# Source code for tensorly.cp_tensor

```
"""
Core operations on CP tensors.
"""
from . import backend as T
from .base import fold, tensor_to_vec
from ._factorized_tensor import FactorizedTensor
from .tenalg import khatri_rao, unfolding_dot_khatri_rao
from .metrics.factors import congruence_coefficient
import numpy as np
# Author: Jean Kossaifi
# License: BSD 3 clause
class CPTensor(FactorizedTensor):
def __init__(self, cp_tensor):
super().__init__()
shape, rank = _validate_cp_tensor(cp_tensor)
weights, factors = cp_tensor
# Should we allow None weights?
if weights is None:
weights = T.ones(rank, **T.context(factors[0]))
self.shape = shape
self.rank = rank
self.factors = factors
self.weights = weights
def __getitem__(self, index):
if index == 0:
return self.weights
elif index == 1:
return self.factors
else:
raise IndexError(
f"You tried to access index {index} of a CP tensor.\n"
"You can only access index 0 and 1 of a CP tensor"
"(corresponding respectively to the weights and factors)"
)
def __setitem__(self, index, value):
if index == 0:
self.weights = value
elif index == 1:
self.factors = value
else:
raise IndexError(
f"You tried to set the value at index {index} of a CP tensor.\n"
"You can only set index 0 and 1 of a CP tensor"
"(corresponding respectively to the weights and factors)"
)
def __iter__(self):
yield self.weights
yield self.factors
def __len__(self):
return 2
def __repr__(self):
message = (
f"(weights, factors) : rank-{self.rank} CPTensor of shape {self.shape}"
)
return message
def to_tensor(self):
return cp_to_tensor(self)
def to_vec(self):
return cp_to_vec(self)
def to_unfolded(self, mode):
return cp_to_unfolded(self, mode)
def cp_copy(self):
return CPTensor(
(
T.copy(self.weights),
[T.copy(self.factors[i]) for i in range(len(self.factors))],
)
)
def mode_dot(self, matrix_or_vector, mode, keep_dim=False, copy=True):
"""n-mode product of a CP tensor and a matrix or vector at the specified mode
Parameters
----------
cp_tensor : tl.CPTensor or (core, factors)
matrix_or_vector : ndarray
1D or 2D array of shape ``(J, i_k)`` or ``(i_k, )``
matrix or vectors to which to n-mode multiply the tensor
mode : int
Returns
-------
CPTensor = (core, factors)
`mode`-mode product of `tensor` by `matrix_or_vector`
* of shape :math:`(i_1, ..., i_{k-1}, J, i_{k+1}, ..., i_N)` if matrix_or_vector is a matrix
* of shape :math:`(i_1, ..., i_{k-1}, i_{k+1}, ..., i_N)` if matrix_or_vector is a vector
See also
--------
cp_mode_dot : chaining several mode_dot in one call
"""
return cp_mode_dot(self, matrix_or_vector, mode, keep_dim=keep_dim, copy=copy)
def norm(self):
"""Returns the l2 norm of a CP tensor
Parameters
----------
cp_tensor : tl.CPTensor or (core, factors)
Returns
-------
l2-norm : int
Notes
-----
This is ||cp_to_tensor(factors)||^2
You can see this using the fact that
khatria-rao(A, B)^T x khatri-rao(A, B) = A^T x A * B^T x B
"""
return cp_norm(self)
def normalize(self, inplace=True):
"""Normalizes the factors to unit length
Turns ``factors = [|U_1, ... U_n|]`` into ``[weights; |V_1, ... V_n|]``,
where the columns of each `V_k` are normalized to unit Euclidean length
from the columns of `U_k` with the normalizing constants absorbed into
`weights`. In the special case of a symmetric tensor, `weights` holds the
eigenvalues of the tensor.
