XPCA factors an observed data matrix X (m rows x n columns) into two factors, A (m x k) and B (n x k) where rank = k. Each row in X corresponds to an observed element, and each column corresponds to a feature of the observed element. These columns can be of mixed variable types - continuous, count, binary, etc.
xpcapy is the python implementation of xpca. There is also an R version of
this code available here
XPCA implements 3 capabilities: xpca, pca, and coca.
coca is from work done by:
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Fang Han and Han Liu. Semiparametric principal component analysis. In NIPS’12: Proceedings of the 26th Annual Conference on Neural Information Pro- cessing Systems, pages 171–179, 2012. URL http://papers.nips.cc/paper/4809-semiparametric-principal-component-analysis.
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Bernhard Egger, Dinu Kaufmann, Sandro Sch ̈onborn, Volker Roth, and Thomas Vetter. Copula eigenfaces — semiparametric principal component analysis for facial appearance modeling. In VISIGRAPP’16: Proceedings of the 11th Joint Conference on Computer Vi- sion, Imaging and Computer Graphics Theory and Applications, pages 50–58. SciTePress, 2016. 10.5220/0005718800480056.
xpca is Sandia-built work done by Cliff Anderson-Bergman, Tamara
Kolda, and Kina Kincher-Winoto. Paper is available on arXiv:
pca is the well-known algorithm. For reference:
Michael E. Tipping and Christopher M. Bishop. Probabilistic principal component analysis. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 61(3):611– 622, August 1999. 10.1111/1467-9868.00196.
xpcapy requires:
These can be installed by running the following from the command line:
$ pip install numpy
$ pip install scipy To install xpcapy:
Download this repo and run the following command from the terminal:
$ cd /path/to/xpcapy
$ python setup.py install Refer to example.py for an example as to how to simulate data and read in a
file. This script will run xpca and save off theta and final fitted matrix to a
file.
Usage:
# Just run it to simulate data
$ python3 example.py
# Or to read in a file of your own
$ python3 example.py --filename example_data.csv --rank 3
Both variations of
the run (simulation or reading in your own data) will output a theta.csv and
fitted.csv
The included example_data.csv is an exemplar data set since it includes
continuous, binary, and count data.
Email xpca@sandia.gov if you have any questions.