# Source code for tensorly.decomposition._cp

```import numpy as np
import warnings

import tensorly as tl
from ._base_decomposition import DecompositionMixin
from ..random import random_cp
from ..base import unfold
from ..cp_tensor import (cp_to_tensor, CPTensor,
unfolding_dot_khatri_rao, cp_norm,
cp_normalize, validate_cp_rank)

# Authors: Jean Kossaifi <jean.kossaifi+tensors@gmail.com>
#          Chris Swierczewski <csw@amazon.com>
#          Sam Schneider <samjohnschneider@gmail.com>
#          Aaron Meurer <asmeurer@gmail.com>

def initialize_cp(tensor, rank, init='svd', svd='numpy_svd', random_state=None, normalize_factors=False):
r"""Initialize factors used in `parafac`.

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.

Parameters
----------
tensor : ndarray
rank : int
init : {'svd', 'random', cptensor}, optional
svd : str, default is 'numpy_svd'
function to use to compute the SVD, acceptable values in tensorly.SVD_FUNS
non_negative : bool, default is False
if True, non-negative factors are returned

Returns
-------
factors : CPTensor
An initial cp tensor.

"""
rng = tl.check_random_state(random_state)

if init == 'random':
kt = random_cp(tl.shape(tensor), rank, normalise_factors=False, **tl.context(tensor))

elif init == 'svd':
try:
svd_fun = tl.SVD_FUNS[svd]
except KeyError:
message = 'Got svd={}. However, for the current backend ({}), the possible choices are {}'.format(
svd, tl.get_backend(), tl.SVD_FUNS)
raise ValueError(message)

factors = []
for mode in range(tl.ndim(tensor)):
U, S, _ = svd_fun(unfold(tensor, mode), n_eigenvecs=rank)

# Put SVD initialization on the same scaling as the tensor in case normalize_factors=False
if mode == 0:
idx = min(rank, tl.shape(S))
U = tl.index_update(U, tl.index[:, :idx], U[:, :idx] * S[:idx])

if tensor.shape[mode] < rank:
# TODO: this is a hack but it seems to do the job for now
random_part = tl.tensor(rng.random_sample((U.shape, rank - tl.shape(tensor)[mode])), **tl.context(tensor))
U = tl.concatenate([U, random_part], axis=1)

factors.append(U[:, :rank])

kt = CPTensor((None, factors))

elif isinstance(init, (tuple, list, CPTensor)):
# TODO: Test this
try:
if normalize_factors is True:
warnings.warn('It is not recommended to initialize a tensor with normalizing. Consider normalizing the tensor before using this function')

kt = CPTensor(init)
weights, factors = kt

if tl.all(weights == 1):
kt = CPTensor((None, factors))
else:
weights_avg = tl.prod(weights) ** (1.0 / tl.shape(weights))
for i in range(len(factors)):
factors[i] = factors[i] * weights_avg
kt = CPTensor((None, factors))
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('Initialization method "{}" not recognized'.format(init))

if normalize_factors:
kt = cp_normalize(kt)

return kt

def sparsify_tensor(tensor, card):
"""Zeros out all elements in the `tensor` except `card` elements with maximum absolute values.

Parameters
----------
tensor : ndarray
card : int
Desired number of non-zero elements in the `tensor`

Returns
-------
ndarray of shape tensor.shape
"""
if card >= np.prod(tensor.shape):
return tensor
bound = tl.sort(tl.abs(tensor), axis=None)[-card]

return tl.where(tl.abs(tensor) < bound, tl.zeros(tensor.shape, **tl.context(tensor)), tensor)

def error_calc(tensor, norm_tensor, weights, factors, sparsity, mask, mttkrp=None):
r""" Perform the error calculation. Different forms are used here depending upon
the available information. If `mttkrp=None` or masking is being performed, then the
full tensor must be constructed. Otherwise, the mttkrp is used to reduce the calculation cost.

Parameters
----------
tensor : tensor
norm_tensor : float
The l2 norm of tensor.
weights : tensor
The current CP weights
factors : tensor
The current CP factors
sparsity : float or int
Whether we allow for a sparse component
mttkrp : tensor or None
The mttkrp product, if available.