Parameters
----------
cp_tensor : CPTensor = (weight, factors)
factors is list of matrices, all with the same number of columns
i.e.::
for u in U:
u[i].shape == (s_i, R)
where `R` is fixed while `s_i` can vary with `i`
inplace : bool, default is True
if False, returns a normalized Copy
otherwise the tensor modifies itself and returns itself
Returns
-------
CPTensor = (normalisation_weights, normalised_factors)
returns itself if inplace is False, a normalized copy otherwise
"""
self.weights, self.factors = cp_normalize(self)
def _validate_cp_tensor(cp_tensor):
"""Validates a cp_tensor in the form (weights, factors)
Returns the rank and shape of the validated tensor
Parameters
----------
cp_tensor : CPTensor or (weights, factors)
Returns
-------
(shape, rank) : (int tuple, int)
size of the full tensor and rank of the CP tensor
"""
if isinstance(cp_tensor, CPTensor):
# it's already been validated at creation
return cp_tensor.shape, cp_tensor.rank
elif isinstance(cp_tensor, (float, int)): # 0-order tensor
return 0, 0
weights, factors = cp_tensor
if T.ndim(factors[0]) == 2:
rank = int(T.shape(factors[0])[1])
elif T.ndim(factors[0]) == 1:
rank = 1
else:
raise ValueError(
"Got a factor with 3 dimensions but CP factors should be at most 2D, of shape (size, rank)."
)
shape = []
for i, factor in enumerate(factors):
s = T.shape(factor)
if len(s) == 2:
current_mode_size, current_rank = s
else: # The shape is just (size, ) if rank 1
current_mode_size, current_rank = *s, 1
if current_rank != rank:
raise ValueError(
"All the factors of a CP tensor should have the same number of column."
f"However, factors[0].shape[1]={rank} but factors[{i}].shape[1]={T.shape(factor)[1]}."
)
shape.append(current_mode_size)
if weights is not None and T.shape(weights) != (rank,):
raise ValueError(
f"Given factors for a rank-{rank} CP tensor but len(weights)={T.shape(weights)}."
)
return tuple(shape), rank
def _cp_n_param(tensor_shape, rank, weights=False):
"""Number of parameters of a CP decomposition for a given `rank` and full `tensor_shape`.
Parameters
----------
tensor_shape : int tuple
shape of the full tensor to decompose (or approximate)
rank : tuple
rank of the CP decomposition
Returns
-------
n_params : int
Number of parameters of a CP decomposition of rank `rank` of a full tensor of shape `tensor_shape`
"""
factors_params = rank * np.sum(tensor_shape)
if weights:
return factors_params + rank
else:
return factors_params
def validate_cp_rank(tensor_shape, rank="same", rounding="round"):
"""Returns the rank of a CP Decomposition
Parameters
----------
tensor_shape : tupe
shape of the tensor to decompose
rank : {'same', float, int}, default is same
way to determine the rank, by default 'same'
if 'same': rank is computed to keep the number of parameters (at most) the same
if float, computes a rank so as to keep rank percent of the original number of parameters
if int, just returns rank
rounding = {'round', 'floor', 'ceil'}
Returns
-------
rank : int
rank of the decomposition
"""
if rounding == "ceil":
rounding_fun = np.ceil
elif rounding == "floor":
rounding_fun = np.floor
elif rounding == "round":
rounding_fun = np.round
else:
raise ValueError(
f"Rounding should be of round, floor or ceil, but got {rounding}"
)
if rank == "same":
rank = float(1)
if isinstance(rank, float):
rank = int(rounding_fun(np.prod(tensor_shape) * rank / np.sum(tensor_shape)))
return rank
[docs]
def cp_normalize(cp_tensor):
"""Returns cp_tensor with factors normalised to unit length
Turns ``factors = [|U_1, ... U_n|]`` into ``[weights; |V_1, ... V_n|]``,
where the columns of each `V_k` are normalized to unit Euclidean length
from the columns of `U_k` with the normalizing constants absorbed into
`weights`. In the special case of a symmetric tensor, `weights` holds the
eigenvalues of the tensor.
