def _update_coordinate_descent(X, W, Ht, l1_reg, l2_reg, shuffle,
random_state):
"""Helper function for _fit_coordinate_descent
Update W to minimize the objective function, iterating once over all
coordinates. By symmetry, to update H, one can call
_update_coordinate_descent(X.T, Ht, W, ...)
"""
n_components = Ht.shape[1]
HHt = fast_dot(Ht.T, Ht)
XHt = safe_sparse_dot(X, Ht)
# L2 regularization corresponds to increase of the diagonal of HHt
if l2_reg != 0.:
# adds l2_reg only on the diagonal
HHt.flat[::n_components + 1] += l2_reg
# L1 regularization corresponds to decrease of each element of XHt
if l1_reg != 0.:
XHt -= l1_reg
if shuffle:
permutation = random_state.permutation(n_components)
else:
permutation = np.arange(n_components)
# The following seems to be required on 64-bit Windows w/ Python 3.5.
permutation = np.asarray(permutation, dtype=np.intp)
return _update_cdnmf_fast(W, HHt, XHt, permutation)
def _update_coordinate_descent(X, W, Ht, l1_reg, l2_reg, shuffle,
random_state):
"""Helper function for _fit_coordinate_descent
Update W to minimize the objective function, iterating once over all
coordinates. By symmetry, to update H, one can call
_update_coordinate_descent(X.T, Ht, W, ...)
"""
n_components = Ht.shape[1]
HHt = fast_dot(Ht.T, Ht)
XHt = safe_sparse_dot(X, Ht)
# L2 regularization corresponds to increase of the diagonal of HHt
if l2_reg != 0.:
# adds l2_reg only on the diagonal
HHt.flat[::n_components + 1] += l2_reg
# L1 regularization corresponds to decrease of each element of XHt
if l1_reg != 0.:
XHt -= l1_reg
if shuffle:
permutation = random_state.permutation(n_components)
else:
permutation = np.arange(n_components)
# The following seems to be required on 64-bit Windows w/ Python 3.5.
permutation = np.asarray(permutation, dtype=np.intp)
return _update_cdnmf_fast(W, HHt, XHt, permutation)
def nmf_iteration_update(self, X, W, H, l1_reg, l2_reg, shuffle):
n_components = H.shape[1]
HHt = np.dot(H.T, H)
XHt = np.dot(X, H)
# L2 regularization corresponds to increase of the diagonal of HHt
if l2_reg != 0.:
# adds l2_reg only on the diagonal
HHt.flat[::n_components + 1] += l2_reg
# L1 regularization corresponds to decrease of each element of XHt
if l1_reg != 0.:
XHt -= l1_reg
if shuffle:
permutation = np.random.permutation(n_components)
else:
permutation = np.arange(n_components)
# The following seems to be required on 64-bit Windows w/ Python 3.5.
permutation = np.asarray(permutation, dtype=np.intp)
return _update_cdnmf_fast(W, HHt, XHt, permutation)
def compute_nmf(A, rank, init='nndsvd', shuffle=False,
l2_reg_H=0.0, l2_reg_W=0.0, l1_reg_H=0.0, l1_reg_W=0.0,
tol=1e-5, maxiter=200, random_state=None):
"""Nonnegative Matrix Factorization.
Hierarchical alternating least squares algorithm
for computing the approximate low-rank nonnegative matrix factorization of
a rectangular `(m, n)` matrix `A`. Given the target rank `rank << min{m,n}`,
the input matrix `A` is factored as `A = W H`. The nonnegative factor
matrices `W` and `H` are of dimension `(m, rank)` and `(rank, n)`, respectively.
Parameters
----------
A : array_like, shape `(m, n)`.
Real nonnegative input matrix.
rank : integer, `rank << min{m,n}`.
Target rank.
init : 'random' | 'nndsvd' | 'nndsvda' | 'nndsvdar'
Method used to initialize the procedure. Default: 'nndsvd'.
Valid options:
- 'random': non-negative random matrices, scaled with:
sqrt(X.mean() / n_components)
- 'nndsvd': Nonnegative Double Singular Value Decomposition (NNDSVD)
initialization (better for sparseness)
- 'nndsvda': NNDSVD with zeros filled with the average of X
(better when sparsity is not desired)
- 'nndsvdar': NNDSVD with zeros filled with small random values
(generally faster, less accurate alternative to NNDSVDa
for when sparsity is not desired)
shuffle : boolean, default: False
If true, randomly shuffle the update order of the variables.
l2_reg_H : float, (default ``l2_reg_H = 0.1``).
Amount of ridge shrinkage to apply to `H` to improve conditionin.
l2_reg_W : float, (default ``l2_reg_W = 0.1``).
Amount of ridge shrinkage to apply to `W` to improve conditionin.
l1_reg_H : float, (default ``l1_reg_H = 0.0``).
Sparsity controlling parameter on `H`.