Returns
-------
unnorml_rec_error : float
The unnormalized reconstruction error.
tensor : tensor
The tensor, in case it has been updated by masking.
norm_tensor: float
The tensor norm, in case it has been updated by masking.
"""

# If we have to update the mask we already have to build the full tensor
if (mask is not None) or (mttkrp is None):
low_rank_component = cp_to_tensor((weights, factors))

# Update the tensor based on the mask
norm_tensor = tl.norm(tensor, 2)

if sparsity:
sparse_component = sparsify_tensor(tensor - low_rank_component, sparsity)
else:
sparse_component = 0.0

unnorml_rec_error = tl.norm(tensor - low_rank_component - sparse_component, 2)
else:
if sparsity:
low_rank_component = cp_to_tensor((weights, factors))
sparse_component = sparsify_tensor(tensor - low_rank_component, sparsity)

unnorml_rec_error = tl.norm(tensor - low_rank_component - sparse_component, 2)
else:
# ||tensor - rec||^2 = ||tensor||^2 + ||rec||^2 - 2*<tensor, rec>
factors_norm = cp_norm((weights, factors))

# mttkrp and factor for the last mode. This is equivalent to the
# inner product <tensor, factorization>
iprod = tl.sum(tl.sum(mttkrp * factors[-1], axis=0) * weights)
unnorml_rec_error = tl.sqrt(tl.abs(norm_tensor**2 + factors_norm**2 - 2 * iprod))

return unnorml_rec_error, tensor, norm_tensor

[docs]def parafac(tensor, rank, n_iter_max=100, init='svd', svd='numpy_svd',
normalize_factors=False, orthogonalise=False,
tol=1e-8, random_state=None,
verbose=0, return_errors=False,
sparsity=None,
cvg_criterion='abs_rec_error',
fixed_modes=None,
linesearch=False):
"""CANDECOMP/PARAFAC decomposition via alternating least squares (ALS)
Computes a rank-`rank` decomposition of `tensor` _ such that::

tensor = [|weights; factors, ..., factors[-1] |].

Parameters
----------
tensor : ndarray
rank  : int
Number of components.
n_iter_max : int
Maximum number of iteration
init : {'svd', 'random'}, optional
Type of factor matrix initialization. See `initialize_factors`.
svd : str, default is 'numpy_svd'
function to use to compute the SVD, acceptable values in tensorly.SVD_FUNS
normalize_factors : if True, aggregate the weights of each factor in a 1D-tensor
of shape (rank, ), which will contain the norms of the factors
tol : float, optional
(Default: 1e-6) Relative reconstruction error tolerance. The
algorithm is considered to have found the global minimum when the
reconstruction error is less than `tol`.
random_state : {None, int, np.random.RandomState}
verbose : int, optional
Level of verbosity
return_errors : bool, optional
Activate return of iteration errors
array of booleans with the same shape as ``tensor`` should be 0 where
the values are missing and 1 everywhere else. Note:  if tensor is
sparse, then mask should also be sparse with a fill value of 1 (or
True). Allows for missing values _
cvg_criterion : {'abs_rec_error', 'rec_error'}, optional
Stopping criterion for ALS, works if `tol` is not None.
If 'rec_error',  ALS stops at current iteration if ``(previous rec_error - current rec_error) < tol``.
If 'abs_rec_error', ALS terminates when `|previous rec_error - current rec_error| < tol`.
sparsity : float or int
If `sparsity` is not None, we approximate tensor as a sum of low_rank_component and sparse_component, where low_rank_component = cp_to_tensor((weights, factors)). `sparsity` denotes desired fraction or number of non-zero elements in the sparse_component of the `tensor`.
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.
If using a tensor with masked values, this initializes using SVD multiple times to
remove the effect of these missing values on the initialization.
linesearch : bool, default is False
Whether to perform line search as proposed by Bro .

Returns
-------
CPTensor : (weight, factors)
* weights : 1D array of shape (rank, )

* all ones if normalize_factors is False (default)
* weights of the (normalized) factors otherwise

* factors : List of factors of the CP decomposition element `i` is of shape ``(tensor.shape[i], rank)``
* sparse_component : nD array of shape tensor.shape. Returns only if `sparsity` is not None.

errors : list
A list of reconstruction errors at each iteration of the algorithms.