Parameters
----------
cp_tensor : CPTensor = (weight, factors)
factors is list of matrices, all with the same number of columns
i.e.::
for u in U:
u[i].shape == (s_i, R)
where `R` is fixed while `s_i` can vary with `i`
Returns
-------
CPTensor = (normalisation_weights, normalised_factors)
"""
_, rank = _validate_cp_tensor(cp_tensor)
weights, factors = cp_tensor
if weights is None:
weights = T.ones(rank, **T.context(factors[0]))
normalized_factors = []
for i, factor in enumerate(factors):
if i == 0:
factor = factor * weights
weights = T.ones(rank, **T.context(factor))
scales = T.norm(factor, axis=0)
scales_non_zero = T.where(
scales == 0, T.ones(T.shape(scales), **T.context(factor)), scales
)
weights = weights * scales
normalized_factors.append(factor / T.reshape(scales_non_zero, (1, -1)))
return CPTensor((weights, normalized_factors))
def cp_flip_sign(cp_tensor, mode=0, func=None):
"""Returns cp_tensor with factors flipped to have positive signs.
The sign of a given column is determined by `func`, which is the mean
by default. Any negative signs are assigned to the mode indicated by `mode`.
Parameters
----------
cp_tensor : CPTensor = (weight, factors)
factors is list of matrices, all with the same number of columns
i.e.::
for u in U:
u[i].shape == (s_i, R)
where `R` is fixed while `s_i` can vary with `i`
mode: int
mode that should receive negative signs
func: tensorly function
a function that should summarize the sign of a column
it must be able to take an axis argument
Returns
-------
CPTensor = (normalisation_weights, normalised_factors)
"""
_validate_cp_tensor(cp_tensor)
weights, factors = cp_tensor
if func is None:
func = T.mean
for jj in range(0, len(factors)):
# Skip the target mode
if jj == mode:
continue
# Calculate the sign of the current factor in each component
column_signs = T.sign(func(factors[jj], axis=0))
# Update both the current and receiving factor
factors[mode] = factors[mode] * column_signs[np.newaxis, :]
factors[jj] = factors[jj] * column_signs[np.newaxis, :]
# Check the weight signs
weight_signs = T.sign(weights)
factors[mode] = factors[mode] * weight_signs[np.newaxis, :]
weights = T.abs(weights)
return CPTensor((weights, factors))
def cp_lstsq_grad(cp_tensor, tensor, return_loss=False, mask=None):
"""This function computes (for a third-order tensor)
.. math::
\nabla 0.5 ||\\mathcal{X} - [\\mathbf{w}; \\mathbf{A}, \\mathbf{B}, \\mathbf{C}]||^2
where :math:`[\\mathbf{w}; \\mathbf{A}, \\mathbf{B}, \\mathbf{C}]` is the CP decomposition with weights
:math:`\\mathbf{w}` and factor matrices :math:`\\mathbf{A}`, :math:`\\mathbf{B}` and :math:`\\mathbf{C}`.
Note that this does not return the gradient with respect to the weights even if CP is normalized.
Parameters
----------
cp_tensor : CPTensor = (weight, factors)
factors is a list of factor matrices, all with the same number of columns
i.e. for all matrix U in factor_matrices:
U has shape ``(s_i, R)``, where R is fixed and s_i varies with i
mask : ndarray
A mask to be applied to the final tensor. It should be
broadcastable to the shape of the final tensor, that is
``(U[1].shape[0], ... U[-1].shape[0])``.
return_loss : bool
Optionally return the scalar loss function along with the gradient.
Returns
-------
cp_gradient : CPTensor = (None, factors)
factors is a list of factor matrix gradients, all with the same number of columns
i.e. for all matrix U in factor_matrices:
U has shape ``(s_i, R)``, where R is fixed and s_i varies with i
loss : float
Scalar quantity of the loss function corresponding to cp_gradient. Only returned
if return_loss = True.
"""
_validate_cp_tensor(cp_tensor)
_, factors = cp_tensor
diff = tensor - cp_to_tensor(cp_tensor)
if mask is not None:
diff = diff * mask
grad_fac = [
-unfolding_dot_khatri_rao(diff, cp_tensor, ii) for ii in range(len(factors))
]
if return_loss:
return CPTensor((None, grad_fac)), 0.5 * T.sum(diff**2)
return CPTensor((None, grad_fac))
[docs]
def cp_to_tensor(cp_tensor, mask=None):
"""Turns the Khatri-product of matrices into a full tensor
``factor_matrices = [|U_1, ... U_n|]`` becomes
a tensor shape ``(U[1].shape[0], U[2].shape[0], ... U[-1].shape[0])``
Parameters
----------
cp_tensor : CPTensor = (weight, factors)
factors is a list of factor matrices, all with the same number of columns
i.e. for all matrix U in factor_matrices:
U has shape ``(s_i, R)``, where R is fixed and s_i varies with i
mask : ndarray a mask to be applied to the final tensor. It should be
broadcastable to the shape of the final tensor, that is
``(U[1].shape[0], ... U[-1].shape[0])``.
