import numpy as np
import warnings
import tensorly as tl
from ..random import random_cp
from ._base_decomposition import DecompositionMixin
from ..base import unfold
from ..cp_tensor import CPTensor, cp_norm, validate_cp_rank
from ..tenalg.proximal import admm, proximal_operator, validate_constraints
from ..tenalg.svd import svd_interface
from ..tenalg import unfolding_dot_khatri_rao
# Author: Jean Kossaifi
# Jeremy Cohen <jeremy.cohen@irisa.fr>
# Caglayan Tuna <caglayantun@gmail.com>
# License: BSD 3 clause
def initialize_constrained_parafac(
tensor,
rank,
init="svd",
svd="truncated_svd",
random_state=None,
non_negative=None,
l1_reg=None,
l2_reg=None,
l2_square_reg=None,
unimodality=None,
normalize=None,
simplex=None,
normalized_sparsity=None,
soft_sparsity=None,
smoothness=None,
monotonicity=None,
hard_sparsity=None,
):
r"""Initialize factors used in `constrained_parafac`.
Parameters
----------
The type of initialization is set using `init`. If `init == 'random'` then
initialize factor matrices with uniform distribution using `random_state`. If `init == 'svd'` then
initialize the `m`th factor matrix using the `rank` left singular vectors
of the `m`th unfolding of the input tensor. If init is a previously initialized `cp tensor`, all
the weights are pulled in the last factor and then the weights are set to "1" for the output tensor.
Lastly, factors are updated with proximal operator according to the selected constraint(s), so that they satisfy the
imposed constraints (does not apply to cptensor initialization).
Parameters
----------
tensor : ndarray
rank : int
random_state : {None, int, np.random.RandomState}
init : {'svd', 'random', cptensor}, optional
svd : str, default is 'truncated_svd'
function to use to compute the SVD, acceptable values in tensorly.SVD_FUNS
non_negative : bool or dictionary
This constraint is clipping negative values to '0'.
If it is True, non-negative constraint is applied to all modes.
l1_reg : float or list or dictionary, optional
Penalizes the factor with the l1 norm using the input value as regularization parameter.
l2_reg : float or list or dictionary, optional
Penalizes the factor with the l2 norm using the input value as regularization parameter.
l2_square_reg : float or list or dictionary, optional
Penalizes the factor with the l2 square norm using the input value as regularization parameter.
unimodality : bool or dictionary, optional
If it is True, unimodality constraint is applied to all modes.
Applied to each column seperately.
normalize : bool or dictionary, optional
This constraint divides all the values by maximum value of the input array.
If it is True, normalize constraint is applied to all modes.
simplex : float or list or dictionary, optional
Projects on the simplex with the given parameter
Applied to each column seperately.
normalized_sparsity : float or list or dictionary, optional
Normalizes with the norm after hard thresholding
soft_sparsity : float or list or dictionary, optional
Impose that the columns of factors have L1 norm bounded by a user-defined threshold.
smoothness : float or list or dictionary, optional
Optimizes the factors by solving a banded system
monotonicity : bool or dictionary, optional
Projects columns to monotonically decreasing distrbution
Applied to each column seperately.
If it is True, monotonicity constraint is applied to all modes.
hard_sparsity : float or list or dictionary, optional
Hard thresholding with the given threshold
Returns
-------
factors : CPTensor
An initial cp tensor.