References
----------
..  T.G.Kolda and B.W.Bader, "Tensor Decompositions and Applications", SIAM
REVIEW, vol. 51, n. 3, pp. 455-500, 2009.

..  Tomasi, Giorgio, and Rasmus Bro. "PARAFAC and missing values."
Chemometrics and Intelligent Laboratory Systems 75.2 (2005): 163-180.

..  R. Bro, "Multi-Way Analysis in the Food Industry: Models, Algorithms, and
Applications", PhD., University of Amsterdam, 1998
"""
rank = validate_cp_rank(tl.shape(tensor), rank=rank)

if orthogonalise and not isinstance(orthogonalise, int):
orthogonalise = n_iter_max

if linesearch:
acc_pow = 2.0 # Extrapolate to the iteration^(1/acc_pow) ahead
acc_fail = 0 # How many times acceleration have failed
max_fail = 4 # Increase acc_pow with one after max_fail failure

weights, factors = initialize_cp(tensor, rank, init=init, svd=svd,
random_state=random_state,
normalize_factors=normalize_factors)

if mask is not None and init == "svd":

weights, factors = initialize_cp(tensor, rank, init=init, svd=svd, random_state=random_state, normalize_factors=normalize_factors)

rec_errors = []
norm_tensor = tl.norm(tensor, 2)
Id = tl.eye(rank, **tl.context(tensor)) * l2_reg

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]

if sparsity:
sparse_component = tl.zeros_like(tensor)
if isinstance(sparsity, float):
sparsity = int(sparsity * np.prod(tensor.shape))
else:
sparsity = int(sparsity)

for iteration in range(n_iter_max):
if orthogonalise and iteration <= orthogonalise:
factors = [tl.qr(f) if min(tl.shape(f)) >= rank else f for i, f in enumerate(factors)]

if linesearch and iteration % 2 == 0:
factors_last = [tl.copy(f) for f in factors]
weights_last = tl.copy(weights)

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)
pseudo_inverse += Id

mttkrp = unfolding_dot_khatri_rao(tensor, (None, factors), mode)

factor = tl.transpose(tl.solve(tl.transpose(pseudo_inverse),
tl.transpose(mttkrp)))

if normalize_factors:
scales = tl.norm(factor, 2, axis=0)
weights = tl.where(scales==0, tl.ones(tl.shape(scales), **tl.context(factor)), scales)
factor = factor / tl.reshape(weights, (1, -1))

factors[mode] = factor

# Will we be performing a line search iteration
if linesearch and iteration % 2 == 0 and iteration > 5:
line_iter = True
else:
line_iter = False

# Calculate the current unnormalized error if we need it
if (tol or return_errors) and line_iter is False:
unnorml_rec_error, tensor, norm_tensor = error_calc(tensor, norm_tensor, weights, factors, sparsity, mask, mttkrp)
else:

# Start line search if requested.
if line_iter is True:
jump = iteration ** (1.0 / acc_pow)

new_weights = weights_last + (weights - weights_last) * jump
new_factors = [factors_last[ii] + (factors[ii] - factors_last[ii]) * jump for ii in range(tl.ndim(tensor))]

new_rec_error, new_tensor, new_norm_tensor = error_calc(tensor, norm_tensor, new_weights, new_factors, sparsity, mask)

if (new_rec_error / new_norm_tensor) < rec_errors[-1]:
factors, weights = new_factors, new_weights
tensor, norm_tensor = new_tensor, new_norm_tensor
unnorml_rec_error = new_rec_error
acc_fail = 0

if verbose:
print("Accepted line search jump of {}.".format(jump))
else:
unnorml_rec_error, tensor, norm_tensor = error_calc(tensor, norm_tensor, weights, factors, sparsity, mask, mttkrp)
acc_fail += 1

if verbose:
print("Line search failed for jump of {}.".format(jump))

if acc_fail == max_fail:
acc_pow += 1.0
acc_fail = 0

if verbose:
print("Reducing acceleration.")

