
  if not tshape:
  return (self.data @ other.data)[0, 0]
- tenmatInstance = tenmat()
- tenmatInstance.tshape = tshape
- tenmatInstance.rindices = np.arange(len(self.rindices))
- tenmatInstance.cindices = np.arange(len(other.cindices)) + len(
- self.rindices
+ tenmatInstance = tenmat(
+ self.data @ other.data,
+ np.arange(len(self.rindices)),
+ np.arange(len(other.cindices)) + len(self.rindices),
+ tshape,
+ copy=False,
  )
- tenmatInstance.data = self.data @ other.data
  return tenmatInstance
  assert False, "tenmat multiplication only valid with scalar or tenmat objects."
 

pyttb/tensor.py

@@ -459,6 +459,7 @@ def to_tenmat(
  cdims_cyclic: Optional[
  Union[Literal["fc"], Literal["bc"], Literal["t"]]
  ] = None,
+ copy: bool = True,
  ) -> ttb.tenmat:
  """
  Construct a :class:`pyttb.tenmat` from a :class:`pyttb.tensor` and
@@ -476,6 +477,8 @@ def to_tenmat(
  in the order range(rdims,self.ndims()) followed by range(0, rdims).
  _bc_ (backward cyclic) range(rdims-1, -1, -1) then
  range(self.ndims(), rdims, -1).
+ copy:
+ Whether to make a copy of provided data or just reference it.
 
  Notes
  -----
@@ -570,7 +573,7 @@ def to_tenmat(
  (rprod, cprod),
  order="F",
  )
- return ttb.tenmat(data, rdims, cdims, tshape=tshape)
+ return ttb.tenmat(data, rdims, cdims, tshape=tshape, copy=copy)
 
  def innerprod(
  self, other: Union[tensor, ttb.sptensor, ttb.ktensor, ttb.ttensor]
@@ -1203,7 +1206,7 @@ def scale(
  vector_self = self.to_tenmat(dims, remdims).double()
  # Numpy broadcasting should be equivalent to bsxfun
  result = vector_self * vector_factor
- return ttb.tenmat(result, dims, remdims, self.shape).to_tensor()
+ return ttb.tenmat(result, dims, remdims, self.shape, copy=False).to_tensor()
 
  def squeeze(self) -> Union[tensor, float]:
  """

tests/test_tenmat.py

@@ -134,6 +134,11 @@ def test_tenmat_initialization_from_data(
  tenmatNdarray2 = ttb.tenmat(ndarrayInstance2, rdims, cdims, tshape)
  assert tenmatNdarray2.isequal(tenmatInstance)
 
+ # Reference instead of copy
+ tenmatNdarray2 = ttb.tenmat(ndarrayInstance2, rdims, cdims, tshape, copy=False)
+ assert tenmatNdarray2.isequal(tenmatInstance)
+ assert np.may_share_memory(ndarrayInstance2, tenmatNdarray2.data)
+
  # Exceptions
 
  ## data is not numpy.ndarray
@@ -279,6 +284,12 @@ def test_tenmat_initialization_from_tensor_type(
  assert tenmatTensorBC.shape == shapeBC
  assert tenmatTensorBC.tshape == tshapeBC
 
+ # Reference only
+ simple_tensor = ttb.tenones((2, 2))
+ rdims = np.array([0])
+ tenmat_reference = simple_tensor.to_tenmat(rdims=rdims, copy=False)
+ assert np.may_share_memory(tenmat_reference.data, simple_tensor.data)
+
  # Exceptions
 
  # cdims_cyclic has incorrect value
@@ -337,6 +348,11 @@ def test_tenmat_to_tensor():
  tenmatInstance4 = tensorInstance4.to_tenmat(np.array([3, 0]))
  tensorTenmatInstance4 = tenmatInstance4.to_tensor()
  assert tensorInstance4.isequal(tensorTenmatInstance4)
+ assert not np.may_share_memory(tensorTenmatInstance4.data, tenmatInstance4.data)
+
+ # Reference instead of copy
+ tensorTenmatInstance4_ref = tenmatInstance4.to_tensor(copy=False)
+ assert np.may_share_memory(tensorTenmatInstance4_ref.data, tenmatInstance4.data)
 
 
 def test_tenmat_ctranspose(sample_tenmat_4way):