This is an implementation of MIDAS - edge stream anomaly detection but implemented in Go.
For more information about how it works, please checkout the resources section.
Checkout the examples folder for detailed instructions
import (
"github.com/steve0hh/midas"
"fmt"
)
func main () {
src := []int{2,2,3,3,5,5,7,11,1,2}
dst := []int{3,3,4,4,9,9,73,74,75,76}
times := []int{1,1,2,2,2,2,2,2,2,2}
// using function to score the edges
midasAnormScore := midas.Midas(src, dst, times, 2, 769)
midasRAnormScore := midas.MidasR(src, dst, times, 2, 769, 0.6)
fmt.Println(midasAnormScore)
fmt.Println(midasRAnormScore)
// using sklearn FitPredict api for midas
m := midas.NewMidasModel(2, 769, 9460)
fmt.Println(m.FitPredict(2,3,1))
fmt.Println(m.FitPredict(2,3,1))
fmt.Println(m.FitPredict(3,4,2))
// using sklearn FitPredict api for midasR
mr := midas.NewMidasRModel(2, 769, 9460, 0.6)
fmt.Println(mr.FitPredict(2,3,1))
fmt.Println(mr.FitPredict(2,3,1))
fmt.Println(mr.FitPredict(3,4,2))
}- Orginal implementation of MIDAS in C++
- MIDAS: Microcluster-Based Detector of Anomalies in Edge Streams
If you use this code for your research, please consider citing the original paper.
@article{bhatia2019midas,
title={MIDAS: Microcluster-Based Detector of Anomalies in Edge Streams},
author={Bhatia, Siddharth and Hooi, Bryan and Yoon, Minji and Shin, Kijung and Faloutsos, Christos},
journal={arXiv preprint arXiv:1911.04464},
year={2019}
}
Everyone is encouraged to help improve this project. Here are a few ways you can help:
- Report bugs
- Fix bugs and submit pull requests
- Write, clarify, or fix documentation
- Suggest or add new features
- Godocs documentation
- Add sklearn/keras fit & predict API
- More examples and tests
- Make code more efficient