Published May 28, 2021
| Version 1.0.0
Software
Open
pyCP_APR
Creators
- 1. Advanced Research in Cyber Systems, Los Alamos National Laboratory
- 2. Computer, Computational, and Statistical Sciences Division, Los Alamos National Laboratory
- 3. Theoretical Division, Los Alamos National Laboratory
- 4. Chennupati: Computer, Computational, and Statistical Sciences Division, Los Alamos National Laboratory
Description
pyCP_APR is a Python library for tensor decomposition and anomaly detection. It is designed for the fast analysis of large datasets by accelerating computation speed using GPUs. pyCP_APR uses the CANDECOMP/PARAFAC Alternating Poisson Regression (CP-APR) tensor factorization algorithm utilizing both Numpy and PyTorch backend. While the Numpy backend can be used for the analysis of both sparse and dense tensors, PyTorch backend provides faster decomposition of large and sparse tensors on the GPU. pyCP_APR's Scikit-learn like API allows comfortable interaction with the library, and include the methods for anomaly detection via the p-values obtained from the CP-APR factorization.
Files
pyCP_APR-main.zip
Files
(3.2 MB)
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md5:9e5b1f10ecae7b88c5fb6c598a8bd4bf
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Additional details
Related works
- Is supplement to
- Software: https://github.com/lanl/pyCP_APR (URL)
References
- General software, latest release: Brett W. Bader, Tamara G. Kolda and others, Tensor Toolbox for MATLAB, Version 3.2.1, www.tensortoolbox.org, April 5, 2021.
- Dense tensors: B. W. Bader and T. G. Kolda, Algorithm 862: MATLAB Tensor Classes for Fast Algorithm Prototyping, ACM Trans. Mathematical Software, 32(4):635-653, 2006, http://dx.doi.org/10.1145/1186785.1186794.
- Sparse, Kruskal, and Tucker tensors: B. W. Bader and T. G. Kolda, Efficient MATLAB Computations with Sparse and Factored Tensors, SIAM J. Scientific Computing, 30(1):205-231, 2007, http://dx.doi.org/10.1137/060676489.
- Chi, E.C. and Kolda, T.G., 2012. On tensors, sparsity, and nonnegative factorizations. SIAM Journal on Matrix Analysis and Applications, 33(4), pp.1272-1299.
- M. E. Eren, J. S. Moore and B. S. Alexandrov, "Multi-Dimensional Anomalous Entity Detection via Poisson Tensor Factorization," 2020 IEEE International Conference on Intelligence and Security Informatics (ISI), 2020, pp. 1-6, doi: 10.1109/ISI49825.2020.9280524.
- M. Turcotte, A. Kent and C. Hash, "Unified Host and Network Data Set", in Data Science for Cyber-Security. November 2018, 1-22.