https://github.com/sandialabs/xpca
Implementation of XPCA in Python (https://arxiv.org/abs/1808.07510)
https://github.com/sandialabs/xpca
scr-2407 snl-comp-science-libs snl-data-analysis
Last synced: over 1 year ago
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Implementation of XPCA in Python (https://arxiv.org/abs/1808.07510)
- Host: GitHub
- URL: https://github.com/sandialabs/xpca
- Owner: sandialabs
- License: other
- Created: 2023-06-26T16:50:23.000Z (about 3 years ago)
- Default Branch: main
- Last Pushed: 2023-06-26T16:50:35.000Z (about 3 years ago)
- Last Synced: 2025-01-11T15:25:10.581Z (over 1 year ago)
- Topics: scr-2407, snl-comp-science-libs, snl-data-analysis
- Language: Python
- Homepage:
- Size: 21.5 KB
- Stars: 2
- Watchers: 2
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE.txt
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README
## Table of Contents
* [Background](#background)
* [Install](#install)
* [Example](#example)
## Background
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](https://gitlab.com/xpca/xpcar)
XPCA implements 3 capabilities: xpca, pca, and coca.
### coca
`coca` is from work done by:
1. 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.
2. 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
`xpca` is Sandia-built work done by Cliff Anderson-Bergman, Tamara
Kolda, and Kina Kincher-Winoto. Paper is available on arXiv:
[C. Anderson-Bergman, T. G. Kolda, K. Kincher-Winoto. XPCA: Extending PCA for a
Combination of Discrete and Continuous Variables. arXiv:1808.07510,
2018.](https://arxiv.org/abs/1808.07510)
### pca
`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.
## Install
`xpcapy` requires:
* Python 3 (tested on 3.6)
* [NumPy](http://scipy.org/index.html) 1.11.0
* [SciPy](http://scipy.org/index.html) 0.17.1
These can be installed by running the following from the command line:
```bash
$ pip install numpy
$ pip install scipy
```
To install `xpcapy`:
Download this repo and run the following command from the terminal:
```bash
$ cd /path/to/xpcapy
$ python setup.py install
```
## Example
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.
## Questions?
Email xpca@sandia.gov if you have any questions.