"""
n_modes = tl.ndim(tensor)
rng = tl.check_random_state(random_state)
if init == "random":
weights, factors = random_cp(
tl.shape(tensor), rank, normalise_factors=False, **tl.context(tensor)
)
elif init == "svd":
factors = []
for mode in range(tl.ndim(tensor)):
U, S, _ = svd_interface(unfold(tensor, mode), n_eigenvecs=rank, method=svd)
# Put SVD initialization on the same scaling as the tensor in case normalize_factors=False
if mode == 0:
idx = min(rank, tl.shape(S)[0])
U = tl.index_update(U, tl.index[:, :idx], U[:, :idx] * S[:idx])
if tensor.shape[mode] < rank:
random_part = tl.tensor(
rng.random_sample((U.shape[0], rank - tl.shape(tensor)[mode])),
**tl.context(tensor),
)
U = tl.concatenate([U, random_part], axis=1)
factors.append(U[:, :rank])
elif isinstance(init, (tuple, list, CPTensor)):
try:
weights, factors = CPTensor(init)
if tl.all(weights == 1):
weights, factors = CPTensor((None, factors))
else:
weights_avg = tl.prod(weights) ** (1.0 / tl.shape(weights)[0])
for i in range(len(factors)):
factors[i] = factors[i] * weights_avg
kt = CPTensor((None, factors))
return kt
except ValueError:
raise ValueError(
"If initialization method is a mapping, then it must "
"be possible to convert it to a CPTensor instance"
)
else:
raise ValueError(f'Initialization method "{init}" not recognized')
for i in range(n_modes):
factors[i] = proximal_operator(
factors[i],
non_negative=non_negative,
l1_reg=l1_reg,
l2_reg=l2_reg,
l2_square_reg=l2_square_reg,
unimodality=unimodality,
normalize=normalize,
simplex=simplex,
normalized_sparsity=normalized_sparsity,
soft_sparsity=soft_sparsity,
smoothness=smoothness,
monotonicity=monotonicity,
hard_sparsity=hard_sparsity,
n_const=n_modes,
order=i,
)
kt = CPTensor((None, factors))
return kt
[docs]
def constrained_parafac(
tensor,
rank,
n_iter_max=100,
n_iter_max_inner=10,
init="svd",
svd="truncated_svd",
tol_outer=1e-8,
tol_inner=1e-6,
random_state=None,
verbose=0,
return_errors=False,
cvg_criterion="abs_rec_error",
fixed_modes=None,
non_negative=None,
l1_reg=None,
l2_reg=None,
l2_square_reg=None,
unimodality=None,
normalize=None,
simplex=None,
normalized_sparsity=None,
soft_sparsity=None,
smoothness=None,
monotonicity=None,
hard_sparsity=None,
):
"""CANDECOMP/PARAFAC decomposition via alternating optimization of
alternating direction method of multipliers (AO-ADMM):
Computes a rank-`rank` decomposition of `tensor` [1]_ such that::
tensor = [|weights; factors[0], ..., factors[-1] |],
where factors are either penalized or constrained according to the user-defined constraint.
In order to compute the factors efficiently, the ADMM algorithm
introduces an auxilliary factor which is called factor_aux in the function.
Parameters
----------
tensor : ndarray
rank : int
Number of components.
n_iter_max : int
Maximum number of iteration for outer loop
n_iter_max_inner : int
Number of iteration for inner loop
init : {'svd', 'random', cptensor}, optional
Type of factor matrix initialization. See `initialize_factors`.
svd : str, default is 'truncated_svd'
function to use to compute the SVD, acceptable values in tensorly.SVD_FUNS
tol_outer : float, optional
(Default: 1e-8) Relative reconstruction error tolerance for outer loop. The
algorithm is considered to have found a local minimum when the
reconstruction error is less than `tol_outer`.
tol_inner : float, optional
(Default: 1e-6) Absolute reconstruction error tolerance for factor update during inner loop, i.e. ADMM optimization.
random_state : {None, int, np.random.RandomState}
verbose : int, optional
Level of verbosity
return_errors : bool, optional
Activate return of iteration errors
non_negative : bool or dictionary
This constraint is clipping negative values to '0'.
If it is True, non-negative constraint is applied to all modes.
l1_reg : float or list or dictionary, optional
Penalizes the factor with the l1 norm using the input value as regularization parameter.
l2_reg : float or list or dictionary, optional
Penalizes the factor with the l2 norm using the input value as regularization parameter.
l2_square_reg : float or list or dictionary, optional
Penalizes the factor with the l2 square norm using the input value as regularization parameter.
unimodality : bool or dictionary, optional
If it is True, unimodality constraint is applied to all modes.
