`sklearn.decomposition`.NMF¶

class sklearn.decomposition.NMF(n_components=None, init=None, sparseness=None, beta=1, eta=0.1, tol=0.0001, max_iter=200, nls_max_iter=2000, random_state=None)[source]¶

Non-Negative matrix factorization by Projected Gradient (NMF)

Read more in the User Guide.

Parameters:

n_components : int or None

Number of components, if n_components is not set all components are kept

init : ‘nndsvd’ | ‘nndsvda’ | ‘nndsvdar’ | ‘random’

Method used to initialize the procedure. Default: ‘nndsvdar’ if n_components < n_features, otherwise random. Valid options:

'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)
'random': non-negative random matrices

sparseness : ‘data’ | ‘components’ | None, default: None

Where to enforce sparsity in the model.

beta : double, default: 1

Degree of sparseness, if sparseness is not None. Larger values mean more sparseness.

eta : double, default: 0.1

Degree of correctness to maintain, if sparsity is not None. Smaller values mean larger error.

tol : double, default: 1e-4

Tolerance value used in stopping conditions.

max_iter : int, default: 200

Number of iterations to compute.

nls_max_iter : int, default: 2000

Number of iterations in NLS subproblem.

random_state : int or RandomState

Random number generator seed control.

Attributes:

components_ : array, [n_components, n_features]

Non-negative components of the data.

reconstruction_err_ : number

Frobenius norm of the matrix difference between the training data and the reconstructed data from the fit produced by the model. || X - WH ||_2

n_iter_ : int

Number of iterations run.

References

This implements

C.-J. Lin. Projected gradient methods for non-negative matrix factorization. Neural Computation, 19(2007), 2756-2779. http://www.csie.ntu.edu.tw/~cjlin/nmf/

P. Hoyer. Non-negative Matrix Factorization with Sparseness Constraints. Journal of Machine Learning Research 2004.

NNDSVD is introduced in

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]])
>>> from sklearn.decomposition import ProjectedGradientNMF
>>> model = ProjectedGradientNMF(n_components=2, init='random',
...                              random_state=0)
>>> model.fit(X) 
ProjectedGradientNMF(beta=1, eta=0.1, init='random', max_iter=200,
        n_components=2, nls_max_iter=2000, random_state=0, sparseness=None,
        tol=0.0001)
>>> model.components_
array([[ 0.77032744,  0.11118662],
       [ 0.38526873,  0.38228063]])
>>> model.reconstruction_err_ 
0.00746...
>>> model = ProjectedGradientNMF(n_components=2,
...              sparseness='components', init='random', random_state=0)
>>> model.fit(X) 
ProjectedGradientNMF(beta=1, eta=0.1, init='random', max_iter=200,
            n_components=2, nls_max_iter=2000, random_state=0,
            sparseness='components', tol=0.0001)
>>> model.components_
array([[ 1.67481991,  0.29614922],
       [ 0.        ,  0.4681982 ]])
>>> model.reconstruction_err_ 
0.513...

Methods

`fit`(X[, y])	Learn a NMF model for the data X.
`fit_transform`(X[, y])	Learn a NMF model for the data X and returns the transformed data.
`get_params`([deep])	Get parameters for this estimator.
`set_params`(**params)	Set the parameters of this estimator.
`transform`(X)	Transform the data X according to the fitted NMF model

__init__(n_components=None, init=None, sparseness=None, beta=1, eta=0.1, tol=0.0001, max_iter=200, nls_max_iter=2000, random_state=None)[source]¶

fit(X, y=None, **params)[source]¶

Learn a NMF model for the data X.

Parameters:

X: {array-like, sparse matrix}, shape = [n_samples, n_features] :

Data matrix to be decomposed

Returns:

self :

fit_transform(X, y=None)[source]¶

Learn a NMF model for the data X and returns the transformed data.

This is more efficient than calling fit followed by transform.

Parameters:

X: {array-like, sparse matrix}, shape = [n_samples, n_features] :

Data matrix to be decomposed

Returns:

data: array, [n_samples, n_components] :

Transformed data

get_params(deep=True)[source]¶

Get parameters for this estimator.

Parameters:

deep: boolean, optional :

If True, will return the parameters for this estimator and contained subobjects that are estimators.

Returns:

params : mapping of string to any

Parameter names mapped to their values.

set_params(**params)[source]¶

Set the parameters of this estimator.

The method works on simple estimators as well as on nested objects (such as pipelines). The former have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.