Khatrirao cleanup

pyttb/cp_apr.py

@@ -1171,8 +1171,10 @@ def tt_calcpi_prowsubprob(
             Pi *= Model[i][Data.subs[sparse_indices, i], :]
     else:
         Pi = ttb.khatrirao(
-            Model.factor_matrices[:factorIndex]
-            + Model.factor_matrices[factorIndex + 1 : ndims + 1],
+            *(
+                Model.factor_matrices[:factorIndex]
+                + Model.factor_matrices[factorIndex + 1 : ndims + 1]
+            ),
             reverse=True,
         )
 
@@ -1660,8 +1662,10 @@ def calculatePi(Data, Model, rank, factorIndex, ndims):
             Pi *= Model[i][Data.subs[:, i], :]
     else:
         Pi = ttb.khatrirao(
-            Model.factor_matrices[:factorIndex]
-            + Model.factor_matrices[factorIndex + 1 :],
+            *(
+                Model.factor_matrices[:factorIndex]
+                + Model.factor_matrices[factorIndex + 1 :]
+            ),
             reverse=True,
         )
 

pyttb/khatrirao.py

@@ -1,11 +1,11 @@
 # Copyright 2022 National Technology & Engineering Solutions of Sandia,
 # LLC (NTESS). Under the terms of Contract DE-NA0003525 with NTESS, the
 # U.S. Government retains certain rights in this software.
-
+"""Khatri-Rao Product Implementation"""
 import numpy as np
 
 
-def khatrirao(*listOfMatrices, reverse=False):
+def khatrirao(*matrices: np.ndarray, reverse: bool = False) -> np.ndarray:
     """
     KHATRIRAO Khatri-Rao product of matrices.
 
@@ -16,69 +16,45 @@ def khatrirao(*listOfMatrices, reverse=False):
 
     Parameters
     ----------
-    Matrices: [:class:`numpy.ndarray`] or :class:`numpy.ndarray`,:class:`numpy.ndarray`...
+    matrices: Collection of matrices to take the product of
     reverse: bool Set to true to calculate product in reverse
 
-    Returns
-    -------
-    product: float
-
     Examples
     --------
     >>> A = np.random.normal(size=(5,2))
     >>> B = np.random.normal(size=(5,2))
     >>> _ = khatrirao(A,B) #<-- Khatri-Rao of A and B
     >>> _ = khatrirao(B,A,reverse=True) #<-- same thing as above
-    >>> _ = khatrirao([A,A,B]) #<-- passing a list
-    >>> _ = khatrirao([B,A,A],reverse = True) #<-- same as above
+    >>> _ = khatrirao(A,A,B) #<-- passing multiple items
+    >>> _ = khatrirao(B,A,A,reverse = True) #<-- same as above
+    >>> _ = khatrirao(*[A,A,B]) #<-- passing a list via unpacking items
     """
     # Determine if list of matrices of multiple matrix arguments
-    if isinstance(listOfMatrices[0], list):
-        if len(listOfMatrices) == 1:
-            listOfMatrices = listOfMatrices[0]
-        else:
-            assert (
-                False
-            ), "Khatri Rao Acts on multiple Array arguments or a list of Arrays"
+    if len(matrices) == 1 and isinstance(matrices[0], list):
+        raise ValueError(
+            "Khatrirao interface has changed. Instead of "
+            " `khatrirao([matrix_a, matrix_b])` please update to use argument "
+            "unpacking `khatrirao(*[matrix_a, matrix_b])`. This reduces ambiguity "
+            "in usage moving forward. "
+        )
+
+    if not isinstance(reverse, bool):
+        raise ValueError(f"Expected a bool for reverse but received {reverse}")
 