Applied to each column seperately.
normalize : bool or dictionary, optional
This constraint divides all the values by maximum value of the input array.
If it is True, normalize constraint is applied to all modes.
simplex : float or list or dictionary, optional
Projects on the simplex with the given parameter
Applied to each column seperately.
normalized_sparsity : float or list or dictionary, optional
Normalizes with the norm after hard thresholding
soft_sparsity : float or list or dictionary, optional
Impose that the columns of factors have L1 norm bounded by a user-defined threshold.
smoothness : float or list or dictionary, optional
Optimizes the factors by solving a banded system
monotonicity : bool or dictionary, optional
Projects columns to monotonically decreasing distrbution
Applied to each column seperately.
If it is True, monotonicity constraint is applied to all modes.
hard_sparsity : float or list or dictionary, optional
Hard thresholding with the given threshold
cvg_criterion : {'abs_rec_error', 'rec_error'}, optional
Stopping criterion if `tol` is not None.
If 'rec_error', algorithm stops at current iteration if ``(previous rec_error - current rec_error) < tol``.
If 'abs_rec_error', algorithm terminates when `|previous rec_error - current rec_error| < tol`.
fixed_modes : list, default is None
A list of modes for which the initial value is not modified.
The last mode cannot be fixed due to error computation.
Returns
-------
CPTensor : (weight, factors)
* weights : 1D array of shape (rank, )
* factors : List of factors of the CP decomposition element `i` is of shape ``(tensor.shape[i], rank)``
errors : list
A list of reconstruction errors at each iteration of the algorithms.
References
----------
.. [1] T.G.Kolda and B.W.Bader, "Tensor Decompositions and Applications", SIAM
REVIEW, vol. 51, n. 3, pp. 455-500, 2009.
.. [2] Huang, Kejun, Nicholas D. Sidiropoulos, and Athanasios P. Liavas.
"A flexible and efficient algorithmic framework for constrained matrix and tensor factorization." IEEE
Transactions on Signal Processing 64.19 (2016): 5052-5065.
"""
rank = validate_cp_rank(tl.shape(tensor), rank=rank)
_, _ = validate_constraints(
non_negative=non_negative,
l1_reg=l1_reg,
l2_reg=l2_reg,
l2_square_reg=l2_square_reg,
unimodality=unimodality,
normalize=normalize,
simplex=simplex,
normalized_sparsity=normalized_sparsity,
soft_sparsity=soft_sparsity,
smoothness=smoothness,
monotonicity=monotonicity,
hard_sparsity=hard_sparsity,
n_const=tl.ndim(tensor),
)
weights, factors = initialize_constrained_parafac(
tensor,
rank,
init=init,
svd=svd,
random_state=random_state,
non_negative=non_negative,
l1_reg=l1_reg,
l2_reg=l2_reg,
l2_square_reg=l2_square_reg,
unimodality=unimodality,
normalize=normalize,
simplex=simplex,
normalized_sparsity=normalized_sparsity,
soft_sparsity=soft_sparsity,
smoothness=smoothness,
monotonicity=monotonicity,
hard_sparsity=hard_sparsity,
)
rec_errors = []
norm_tensor = tl.norm(tensor, 2)
if fixed_modes is None:
fixed_modes = []
if tl.ndim(tensor) - 1 in fixed_modes:
warnings.warn(
"You asked for fixing the last mode, which is not supported.\n "
"The last mode will not be fixed. Consider using tl.moveaxis()"
)
fixed_modes.remove(tl.ndim(tensor) - 1)
modes_list = [mode for mode in range(tl.ndim(tensor)) if mode not in fixed_modes]
# ADMM inits
dual_variables = []
factors_aux = []
for i in range(len(factors)):
dual_variables.append(tl.zeros(tl.shape(factors[i])))
factors_aux.append(tl.transpose(tl.zeros(tl.shape(factors[i]))))
for iteration in range(n_iter_max):
