sklearn.decomposition
.ProjectedGradientNMF¶
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class
sklearn.decomposition.
ProjectedGradientNMF
(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]¶
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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 :
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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
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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.
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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.Returns: self :
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