https://github.com/antixk/sgmrfmix
A Python library for sGMRFmix model for anomaly detection in time series
https://github.com/antixk/sgmrfmix
anomaly-detection cpp graphical-lasso machine-learning python3 sgmrfmix sparse-modeling sparse-representations time-series variational-bayes
Last synced: 12 months ago
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A Python library for sGMRFmix model for anomaly detection in time series
- Host: GitHub
- URL: https://github.com/antixk/sgmrfmix
- Owner: AntixK
- License: mit
- Created: 2021-01-13T19:19:38.000Z (about 5 years ago)
- Default Branch: main
- Last Pushed: 2021-03-29T23:11:58.000Z (about 5 years ago)
- Last Synced: 2025-04-13T00:36:03.537Z (12 months ago)
- Topics: anomaly-detection, cpp, graphical-lasso, machine-learning, python3, sgmrfmix, sparse-modeling, sparse-representations, time-series, variational-bayes
- Language: Jupyter Notebook
- Homepage:
- Size: 11.8 MB
- Stars: 4
- Watchers: 3
- Forks: 2
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE.md
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README
# sGMRFmix
[](https://travis-ci.com/AntixK/sGMRFmix)
Python library for sGMRFmix model for anomaly detection in time-series data.
sGMRFmix is short for sparse mixture of Gaussian Markov Random Fields.
This is essentially a C++ (and python) port of the R package [`sGMRFmix`](https://cran.r-project.org/web/packages/sGMRFmix/index.html) to make it run faster for larger datasets.
## Model Overview
sGMRFmix is a mixture of GMRFs that predict the likelihood of a random variable using the variables in its markov blanket. Lower the log likelihood, higher the anomaly score. The markov blanket is estimated using a Gaussian graphical model with constraint that enforces sparsity in the inverse covariance matrices of the mixture of GMRF model. This can be done in a stright-forward manner using Graphical LASSO model. You can check out the [paper](https://ide-research.net/papers/2016_ICDM_Ide.pdf) for further details and the math.
## Performance Comparison
The follwing plot shows the performance comparison with the only [`sGMRFmix`](https://cran.r-project.org/web/packages/sGMRFmix/index.html) library available publicaly in R language.
## Installation
### Requirements
- Python >= 3.6 (For Python Thread-Specific-Storage (TSS) API used by pybind11)
- Numpy >= 1.16.5
### Binaries
Check out the releases of this repo for wheels for various platforms.
Install the wheel using pip inside your python environment.
```
pip install sgmrfmix-.whl
```
### Build from source
For Linux
```
sudo apt-get update
sudo apt-get install libopenblas-dev liblapack-dev libarpack2-dev libsuperlu-dev
sudo apt install libarmadillo-dev libboost-all-dev build-essential
```
For Mac
```
brew install cmake pkg-config boost openblas
brew install armadillo
```
Clone the repository (including the pybind11 submodule) into a suitable directory
```
git clone --recursive git@github.com:AntixK/sGMRFmix.git
cd sGMRFmix
```
Build the C++ files
```
cd build
cmake ..
make
```
Install requirements and build the library.
Optionally create a python virtual environment to install the library.
```
cd ..
pip install -r requirements.txt
python setup.py install
pip install auditwheel
auditwheel show dist/sgmrfmix-0.1-cp37-cp37m-linux_x86_64.whl
auditwheel repair --plat linux_x86_64 dist/sgmrfmix-0.1-cp37-cp37m-linux_x86_64.whl
## Usage
```python
import numpy as np
from sgmrfmix import sGMRFmix
m = sGMRFmix(K = 5, rho=0.8)
train = np.genfromtxt('train.csv', delimiter=',', skip_header=True)[:, 1:]
test = np.genfromtxt('test.csv', delimiter=',', skip_header=True)[:, 1:]
m.fit(train)
m.show_model_params()
results = m.compute_anomaly(test)
```
Check out further examples in the `Examples/` folder.
## Acknowledgements
- T. Ide, A .Khandelwal, J .Kalagnanam, **Sparse Gaussian Markov Random Field Mixtures for Anomaly Detection**, IEEE 16th International Conference on Data Mining (ICDM), 2016, pp 955–960
- https://rdrr.io/cran/sGMRFmix/f/vignettes/sGMRFmix.Rmd
- https://cran.r-project.org/web/packages/sGMRFmix/vignettes/sGMRFmix.html
- https://github.com/cran/sGMRFmix
- https://github.com/JClavel/glassoFast