if verbose > 1:
print("Starting iteration", iteration + 1)
for mode in modes_list:
if verbose > 1:
print("Mode", mode, "of", tl.ndim(tensor))
pseudo_inverse = tl.tensor(np.ones((rank, rank)), **tl.context(tensor))
for i, factor in enumerate(factors):
if i != mode:
pseudo_inverse = pseudo_inverse * tl.dot(
tl.transpose(factor), factor
)
mttkrp = unfolding_dot_khatri_rao(tensor, (None, factors), mode)
factors[mode], factors_aux[mode], dual_variables[mode] = admm(
mttkrp,
pseudo_inverse,
factors[mode],
dual_variables[mode],
n_iter_max=n_iter_max_inner,
n_const=tl.ndim(tensor),
order=mode,
non_negative=non_negative,
l1_reg=l1_reg,
l2_reg=l2_reg,
l2_square_reg=l2_square_reg,
unimodality=unimodality,
normalize=normalize,
simplex=simplex,
normalized_sparsity=normalized_sparsity,
soft_sparsity=soft_sparsity,
smoothness=smoothness,
monotonicity=monotonicity,
hard_sparsity=hard_sparsity,
tol=tol_inner,
)
factors_norm = cp_norm((weights, factors))
iprod = tl.sum(tl.sum(mttkrp * factors[-1], axis=0) * weights)
rec_error = (
tl.sqrt(tl.abs(norm_tensor**2 + factors_norm**2 - 2 * iprod)) / norm_tensor
)
rec_errors.append(rec_error)
constraint_error = 0
for mode in modes_list:
constraint_error += tl.norm(
factors[mode] - tl.transpose(factors_aux[mode])
) / tl.norm(factors[mode])
if tol_outer:
if iteration >= 1:
rec_error_decrease = rec_errors[-2] - rec_errors[-1]
if verbose:
print(
f"iteration {iteration}, reconstruction error: {rec_error}, decrease = {rec_error_decrease}"
)
if constraint_error < tol_outer:
break
if cvg_criterion == "abs_rec_error":
stop_flag = abs(rec_error_decrease) < tol_outer
elif cvg_criterion == "rec_error":
stop_flag = rec_error_decrease < tol_outer
else:
raise TypeError("Unknown convergence criterion")
if stop_flag:
if verbose:
print(f"PARAFAC converged after {iteration} iterations")
break
else:
if verbose:
print(f"reconstruction error={rec_errors[-1]}")
cp_tensor = CPTensor((weights, factors))
if return_errors:
return cp_tensor, rec_errors
else:
return cp_tensor
[docs]
class ConstrainedCP(DecompositionMixin):
"""CANDECOMP/PARAFAC decomposition via alternating optimization of
alternating direction method of multipliers (AO-ADMM):
Computes a rank-`rank` decomposition of `tensor` [1]_ such that::
tensor = [|weights; factors[0], ..., factors[-1] |],
where factors are either penalized or constrained according to the user-defined constraint.
In order to compute the factors efficiently, the ADMM algorithm
introduces an auxilliary factor which is called factor_aux in the function.
Parameters
----------
tensor : ndarray
rank : int
Number of components.
n_iter_max : int
Maximum number of iteration for outer loop
n_iter_max_inner : int
Number of iteration for inner loop
init : {'svd', 'random', cptensor}, optional
Type of factor matrix initialization. See `initialize_factors`.
svd : str, default is 'truncated_svd'
function to use to compute the SVD, acceptable values in tensorly.SVD_FUNS
tol_outer : float, optional
(Default: 1e-8) Relative reconstruction error tolerance for outer loop. The
algorithm is considered to have found a local minimum when the
reconstruction error is less than `tol_outer`.
tol_inner : float, optional
(Default: 1e-6) Absolute reconstruction error tolerance for factor update during inner loop, i.e. ADMM optimization.
random_state : {None, int, np.random.RandomState}
verbose : int, optional
Level of verbosity
return_errors : bool, optional
Activate return of iteration errors
non_negative : bool or dictionary
This constraint is clipping negative values to '0'.