rec_error = unnorml_rec_error / norm_tensor
rec_errors.append(rec_error)

if tol:

if iteration >= 1:
rec_error_decrease = rec_errors[-2] - rec_errors[-1]

if verbose:
print("iteration {}, reconstruction error: {}, decrease = {}, unnormalized = {}".format(iteration, rec_error, rec_error_decrease, unnorml_rec_error))

if cvg_criterion == 'abs_rec_error':
stop_flag = abs(rec_error_decrease) < tol
elif cvg_criterion == 'rec_error':
stop_flag = rec_error_decrease < tol
else:
raise TypeError("Unknown convergence criterion")

if stop_flag:
if verbose:
print("PARAFAC converged after {} iterations".format(iteration))
break

else:
if verbose:
print('reconstruction error={}'.format(rec_errors[-1]))

cp_tensor = CPTensor((weights, factors))

if sparsity:
sparse_component = sparsify_tensor(tensor - cp_to_tensor((weights, factors)),
sparsity)
cp_tensor = (cp_tensor, sparse_component)

if return_errors:
return cp_tensor, rec_errors
else:
return cp_tensor

[docs]def sample_khatri_rao(matrices, n_samples, skip_matrix=None,
return_sampled_rows=False, random_state=None):
"""Random subsample of the Khatri-Rao product of the given list of matrices

If one matrix only is given, that matrix is directly returned.

Parameters
----------
matrices : ndarray list
list of matrices with the same number of columns, i.e.::

for i in len(matrices):
matrices[i].shape = (n_i, m)

n_samples : int
number of samples to be taken from the Khatri-Rao product

skip_matrix : None or int, optional, default is None
if not None, index of a matrix to skip

random_state : None, int or numpy.random.RandomState
if int, used to set the seed of the random number generator
if numpy.random.RandomState, used to generate random_samples

returned_sampled_rows : bool, default is False
if True, also returns a list of the rows sampled from the full
khatri-rao product

Returns
-------
sampled_Khatri_Rao : ndarray
The sampled matricised tensor Khatri-Rao with `n_samples` rows

indices : tuple list
a list of indices sampled for each mode

indices_kr : int list
list of length `n_samples` containing the sampled row indices
"""
if random_state is None or not isinstance(random_state, np.random.RandomState):
rng = tl.check_random_state(random_state)
warnings.warn('You are creating a new random number generator at each call.\n'
'If you are calling sample_khatri_rao inside a loop this will be slow:'
' best to create a rng outside and pass it as argument (random_state=rng).')
else:
rng = random_state

if skip_matrix is not None:
matrices = [matrices[i] for i in range(len(matrices)) if i != skip_matrix]

rank = tl.shape(matrices)
sizes = [tl.shape(m) for m in matrices]

# For each matrix, randomly choose n_samples indices for which to compute the khatri-rao product
indices_list = [rng.randint(0, tl.shape(m), size=n_samples, dtype=int) for m in matrices]
if return_sampled_rows:
# Compute corresponding rows of the full khatri-rao product
indices_kr = np.zeros((n_samples), dtype=int)
for size, indices in zip(sizes, indices_list):
indices_kr = indices_kr * size + indices

# Compute the Khatri-Rao product for the chosen indices
sampled_kr = tl.ones((n_samples, rank), **tl.context(matrices))
for indices, matrix in zip(indices_list, matrices):
sampled_kr = sampled_kr * matrix[indices, :]

if return_sampled_rows:
return sampled_kr, indices_list, indices_kr
else:
return sampled_kr, indices_list

[docs]def randomised_parafac(tensor, rank, n_samples, n_iter_max=100, init='random', svd='numpy_svd',
tol=10e-9, max_stagnation=20, return_errors=False, random_state=None, verbose=1):
"""Randomised CP decomposition via sampled ALS

Parameters
----------
tensor : ndarray
rank   : int
number of components
n_samples : int
number of samples per ALS step
n_iter_max : int
maximum number of iteration
init : {'svd', 'random'}, optional
svd : str, default is 'numpy_svd'
function to use to compute the SVD, acceptable values in tensorly.SVD_FUNS
tol : float, optional
tolerance: the algorithm stops when the variation in
the reconstruction error is less than the tolerance
max_stagnation: int, optional, default is 0
if not zero, the maximum allowed number
of iterations with no decrease in fit
random_state : {None, int, np.random.RandomState}, default is None
