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https://github.com/lanl/pycp_apr

CP-APR Tensor Decomposition with PyTorch backend. pyCP_APR can perform non-negative Poisson Tensor Factorization on GPU, and includes an interface for anomaly detection using the extracted latent patterns.
https://github.com/lanl/pycp_apr

anomaly-detection candecomp-parafac canonical-polyadic cpd cybersecurity dense dense-tensors gpu latent-features non-negative-tensor-factorization numpy poisson-distribution pytorch sparse sparse-tensors tensor-decomposition tensor-factorization tensors

Last synced: about 2 months ago
JSON representation

Host: GitHub
URL: https://github.com/lanl/pycp_apr
Owner: lanl
Created: 2021-05-29T00:55:33.000Z (over 3 years ago)
Default Branch: main
Last Pushed: 2023-12-18T23:23:21.000Z (about 1 year ago)
Last Synced: 2024-06-27T00:19:22.158Z (7 months ago)
Topics: anomaly-detection, candecomp-parafac, canonical-polyadic, cpd, cybersecurity, dense, dense-tensors, gpu, latent-features, non-negative-tensor-factorization, numpy, poisson-distribution, pytorch, sparse, sparse-tensors, tensor-decomposition, tensor-factorization, tensors
Language: Python
Homepage: https://lanl.github.io/pyCP_APR/
Size: 5.89 MB
Stars: 11
Watchers: 5
Forks: 6
Open Issues: 0
Metadata Files:
- Readme: README.md
- License: LICENSE

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README

        # pyCP_APR



[![Build Status](https://github.com/lanl/pyCP_APR/actions/workflows/ci_tests.yml/badge.svg?branch=main)](https://github.com/lanl/pyCP_APR/actions/workflows/ci_tests.yml/badge.svg?branch=main) [![License](https://img.shields.io/badge/License-BSD%203--Clause-blue.svg)](https://img.shields.io/badge/License-BSD%203--Clause-blue.svg) [![Python Version](https://img.shields.io/badge/python-v3.9-blue)](https://img.shields.io/badge/python-v3.8.5-blue) [![DOI](https://img.shields.io/badge/DOI-10.5281%2Fzenodo.4840598-blue.svg)](https://doi.org/10.5281/zenodo.4840598)





  



**pyCP_APR** is a Python library for tensor decomposition and anomaly detection that is developed as part of the R&D 100 award wining **[SmartTensors AI](https://smart-tensors.lanl.gov/software/)** project. 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. The anomaly detection methods via the p-values optained from CP-APR was introduced by Eren et al. in [6] using the [Unified Host and Network Dataset](https://csr.lanl.gov/data/2017/) [5]. Our work follows the [MATLAB Tensor Toolbox](https://www.tensortoolbox.org/cp.html) [1-3] implementation of CP-APR [4].



### [:information_source: Documentation](https://lanl.github.io/pyCP_APR/)   [:orange_book: Example Notebooks](examples/)   [:bar_chart: Datasets](data/tensors) 

  

### [:page_facing_up: Paper 1](https://ieeexplore.ieee.org/abstract/document/9280524)   [:page_facing_up: Paper 2](https://dl.acm.org/doi/abs/10.1145/3519602)

    

### [:link: Website](https://smart-tensors.LANL.gov)



## Installation

#### Option 1: Install using *pip*

```shell

pip install git+https://github.com/lanl/pyCP_APR.git

```

#### Option 2: Install from source

```shell

git clone https://github.com/lanl/pyCP_APR.git

cd pyCP_APR

conda create --name pyCP_APR python=3.9

source activate pyCP_APR

pip install -e . # or 

```

#### Jupyter Setup Tutorial for using the examples ([Link](https://www.maksimeren.com/post/conda-and-jupyter-setup-for-research/))

## Example Usage

```python

from pyCP_APR import CP_APR

from pyCP_APR.datasets import load_dataset

# Load a sample tensor

data = load_dataset(name="TOY")

# Training and test tensor in COO format

# Non-zero coordinates and corresponding non-zero values

coords_train, nnz_train = data['train_coords'], data['train_count']

coords_test, nnz_test = data['test_coords'], data['test_binary']

# CP-APR Object with PyTorch backend on a GPU. 

# Transfer the latent factors back to Numpy arrays.

model = CP_APR(n_iters=10,

               random_state=42,

               verbose=1,

               method='torch',

               device='gpu',

               return_type='numpy')

# Take rank 45 decomposition

M = model.fit(coords=coords_train, values=nnz_train, rank=45)

# Predict the scores over the trained tensor

y_score = model.predict_scores(coords=coords_test, values=nnz_test)