If it is True, non-negative constraint is applied to all modes.
l1_reg : float or list or dictionary, optional
Penalizes the factor with the l1 norm using the input value as regularization parameter.
l2_reg : float or list or dictionary, optional
Penalizes the factor with the l2 norm using the input value as regularization parameter.
l2_square_reg : float or list or dictionary, optional
Penalizes the factor with the l2 square norm using the input value as regularization parameter.
unimodality : bool or dictionary, optional
If it is True, unimodality constraint is applied to all modes.
Applied to each column seperately.
normalize : bool or dictionary, optional
This constraint divides all the values by maximum value of the input array.
If it is True, normalize constraint is applied to all modes.
simplex : float or list or dictionary, optional
Projects on the simplex with the given parameter
Applied to each column seperately.
normalized_sparsity : float or list or dictionary, optional
Normalizes with the norm after hard thresholding
soft_sparsity : float or list or dictionary, optional
Impose that the columns of factors have L1 norm bounded by a user-defined threshold.
smoothness : float or list or dictionary, optional
Optimizes the factors by solving a banded system
monotonicity : bool or dictionary, optional
Projects columns to monotonically decreasing distrbution
Applied to each column seperately.
If it is True, monotonicity constraint is applied to all modes.
hard_sparsity : float or list or dictionary, optional
Hard thresholding with the given threshold
cvg_criterion : {'abs_rec_error', 'rec_error'}, optional
Stopping criterion if `tol` is not None.
If 'rec_error', algorithm stops at current iteration if ``(previous rec_error - current rec_error) < tol``.
If 'abs_rec_error', algorithm terminates when `|previous rec_error - current rec_error| < tol`.
fixed_modes : list, default is None
A list of modes for which the initial value is not modified.
The last mode cannot be fixed due to error computation.
Returns
-------
CPTensor : (weight, factors)
* weights : 1D array of shape (rank, )
* factors : List of factors of the CP decomposition element `i` is of shape ``(tensor.shape[i], rank)``
errors : list
A list of reconstruction errors at each iteration of the algorithms.
References
----------
.. [1] T.G.Kolda and B.W.Bader, "Tensor Decompositions and Applications", SIAM
REVIEW, vol. 51, n. 3, pp. 455-500, 2009.
.. [2] Huang, Kejun, Nicholas D. Sidiropoulos, and Athanasios P. Liavas.
"A flexible and efficient algorithmic framework for constrained matrix and tensor factorization." IEEE
Transactions on Signal Processing 64.19 (2016): 5052-5065.
"""
def __init__(
self,
rank,
n_iter_max=100,
n_iter_max_inner=10,
init="svd",
svd="truncated_svd",
tol_outer=1e-8,
tol_inner=1e-6,
random_state=None,
verbose=0,
return_errors=False,
cvg_criterion="abs_rec_error",
fixed_modes=None,
non_negative=None,
l1_reg=None,
l2_reg=None,
l2_square_reg=None,
unimodality=None,
normalize=None,
simplex=None,
normalized_sparsity=None,
soft_sparsity=None,
smoothness=None,
monotonicity=None,
hard_sparsity=None,
):
self.rank = rank
self.n_iter_max = n_iter_max
self.n_iter_max_inner = n_iter_max_inner
self.init = init
self.svd = svd
self.tol_outer = tol_outer
self.tol_inner = tol_inner
self.random_state = random_state
self.verbose = verbose
self.return_errors = return_errors
self.cvg_criterion = cvg_criterion
self.fixed_modes = fixed_modes
self.non_negative = non_negative
self.l1_reg = l1_reg
self.l2_reg = l2_reg
self.l2_square_reg = l2_square_reg
self.unimodality = unimodality
self.normalize = normalize
self.simplex = simplex
self.normalized_sparsity = normalized_sparsity
self.soft_sparsity = soft_sparsity
self.smoothness = smoothness
self.monotonicity = monotonicity
self.hard_sparsity = hard_sparsity