     # Error checking on input and set matrix order
-    if reverse == True:
-        listOfMatrices = list(reversed(listOfMatrices))
-    ndimsA = [len(matrix.shape) == 2 for matrix in listOfMatrices]
-    if not np.all(ndimsA):
+    if reverse is True:
+        matrices = tuple(reversed(matrices))
+    if not all(len(matrix.shape) == 2 for matrix in matrices):
         assert False, "Each argument must be a matrix"
 
-    ncolFirst = listOfMatrices[0].shape[1]
-    ncols = [matrix.shape[1] == ncolFirst for matrix in listOfMatrices]
-    if not np.all(ncols):
+    ncolFirst = matrices[0].shape[1]
+    if not all(matrix.shape[1] == ncolFirst for matrix in matrices):
         assert False, "All matrices must have the same number of columns."
 
     # Computation
-    # print(f'A =\n {listOfMatrices}')
-    P = listOfMatrices[0]
-    # print(f'size_P = \n{P.shape}')
-    # print(f'P = \n{P}')
-    if ncolFirst == 1:
-        for i in listOfMatrices[1:]:
-            # print(f'size_Ai = \n{i.shape}')
-            # print(f'size_reshape_Ai = \n{np.reshape(i, newshape=(-1, ncolFirst)).shape}')
-            # print(f'size_P = \n{P.shape}')
-            # print(f'size_reshape_P = \n{np.reshape(P, newshape=(ncolFirst, -1)).shape}')
-            P = np.reshape(i, newshape=(-1, ncolFirst)) * np.reshape(
-                P, newshape=(ncolFirst, -1), order="F"
-            )
-            # print(f'size_P = \n{P.shape}')
-            # print(f'P = \n{P}')
-    else:
-        for i in listOfMatrices[1:]:
-            # print(f'size_Ai = \n{i.shape}')
-            # print(f'size_reshape_Ai = \n{np.reshape(i, newshape=(-1, 1, ncolFirst)).shape}')
-            # print(f'size_P = \n{P.shape}')
-            # print(f'size_reshape_P = \n{np.reshape(P, newshape=(1, -1, ncolFirst)).shape}')
-            P = np.reshape(i, newshape=(-1, 1, ncolFirst)) * np.reshape(
-                P, newshape=(1, -1, ncolFirst), order="F"
-            )
-            # print(f'size_P = \n{P.shape}')
-            # print(f'P = \n{P}')
-
+    P = matrices[0]
+    for i in matrices[1:]:
+        P = np.reshape(i, newshape=(-1, 1, ncolFirst)) * np.reshape(
+            P, newshape=(1, -1, ncolFirst), order="F"
+        )
     return np.reshape(P, newshape=(-1, ncolFirst), order="F")

pyttb/ktensor.py

@@ -990,7 +990,7 @@ def full(self):
          [63. 85.]]
         <BLANKLINE>
         """
-        data = self.weights @ ttb.khatrirao(self.factor_matrices, reverse=True).T
+        data = self.weights @ ttb.khatrirao(*self.factor_matrices, reverse=True).T
         return ttb.tensor.from_data(data, self.shape)
 
     def innerprod(self, other):

pyttb/sptensor.py

@@ -348,7 +348,7 @@ def allsubs(self) -> np.ndarray:
         for n in range(0, self.ndims):
             i = o.copy()
             i[n] = np.expand_dims(np.arange(0, self.shape[n]), axis=1)
-            s[:, n] = np.squeeze(ttb.khatrirao(i))
+            s[:, n] = np.squeeze(ttb.khatrirao(*i))
 
         return s.astype(int)
 