Returns
-------
ndarray
full tensor of shape ``(U[1].shape[0], ... U[-1].shape[0])``
Notes
-----
This version works by first computing the mode-0 unfolding of the tensor
and then refolding it.
There are other possible and equivalent alternate implementation, e.g.
summing over r and updating an outer product of vectors.
"""
shape, _ = _validate_cp_tensor(cp_tensor)
if not shape: # 0-order tensor
return cp_tensor
weights, factors = cp_tensor
if len(shape) == 1: # just a vector
return T.sum(weights * factors[0], axis=1)
if weights is None:
weights = 1
if mask is None:
full_tensor = T.dot(
factors[0] * weights, T.transpose(khatri_rao(factors, skip_matrix=0))
)
else:
full_tensor = T.sum(
khatri_rao([factors[0] * weights] + factors[1:], mask=mask), axis=1
)
return fold(full_tensor, 0, shape)
[docs]
def cp_to_unfolded(cp_tensor, mode):
"""Turns the khatri-product of matrices into an unfolded tensor
turns ``factors = [|U_1, ... U_n|]`` into a mode-`mode`
unfolding of the tensor
Parameters
----------
cp_tensor : CPTensor = (weight, factors)
factors is a list of matrices, all with the same number of columns
ie for all u in factor_matrices:
u[i] has shape (s_u_i, R), where R is fixed
mode: int
mode of the desired unfolding
Returns
-------
ndarray
unfolded tensor of shape (tensor_shape[mode], -1)
Notes
-----
Writing factors = [U_1, ..., U_n], we exploit the fact that
``U_k = U[k].dot(khatri_rao(U_1, ..., U_k-1, U_k+1, ..., U_n))``
"""
_validate_cp_tensor(cp_tensor)
weights, factors = cp_tensor
if weights is not None:
return T.dot(
factors[mode] * weights, T.transpose(khatri_rao(factors, skip_matrix=mode))
)
else:
return T.dot(factors[mode], T.transpose(khatri_rao(factors, skip_matrix=mode)))
[docs]
def cp_to_vec(cp_tensor):
"""Turns the khatri-product of matrices into a vector
(the tensor ``factors = [|U_1, ... U_n|]``
is converted into a raveled mode-0 unfolding)
Parameters
----------
cp_tensor : CPTensor = (weight, factors)
factors is a list of matrices, all with the same number of columns
i.e.::
for u in U:
u[i].shape == (s_i, R)
where `R` is fixed while `s_i` can vary with `i`
Returns
-------
ndarray
vectorised tensor
"""
return tensor_to_vec(cp_to_tensor(cp_tensor))
[docs]
def cp_mode_dot(cp_tensor, matrix_or_vector, mode, keep_dim=False, copy=False):
"""n-mode product of a CP tensor and a matrix or vector at the specified mode
Parameters
----------
cp_tensor : tl.CPTensor or (core, factors)
matrix_or_vector : ndarray
1D or 2D array of shape ``(J, i_k)`` or ``(i_k, )``
matrix or vectors to which to n-mode multiply the tensor
mode : int
Returns
-------
CPTensor = (core, factors)
`mode`-mode product of `tensor` by `matrix_or_vector`
* of shape :math:`(i_1, ..., i_{k-1}, J, i_{k+1}, ..., i_N)` if matrix_or_vector is a matrix
* of shape :math:`(i_1, ..., i_{k-1}, i_{k+1}, ..., i_N)` if matrix_or_vector is a vector
See also
--------
cp_multi_mode_dot : chaining several mode_dot in one call
"""
shape, _ = _validate_cp_tensor(cp_tensor)
weights, factors = cp_tensor
contract = False
if T.ndim(matrix_or_vector) == 2: # Tensor times matrix
# Test for the validity of the operation
if matrix_or_vector.shape[1] != shape[mode]:
raise ValueError(
f"shapes {shape} and {matrix_or_vector.shape} not aligned in mode-{mode} multiplication: "
f"{shape[mode]} (mode {mode}) != {matrix_or_vector.shape[1]} (dim 1 of matrix)"
)
elif T.ndim(matrix_or_vector) == 1: # Tensor times vector
if matrix_or_vector.shape[0] != shape[mode]:
raise ValueError(
f"shapes {shape} and {matrix_or_vector.shape} not aligned for mode-{mode} multiplication: "
f"{shape[mode]} (mode {mode}) != {matrix_or_vector.shape[0]} (vector size)"
)
if not keep_dim:
contract = True # Contract over that mode
else:
raise ValueError("Can only take n_mode_product with a vector or a matrix.")
