Sklearn matrix factorization

Path	pimlico.modules.sklearn.matrix_factorization
Executable	yes

Provides a simple interface to Scikit-Learn’s various matrix factorization models.

Since they provide a consistent training interface, you can simply choose the class name of the method you want to use and specify options relevant to that method in the options option. For available options, take a look at the table of parameters in the Scikit-Learn documentation for each class.

Inputs

Name	Type(s)
matrix	ScipySparseMatrix

Outputs

Name	Type(s)
w	NumpyArray
h	NumpyArray

Options

Name	Description	Type
class	(required) Scikit-learn class to use to fit the matrix factorization. Should be the name of a class in the package sklearn.decomposition that has a fit_transform() method and a components_ attribute. Supported classes: NMF, SparsePCA, ProjectedGradientNMF, FastICA, FactorAnalysis, PCA, RandomizedPCA, LatentDirichletAllocation, TruncatedSVD	‘NMF’, ‘SparsePCA’, ‘ProjectedGradientNMF’, ‘FastICA’, ‘FactorAnalysis’, ‘PCA’, ‘RandomizedPCA’, ‘LatentDirichletAllocation’ or ‘TruncatedSVD’
options	Options to pass into the constructor of the sklearn class, formatted as a JSON dictionary (potentially without the {}s). E.g.: ‘n_components=200, solver=”cd”, tol=0.0001, max_iter=200’	string