Nimfa is a Python module that implements many algorithms for nonnegative matrix factorization.
Documentation and examples are at Nimfa website.
Hidden patients and hidden genes - Understanding cancer data with matrix factorization is a tutorial on how one to use Nimfa to analyze breast cancer transcriptome data sets from The International Cancer Genome Consortium (ICGC). A column about the analysis of cancer data using Nimfa recently appeared in the ACM XRDS magazine.
Run alternating least squares nonnegative matrix factorization with projected gradients and Random Vcol initialization algorithm on medulloblastoma gene expression data:
>>> import nimfa
>>> V = nimfa.examples.medulloblastoma.read(normalize=True)
>>> lsnmf = nimfa.Lsnmf(V, seed='random_vcol', rank=50, max_iter=100)
>>> lsnmf_fit = lsnmf()
>>> print('Rss: %5.4f' % lsnmf_fit.fit.rss())
Rss: 0.2668
>>> print('Evar: %5.4f' % lsnmf_fit.fit.evar())
Evar: 0.9997
>>> print('K-L divergence: %5.4f' % lsnmf_fit.distance(metric='kl'))
K-L divergence: 38.8744
>>> print('Sparseness, W: %5.4f, H: %5.4f' % lsnmf_fit.fit.sparseness())
Sparseness, W: 0.7297, H: 0.8796
@article{Zitnik2012,
title = {Nimfa: A Python Library for Nonnegative Matrix Factorization},
author = {{\v{Z}}itnik, Marinka and Zupan, Bla{\v{z}}},
journal = {Journal of Machine Learning Research},
volume = {13},
pages = {849-853},
year = {2012}
}
nimfa - A Python Library for Nonnegative Matrix Factorization Techniques Copyright (C) 2011-2015 Marinka Zitnik and Blaz Zupan
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