if copy:
factors = [T.copy(f) for f in factors]
weights = T.copy(weights)
if contract:
factor = factors.pop(mode)
factor = T.dot(matrix_or_vector, factor)
mode = max(mode - 1, 0)
factors[mode] *= factor
else:
factors[mode] = T.dot(matrix_or_vector, factors[mode])
if copy:
return CPTensor((weights, factors))
else:
cp_tensor.shape = tuple(f.shape[0] for f in factors)
return cp_tensor
[docs]
def cp_norm(cp_tensor):
"""Returns the l2 norm of a CP tensor
Parameters
----------
cp_tensor : tl.CPTensor or (core, factors)
Returns
-------
l2-norm : int
Notes
-----
This is ||cp_to_tensor(factors)||^2
You can see this using the fact that
khatria-rao(A, B)^T x khatri-rao(A, B) = A^T x A * B^T x B
"""
_ = _validate_cp_tensor(cp_tensor)
weights, factors = cp_tensor
norm = T.ones((factors[0].shape[1], factors[0].shape[1]), **T.context(factors[0]))
for f in factors:
norm = norm * T.dot(T.transpose(f), T.conj(f))
if weights is not None:
# norm = T.dot(T.dot(weights, norm), weights)
norm = norm * (T.reshape(weights, (-1, 1)) * T.reshape(weights, (1, -1)))
# We sum even if weights is not None
return T.sqrt(T.sum(norm))
[docs]
def cp_permute_factors(ref_cp_tensor, tensors_to_permute):
"""
Compares factors of a reference cp tensor with factors of other another tensor (or list of tensor) in order to match component order.
Permutation occurs on the columns of factors, minimizing the cosine distance to reference cp tensor with scipy
Linear Sum Assignment method. The permuted tensor (or list of tensors) and list of permutation for each permuted tensors are returned.
Parameters
----------
ref_cp_tensor : cp tensor
The tensor that serves as a reference for permutation.
tensors_to_permute : cp tensor or list of cp tensors
The tensors to permute so that the order of components match the reference tensor. Number of components must match.
Returns
-------
permuted_tensors : permuted cp tensor or list of cp tensors
permutation : list
list of permuted indices. Lenght is equal to rank of cp_tensors.
"""
if not isinstance(tensors_to_permute, list):
permuted_tensors = [tensors_to_permute.cp_copy()]
tensors_to_permute = [tensors_to_permute]
else:
permuted_tensors = []
for i in range(len(tensors_to_permute)):
permuted_tensors.append(tensors_to_permute[i].cp_copy())
tensors_to_permute[i] = cp_normalize(tensors_to_permute[i])
ref_cp_tensor = cp_normalize(ref_cp_tensor)
n_tensors = len(tensors_to_permute)
n_factors = len(ref_cp_tensor.factors)
permutation = []
for i in range(n_tensors):
_, col = congruence_coefficient(
ref_cp_tensor.factors, tensors_to_permute[i].factors
)
col = T.tensor(col, dtype=T.int64)
for f in range(n_factors):
permuted_tensors[i].factors[f] = permuted_tensors[i].factors[f][:, col]
permuted_tensors[i].weights = permuted_tensors[i].weights[col]
permutation.append(col)
if len(permuted_tensors) == 1:
permuted_tensors = permuted_tensors[0]
return permuted_tensors, permutation
```