```

**See the [examples](examples/) for more.**

## How to Cite pyCP_APR?

If you use pyCP_APR please cite the [original paper](https://doi.org/10.1109/ISI49825.2020.9280524) that introduces our anomaly detection framework, and the [follow-up paper](https://doi.org/10.1145/3519602) that generalizes the method to number of other anomaly detection problems and introduces the library alongside new ensemble based extension of our anomaly detection method:

```latex

@article{10.1145/3519602,

  author = {Eren, Maksim E. and Moore, Juston S. and Skau, Erik and Moore, Elisabeth and Bhattarai, Manish and Chennupati, Gopinath and Alexandrov, Boian S.},

  title = {General-Purpose Unsupervised Cyber Anomaly Detection via Non-Negative Tensor Factorization},

  year = {2022},

  publisher = {Association for Computing Machinery},

  address = {New York, NY, USA},

  issn = {2692-1626},

  url = {https://doi.org/10.1145/3519602},

  doi = {10.1145/3519602},

  note = {Just Accepted},

  journal = {Digital Threats},

  month = {feb},

  keywords = {malware, anomaly detection, CPD, ensemble learning, non-negative tensor factorization, data fusion, GPU, Poisson tensor factorization, cyber security, unsupervised learning}

}

@INPROCEEDINGS{Eren2020ISI,

  author={M. E. {Eren} and J. S. {Moore} and B. S. {Alexandrov}},

  booktitle={2020 IEEE International Conference on Intelligence and Security Informatics (ISI)},

  title={Multi-Dimensional Anomalous Entity Detection via Poisson Tensor Factorization},

  year={2020},

  pages={1-6},

  doi={10.1109/ISI49825.2020.9280524}

}

@MISC{Eren2021pyCPAPR,

  author = {M. E. {Eren} and J. S. {Moore} and E. {Skau} and M. {Bhattarai} and G. {Chennupati} and B. S. {Alexandrov}},

  title = {pyCP\_APR},

  year = {2021},

  publisher = {GitHub},

  journal = {GitHub repository},

  doi = {10.5281/zenodo.4840598},

  howpublished = {\url{https://github.com/lanl/pyCP\_APR}}

}

```

## Authors

- [Maksim Ekin Eren](mailto:[email protected]): Advanced Research in Cyber Systems, Los Alamos National Laboratory

- [Juston S. Moore](mailto:[email protected]): Advanced Research in Cyber Systems, Los Alamos National Laboratory

- [Erik Skau](mailto:[email protected]): Computer, Computational, and Statistical Sciences Division, Los Alamos National Laboratory

- [Manish Bhattarai](mailto:[email protected]): Theoretical Division, Los Alamos National Laboratory

- [Gopinath Chennupati](mailto:[email protected]): Computer, Computational, and Statistical Sciences Division, Los Alamos National Laboratory

- [Boian S. Alexandrov](mailto:[email protected]): Theoretical Division, Los Alamos National Laboratory

## Copyright Notice

>© 2021. Triad National Security, LLC. All rights reserved.

This program was produced under U.S. Government contract 89233218CNA000001 for Los Alamos

National Laboratory (LANL), which is operated by Triad National Security, LLC for the U.S.

Department of Energy/National Nuclear Security Administration. All rights in the program are

reserved by Triad National Security, LLC, and the U.S. Department of Energy/National Nuclear

Security Administration. The Government is granted for itself and others acting on its behalf a

nonexclusive, paid-up, irrevocable worldwide license in this material to reproduce, prepare

derivative works, distribute copies to the public, perform publicly and display publicly, and to permit

others to do so.

**LANL C Number: C21028**

## License:

This program is open source under the BSD-3 License.

Redistribution and use in source and binary forms, with or without modification, are permitted

provided that the following conditions are met:

1. Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer.

2. Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution.

3. Neither the name of the copyright holder nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission.

THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS

IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE

IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR

PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR

CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,

EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,

PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS;

OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY,

WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR

OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF

ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.

## Developer Test Suite

Developer test suites are located under [```tests/```](tests/) directory. Tests can be ran from this folder using ```python -m unittest *```.

## Acknowledgments

We thank Austin Thresher for the valuable feedback on our software design.

## References

[1] 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.

[2] 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.

[3] 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.

[4] 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.

[5] M. Turcotte, A. Kent and C. Hash, “Unified Host and Network Data Set”, in Data Science for Cyber-Security. November 2018, 1-22.

[6] 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.