return_errors : bool, default is False
if True, return a list of all errors
verbose : int, optional
level of verbosity

Returns
-------
factors : ndarray list
list of positive factors of the CP decomposition
element `i` is of shape ``(tensor.shape[i], rank)``

References
----------
..  Casey Battaglino, Grey Ballard and Tamara G. Kolda,
"A Practical Randomized CP Tensor Decomposition",
"""
rank = validate_cp_rank(tl.shape(tensor), rank=rank)

rng = tl.check_random_state(random_state)
weights, factors = initialize_cp(tensor, rank, init=init, svd=svd, random_state=random_state)
rec_errors = []
n_dims = tl.ndim(tensor)
norm_tensor = tl.norm(tensor, 2)
min_error = 0

weights = tl.ones(rank, **tl.context(tensor))
for iteration in range(n_iter_max):
for mode in range(n_dims):
kr_prod, indices_list = sample_khatri_rao(factors, n_samples, skip_matrix=mode, random_state=rng)
indices_list = [i.tolist() for i in indices_list]
# Keep all the elements of the currently considered mode
indices_list.insert(mode, slice(None, None, None))
# MXNet will not be happy if this is a list insteaf of a tuple
indices_list = tuple(indices_list)
if mode:
sampled_unfolding = tensor[indices_list]
else:
sampled_unfolding = tl.transpose(tensor[indices_list])

pseudo_inverse = tl.dot(tl.transpose(kr_prod), kr_prod)
factor = tl.dot(tl.transpose(kr_prod), sampled_unfolding)
factor = tl.transpose(tl.solve(pseudo_inverse, factor))
factors[mode] = factor

if max_stagnation or tol:
rec_error = tl.norm(tensor - cp_to_tensor((weights, factors)), 2) / norm_tensor
if not min_error or rec_error < min_error:
min_error = rec_error
stagnation = -1
stagnation += 1

rec_errors.append(rec_error)

if iteration > 1:
if verbose:
print('reconstruction error={}, variation={}.'.format(
rec_errors[-1], rec_errors[-2] - rec_errors[-1]))

if (tol and abs(rec_errors[-2] - rec_errors[-1]) < tol) or \
(stagnation and (stagnation > max_stagnation)):
if verbose:
print('converged in {} iterations.'.format(iteration))
break

if return_errors:
return CPTensor((weights, factors)), rec_errors
else:
return CPTensor((weights, factors))

[docs]class CP(DecompositionMixin):
"""Candecomp-Parafac decomposition via Alternating-Least Square

Computes a rank-`rank` decomposition of `tensor` _ such that::

tensor = [|weights; factors, ..., factors[-1] |].

Parameters
----------
tensor : ndarray
rank  : int
Number of components.
n_iter_max : int
Maximum number of iteration
init : {'svd', 'random'}, optional
Type of factor matrix initialization. See `initialize_factors`.
svd : str, default is 'numpy_svd'
function to use to compute the SVD, acceptable values in tensorly.SVD_FUNS
normalize_factors : if True, aggregate the weights of each factor in a 1D-tensor
of shape (rank, ), which will contain the norms of the factors
tol : float, optional
(Default: 1e-6) Relative reconstruction error tolerance. The
algorithm is considered to have found the global minimum when the
reconstruction error is less than `tol`.
random_state : {None, int, np.random.RandomState}
verbose : int, optional
Level of verbosity
return_errors : bool, optional
Activate return of iteration errors
array of booleans with the same shape as ``tensor`` should be 0 where
the values are missing and 1 everywhere else. Note:  if tensor is
sparse, then mask should also be sparse with a fill value of 1 (or
True). Allows for missing values _
cvg_criterion : {'abs_rec_error', 'rec_error'}, optional
Stopping criterion for ALS, works if `tol` is not None.
If 'rec_error',  ALS stops at current iteration if ``(previous rec_error - current rec_error) < tol``.
If 'abs_rec_error', ALS terminates when `|previous rec_error - current rec_error| < tol`.
sparsity : float or int
If `sparsity` is not None, we approximate tensor as a sum of low_rank_component and sparse_component, where low_rank_component = cp_to_tensor((weights, factors)). `sparsity` denotes desired fraction or number of non-zero elements in the sparse_component of the `tensor`.
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.
If using a tensor with masked values, this initializes using SVD multiple times to
remove the effect of these missing values on the initialization.
linesearch : bool, default is False
Whether to perform line search as proposed by Bro .

