nimfa 1.2.3

Nimfa

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-like IPython notebook on how one can 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 appearead in the ACM XRDS magazine.

Usage

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

Citing

@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}
}

License

nimfa - A Python Library for Nonnegative Matrix Factorization Techniques Copyright (C) 2011-2015 Marinka Zitnik and Blaz Zupan

This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or any later version.

This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.

JMLR Warranty

THIS SOURCE CODE IS SUPPLIED “AS IS” WITHOUT WARRANTY OF ANY KIND, AND ITS AUTHOR AND THE JOURNAL OF MACHINE LEARNING RESEARCH (JMLR) AND JMLR’S PUBLISHERS AND DISTRIBUTORS, DISCLAIM ANY AND ALL WARRANTIES, INCLUDING BUT NOT LIMITED TO ANY IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE, AND ANY WARRANTIES OR NON INFRINGEMENT. THE USER ASSUMES ALL LIABILITY AND RESPONSIBILITY FOR USE OF THIS SOURCE CODE, AND NEITHER THE AUTHOR NOR JMLR, NOR JMLR’S PUBLISHERS AND DISTRIBUTORS, WILL BE LIABLE FOR DAMAGES OF ANY KIND RESULTING FROM ITS USE.

Without limiting the generality of the foregoing, neither the author, nor JMLR, nor JMLR’s publishers and distributors, warrant that the Source Code will be error-free, will operate without interruption, or will meet the needs of the user.