import tensorly as tl
from ._base_decomposition import DecompositionMixin
from ..tr_tensor import validate_tr_rank, TRTensor
from ..tenalg.svd import svd_interface
[docs]def tensor_ring(input_tensor, rank, mode=0, svd="truncated_svd", verbose=False):
"""Tensor Ring decomposition via recursive SVD
Decomposes `input_tensor` into a sequence of order-3 tensors (factors) [1]_.
Parameters
----------
input_tensor : tensorly.tensor
rank : Union[int, List[int]]
maximum allowable TR rank of the factors
if int, then this is the same for all the factors
if int list, then rank[k] is the rank of the kth factor
mode : int, default is 0
index of the first factor to compute
svd : str, default is 'truncated_svd'
function to use to compute the SVD, acceptable values in tensorly.SVD_FUNS
verbose : boolean, optional
level of verbosity
Returns
-------
factors : TR factors
order-3 tensors of the TR decomposition
References
----------
.. [1] Qibin Zhao et al. "Tensor Ring Decomposition" arXiv preprint arXiv:1606.05535, (2016).
"""
rank = validate_tr_rank(tl.shape(input_tensor), rank=rank)
n_dim = len(input_tensor.shape)
# Change order
if mode:
order = tuple(range(mode, n_dim)) + tuple(range(mode))
input_tensor = tl.transpose(input_tensor, order)
rank = rank[mode:] + rank[:mode]
tensor_size = input_tensor.shape
factors = [None] * n_dim
# Getting the first factor
unfolding = tl.reshape(input_tensor, (tensor_size[0], -1))
n_row, n_column = unfolding.shape
if rank[0] * rank[1] > min(n_row, n_column):
raise ValueError(
f"rank[{mode}] * rank[{mode + 1}] = {rank[0] * rank[1]} is larger than "
f"first matricization dimension {n_row}×{n_column}.\n"
"Failed to compute first factor with specified rank. "
"Reduce specified ranks or change first matricization `mode`."
)
# SVD of unfolding matrix
U, S, V = svd_interface(unfolding, n_eigenvecs=rank[0] * rank[1], method=svd)
# Get first TR factor
factor = tl.reshape(U, (tensor_size[0], rank[0], rank[1]))
factors[0] = tl.transpose(factor, (1, 0, 2))
if verbose is True:
print("TR factor " + str(mode) + " computed with shape " + str(factor.shape))
# Get new unfolding matrix for the remaining factors
unfolding = tl.reshape(S, (-1, 1)) * V
unfolding = tl.reshape(unfolding, (rank[0], rank[1], -1))
unfolding = tl.transpose(unfolding, (1, 2, 0))
# Getting the TR factors up to n_dim - 1
for k in range(1, n_dim - 1):
# Reshape the unfolding matrix of the remaining factors
n_row = int(rank[k] * tensor_size[k])
unfolding = tl.reshape(unfolding, (n_row, -1))
# SVD of unfolding matrix
n_row, n_column = unfolding.shape
current_rank = min(n_row, n_column, rank[k + 1])
U, S, V = svd_interface(unfolding, n_eigenvecs=current_rank, method=svd)
rank[k + 1] = current_rank
# Get kth TR factor
factors[k] = tl.reshape(U, (rank[k], tensor_size[k], rank[k + 1]))
if verbose is True:
print(
"TR factor "
+ str((mode + k) % n_dim)
+ " computed with shape "
+ str(factors[k].shape)
)
# Get new unfolding matrix for the remaining factors
unfolding = tl.reshape(S, (-1, 1)) * V
# Getting the last factor
prev_rank = unfolding.shape[0]
factors[-1] = tl.reshape(unfolding, (prev_rank, -1, rank[0]))
if verbose is True:
print(
"TR factor "
+ str((mode - 1) % n_dim)
+ " computed with shape "
+ str(factors[-1].shape)
)
# Reorder factors to match input
if mode:
factors = factors[-mode:] + factors[:-mode]
return TRTensor(factors)
[docs]class TensorRing(DecompositionMixin):
"""Tensor Ring decomposition via recursive SVD
Decomposes `input_tensor` into a sequence of order-3 tensors (factors) [1]_.
Parameters
----------
input_tensor : tensorly.tensor
rank : Union[int, List[int]]
maximum allowable TR rank of the factors
if int, then this is the same for all the factors
if int list, then rank[k] is the rank of the kth factor
mode : int, default is 0
index of the first factor to compute
svd : str, default is 'truncated_svd'
function to use to compute the SVD, acceptable values in tensorly.SVD_FUNS
verbose : boolean, optional
level of verbosity
Returns
-------
factors : TR factors
order-3 tensors of the TR decomposition
References
----------
.. [1] Qibin Zhao et al. "Tensor Ring Decomposition" arXiv preprint arXiv:1606.05535, (2016).
"""
def __init__(self, rank, mode=0, svd="truncated_svd", verbose=False):
self.rank = rank
self.mode = mode
self.svd = svd
self.verbose = verbose
def fit_transform(self, tensor):
self.decomposition_ = tensor_ring(
tensor, rank=self.rank, mode=self.mode, svd=self.svd, verbose=self.verbose
)
return self.decomposition_