@@ -1723,7 +1723,7 @@ def _set_subtensor(self, key, value):
                     i[n] = np.array(keyCopy[n])[:, None]
                 else:
                     i[n] = np.array(keyCopy[n], ndmin=2)
-                addsubs[:, n] = ttb.khatrirao(i).transpose()[:]
+                addsubs[:, n] = ttb.khatrirao(*i).transpose()[:]
 
             if self.subs.size > 0:
                 # Replace existing values

pyttb/tensor.py

@@ -702,16 +702,18 @@ def mttkrp(self, U: Union[ttb.ktensor, List[np.ndarray]], n: int) -> np.ndarray:
         szn = self.shape[n]
 
         if n == 0:
-            Ur = ttb.khatrirao(U[1 : self.ndims], reverse=True)
+            Ur = ttb.khatrirao(*U[1 : self.ndims], reverse=True)
             Y = np.reshape(self.data, (szn, szr), order="F")
             return Y @ Ur
         if n == self.ndims - 1:  # pylint: disable=no-else-return
-            Ul = ttb.khatrirao(U[0 : self.ndims - 1], reverse=True)
+            Ul = ttb.khatrirao(*U[0 : self.ndims - 1], reverse=True)
             Y = np.reshape(self.data, (szl, szn), order="F")
             return Y.T @ Ul
         else:
-            Ul = ttb.khatrirao(U[n + 1 :], reverse=True)
-            Ur = np.reshape(ttb.khatrirao(U[0:n], reverse=True), (szl, 1, R), order="F")
+            Ul = ttb.khatrirao(*U[n + 1 :], reverse=True)
+            Ur = np.reshape(
+                ttb.khatrirao(*U[0:n], reverse=True), (szl, 1, R), order="F"
+            )
             Y = np.reshape(self.data, (-1, szr), order="F")
             Y = Y @ Ul
             Y = np.reshape(Y, (szl, szn, R), order="F")

tests/test_khatrirao.py

@@ -24,8 +24,8 @@ def test_khatrirao():
             [64, 125, 216],
         ]
     )
-    assert (ttb.khatrirao([A, A, A]) == answer).all()
-    assert (ttb.khatrirao([A, A, A], reverse=True) == answer).all()
+    assert (ttb.khatrirao(*[A, A, A]) == answer).all()
+    assert (ttb.khatrirao(*[A, A, A], reverse=True) == answer).all()
     assert (ttb.khatrirao(A, A, A) == answer).all()
 
     # Test case where inputs are column vectors
@@ -40,19 +40,20 @@ def test_khatrirao():
             a_2[3, 0] * np.ones((16, 1)),
         )
     )
-    assert (ttb.khatrirao([a_2, a_1, a_1]) == result).all()
+    assert (ttb.khatrirao(*[a_2, a_1, a_1]) == result).all()
     assert (ttb.khatrirao(a_2, a_1, a_1) == result).all()
 
-    with pytest.raises(AssertionError) as excinfo:
-        ttb.khatrirao([a_2, a_1, a_1], a_2)
-    assert "Khatri Rao Acts on multiple Array arguments or a list of Arrays" in str(
-        excinfo
-    )
-
     with pytest.raises(AssertionError) as excinfo:
         ttb.khatrirao(a_2, a_1, np.ones((2, 2, 2)))
     assert "Each argument must be a matrix" in str(excinfo)
 
     with pytest.raises(AssertionError) as excinfo:
         ttb.khatrirao(a_2, a_1, a_3)
     assert "All matrices must have the same number of columns." in str(excinfo)
+
+    # Check old interface error
+    with pytest.raises(ValueError):
+        ttb.khatrirao([a_1, a_1, a_1])
+
+    with pytest.raises(ValueError):
+        ttb.khatrirao(a_1, a_1, reverse="cat")

tests/test_package.py

@@ -27,6 +27,7 @@ def test_linting():
         os.path.join(os.path.dirname(ttb.__file__), f"{ttb.tensor.__name__}.py"),
         os.path.join(os.path.dirname(ttb.__file__), f"{ttb.sptensor.__name__}.py"),
         ttb.pyttb_utils.__file__,
+        os.path.join(os.path.dirname(ttb.__file__), f"{ttb.khatrirao.__name__}.py"),
     ]
     # TODO pylint fails to import pyttb in tests
     # add mypy check