Returns
-------
CPTensor : (weight, factors)
* weights : 1D array of shape (rank, )

* all ones if normalize_factors is False (default),
* weights of the (normalized) factors otherwise

* factors : List of factors of the CP decomposition element `i ` is of shape
(tensor.shape[i], rank)
* sparse_component : nD array of shape tensor.shape. Returns only if `sparsity` is not None.

errors : list
A list of reconstruction errors at each iteration of the algorithms.

References
----------
..  T.G.Kolda and B.W.Bader, "Tensor Decompositions and Applications",
SIAM REVIEW, vol. 51, n. 3, pp. 455-500, 2009.

..  Tomasi, Giorgio, and Rasmus Bro. "PARAFAC and missing values."
Chemometrics and Intelligent Laboratory Systems 75.2 (2005): 163-180.

..  R. Bro, "Multi-Way Analysis in the Food Industry: Models, Algorithms, and
Applications", PhD., University of Amsterdam, 1998
"""
def __init__(self, rank, n_iter_max=100, tol=1e-08,
init='svd', svd='numpy_svd',
l2_reg=0,
linesearch=False,
fixed_modes=None,
normalize_factors=False,
orthogonalise=False,
sparsity=None,
cvg_criterion='abs_rec_error',
random_state=None,
verbose=0):
self.rank = rank
self.n_iter_max = n_iter_max
self.tol = tol
self.l2_reg = l2_reg
self.init = init
self.linesearch = linesearch
self.svd = svd
self.normalize_factors = normalize_factors
self.orthogonalise = orthogonalise
self.cvg_criterion = cvg_criterion
self.random_state = random_state
self.verbose = verbose

[docs]    def fit_transform(self, tensor):
"""Decompose an input tensor

Parameters
----------
tensor : tensorly tensor
input tensor to decompose

Returns
-------
CPTensor
decomposed tensor
"""
cp_tensor, errors = parafac(tensor, rank=self.rank,
n_iter_max=self.n_iter_max,
tol=self.tol,
init=self.init,
svd=self.svd,
normalize_factors=self.normalize_factors,
orthogonalise=self.orthogonalise,
linesearch=self.linesearch,
cvg_criterion=self.cvg_criterion,
random_state=self.random_state,
verbose=self.verbose,
return_errors=True)
self.decomposition_ = cp_tensor
self.errors_ = errors
return self.decomposition_

def __repr__(self):
return f'Rank-{self.rank} CP decomposition.'

[docs]class RandomizedCP(DecompositionMixin):
"""Randomised CP decomposition via sampled ALS

Parameters
----------
tensor : ndarray
rank   : int
number of components
n_samples : int
number of samples per ALS step
n_iter_max : int
maximum number of iteration
init : {'svd', 'random'}, optional
svd : str, default is 'numpy_svd'
function to use to compute the SVD, acceptable values in tensorly.SVD_FUNS
tol : float, optional
tolerance: the algorithm stops when the variation in
the reconstruction error is less than the tolerance
max_stagnation: int, optional, default is 0
if not zero, the maximum allowed number
of iterations with no decrease in fit
random_state : {None, int, np.random.RandomState}, default is None
verbose : int, optional
level of verbosity

Returns
-------
factors : ndarray list
list of positive factors of the CP decomposition
element `i` is of shape ``(tensor.shape[i], rank)``

References
----------
..  Casey Battaglino, Grey Ballard and Tamara G. Kolda,
"A Practical Randomized CP Tensor Decomposition",
"""
def __init__(self, rank, n_samples, n_iter_max=100, init='random', svd='numpy_svd',
tol=10e-9, max_stagnation=20, random_state=None, verbose=1):
self.rank = rank
self.n_samples = n_samples
self.n_iter_max = n_iter_max
self.init = init
self.svd = svd
self.tol = tol
self.max_stagnation = max_stagnation
self.random_state = random_state
self.verbose = verbose

def fit_transform(self, tensor):
self.decomposition_, self.errors_ = randomised_parafac(tensor, rank=self.rank, n_samples=self.n_samples,
n_iter_max=self.n_iter_max, init=self.init, svd=self.svd, tol=self.tol, return_errors=True,
max_stagnation=self.max_stagnation, random_state=self.random_state, verbose=self.verbose)
return self.decomposition_
```