Higher values lead to sparser components.
l1_reg_W : float, (default ``l1_reg_W = 0.0``).
Sparsity controlling parameter on `W`.
Higher values lead to sparser components.
tol : float, default: `tol=1e-4`.
Tolerance of the stopping condition.
maxiter : integer, default: `maxiter=100`.
Number of iterations.
random_state : integer, RandomState instance or None, optional (default ``None``)
If integer, random_state is the seed used by the random number generator;
If RandomState instance, random_state is the random number generator;
If None, the random number generator is the RandomState instance used by np.random.
verbose : boolean, default: `verbose=False`.
The verbosity level.
Returns
-------
W: array_like, `(m, rank)`.
Solution to the non-negative least squares problem.
H : array_like, `(rank, n)`.
Solution to the non-negative least squares problem.
Notes
-----
This HALS update algorithm written in cython is adapted from the
scikit-learn implementation for the deterministic NMF. We also have
adapted the initilization scheme.
See: https://github.com/scikit-learn/scikit-learn
References
----------
[1] Cichocki, Andrzej, and P. H. A. N. Anh-Huy. "Fast local algorithms for
large scale nonnegative matrix and tensor factorizations."
IEICE transactions on fundamentals of electronics, communications and
computer sciences 92.3: 708-721, 2009.
[2] C. Boutsidis, E. Gallopoulos: SVD based initialization: A head start for
nonnegative matrix factorization - Pattern Recognition, 2008
http://tinyurl.com/nndsvd
Examples
--------
>>> import numpy as np
>>> X = np.array([[1,1], [2, 1], [3, 1.2], [4, 1], [5, 0.8], [6, 1]])
>>> import ristretto as ro
>>> W, H = ro.nmf(X, rank=2)
"""
random_state = check_random_state(random_state)
# converts A to array, raise ValueError if A has inf or nan
A = np.asarray_chkfinite(A)
m, n = A.shape
if np.any(A < 0):
raise ValueError("Input matrix with nonnegative elements is required.")
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# Initialization methods for factor matrices W and H
# 'normal': nonnegative standard normal random init
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
W, H = _initialize_nmf(A, rank, init=init, eps=1e-6, random_state=random_state)
Ht = np.array(H.T, order='C')
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# Iterate the HALS algorithm until convergence or maxiter is reached
# i) Update factor matrix H and normalize columns
# ii) Update low-dimensional factor matrix W
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
violation = 0.0
for niter in range(maxiter):
# Update factor matrix H with regularization
WtW = W.T.dot(W)
WtW.flat[::rank + 1] += l2_reg_H # adds l2_reg only on the diagonal
AtW = A.T.dot(W) - l1_reg_H
# compute violation update
permutation = random_state.permutation(rank) if shuffle else np.arange(rank)
violation = _update_cdnmf_fast(Ht, WtW, AtW, permutation)
# Update factor matrix W with regularization
HHt = Ht.T.dot(Ht)
HHt.flat[::rank + 1] += l2_reg_W # adds l2_reg only on the diagonal
AHt = A.dot(Ht) - l1_reg_W
# compute violation update
permutation = random_state.permutation(rank) if shuffle else np.arange(rank)
violation += _update_cdnmf_fast(W, HHt, AHt, permutation)
# Compute stopping condition.
if niter == 0:
if violation == 0: break
violation_init = violation
if violation / violation_init <= tol:
break
# Return factor matrices
return W, Ht.T
def compute_rnmf(A, rank, oversample=20, n_subspace=2, init='nndsvd', shuffle=False,
l2_reg_H=0.0, l2_reg_W=0.0, l1_reg_H=0.0, l1_reg_W=0.0,
tol=1e-5, maxiter=200, random_state=None):
"""
Randomized Nonnegative Matrix Factorization.
Randomized hierarchical alternating least squares algorithm
for computing the approximate low-rank nonnegative matrix factorization of
a rectangular `(m, n)` matrix `A`. Given the target rank `rank << min{m,n}`,
the input matrix `A` is factored as `A = W H`. The nonnegative factor
matrices `W` and `H` are of dimension `(m, rank)` and `(rank, n)`, respectively.
The quality of the approximation can be controlled via the oversampling
parameter `oversample` and the parameter `n_subspace` which specifies the number of
subspace iterations.
Parameters
----------
A : array_like, shape `(m, n)`.
Real nonnegative input matrix.
rank : integer, `rank << min{m,n}`.
Target rank, i.e., number of components to extract from the data
oversample : integer, optional (default: 10)
Controls the oversampling of column space. Increasing this parameter
may improve numerical accuracy.
n_subspace : integer, default: 2.
Parameter to control number of subspace iterations. Increasing this
parameter may improve numerical accuracy.
init : 'random' | 'nndsvd' | 'nndsvda' | 'nndsvdar'
Method used to initialize the procedure. Default: 'nndsvd'.
