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 to make it run faster for larger datasets.
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 for further details and the math.
The follwing plot shows the performance comparison with the only sGMRFmix library available publicaly in R language.
- Python >= 3.6 (For Python Thread-Specific-Storage (TSS) API used by pybind11)
- Numpy >= 1.16.5
Check out the releases of this repo for wheels for various platforms. Install the wheel using pip inside your python environment.
pip install sgmrfmix-<platform/other tags>.whl
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.
- 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