Valid options:
- 'random': non-negative random matrices, scaled with:
sqrt(X.mean() / n_components)
- 'nndsvd': Nonnegative Double Singular Value Decomposition (NNDSVD)
initialization (better for sparseness)
- 'nndsvda': NNDSVD with zeros filled with the average of X
(better when sparsity is not desired)
- 'nndsvdar': NNDSVD with zeros filled with small random values
(generally faster, less accurate alternative to NNDSVDa
for when sparsity is not desired)
shuffle : boolean, default: False
If true, randomly shuffle the update order of the variables.
l2_reg_H : float, (default ``l2_reg_H = 0.1``).
Amount of ridge shrinkage to apply to `H` to improve conditioning.
l2_reg_W : float, (default ``l2_reg_W = 0.1``).
Amount of ridge shrinkage to apply to `W` to improve conditioning.
l1_reg_H : float, (default ``l1_reg_H = 0.0``).
Sparsity controlling parameter on `H`.
Higher values lead to sparser components.
l1_reg_W : float, (default ``l1_reg_W = 0.0``).
Sparsity controlling parameter on `W`.
Higher values lead to sparser components.
tol : float, default: `tol=1e-5`.
Tolerance of the stopping condition.
maxiter : integer, default: `maxiter=200`.
Number of iterations.
random_state : integer, RandomState instance or None, optional (default ``None``)
If integer, random_state is the seed used by the random number generator;
If RandomState instance, random_state is the random number generator;
If None, the random number generator is the RandomState instance used by np.random.
verbose : boolean, default: `verbose=False`.
The verbosity level.
Returns
-------
W: array_like, `(m, rank)`.
Solution to the non-negative least squares problem.
H : array_like, `(rank, n)`.
Solution to the non-negative least squares problem.
Notes
-----
This HALS update algorithm written in cython is adapted from the
scikit-learn implementation for the deterministic NMF. We also have
adapted the initilization scheme.
See: https://github.com/scikit-learn/scikit-learn
References
----------
[1] Erichson, N. Benjamin, Ariana Mendible, Sophie Wihlborn, and J. Nathan Kutz.
"Randomized Nonnegative Matrix Factorization."
Pattern Recognition Letters (2018).
[2] Cichocki, Andrzej, and P. H. A. N. Anh-Huy. "Fast local algorithms for
large scale nonnegative matrix and tensor factorizations."
IEICE transactions on fundamentals of electronics, communications and
computer sciences 92.3: 708-721, 2009.
[3] C. Boutsidis, E. Gallopoulos: SVD based initialization: A head start for
nonnegative matrix factorization - Pattern Recognition, 2008
http://tinyurl.com/nndsvd
Examples
--------
>>> import numpy as np
>>> X = np.array([[1,1], [2, 1], [3, 1.2], [4, 1], [5, 0.8], [6, 1]])
>>> import ristretto as ro
>>> W, H = ro.rnmf(X, rank=2, oversample=0)
"""
random_state = check_random_state(random_state)
# converts A to array, raise ValueError if A has inf or nan
A = np.asarray_chkfinite(A)
m, n = A.shape
flipped = False
if n > m:
A = A.T
m, n = A.shape
flipped = True
if A.dtype not in _VALID_DTYPES:
raise ValueError('A.dtype must be one of %s, not %s'
% (' '.join(_VALID_DTYPES), A.dtype))
if np.any(A < 0):
raise ValueError("Input matrix with nonnegative elements is required.")
Q, B = compute_rqb(A, rank, oversample=oversample,
n_subspace=n_subspace, random_state=random_state)
# Initialization methods for factor matrices W and H
W, H = _initialize_nmf(A, rank, init=init, eps=1e-6, random_state=random_state)
Ht = np.array(H.T, order='C')
W_tilde = Q.T.dot(W)
del A
# Iterate the HALS algorithm until convergence or maxiter is reached
violation = 0.0
for niter in range(maxiter):
# Update factor matrix H
WtW = W.T.dot(W)
WtW.flat[::rank + 1] += l2_reg_H # adds l2_reg only on the diagonal
BtW = B.T.dot(W_tilde) - l1_reg_H
# compute violation update
permutation = random_state.permutation(rank) if shuffle else np.arange(rank)
violation = _update_cdnmf_fast(Ht, WtW, BtW, permutation)
# Update factor matrix W
HHt = Ht.T.dot(Ht)
HHt.flat[::rank + 1] += l2_reg_W # adds l2_reg only on the diagonal
# Rotate AHt back to high-dimensional space
BHt = Q.dot(B.dot(Ht)) - l1_reg_W
# compute violation update
permutation = random_state.permutation(rank) if shuffle else np.arange(rank)
violation += _update_cdnmf_fast(W, HHt, BHt, permutation)
# Project W to low-dimensional space
W_tilde = Q.T.dot(W)
# Compute stopping condition.
if niter == 0:
if violation == 0: break
violation_init = violation
if violation / violation_init <= tol:
break
# Return factor matrices
if flipped:
return(Ht, W.T)
return(W, Ht.T)