DOMI

Detecting Outlier Machine Instances through One Dimensional CNN Gaussian Mixture Variational AutoEncoder

DOMI is a VAE-based model which glues one Dimensional Convolution Neural Network and Gaussian Mixture Variational auto-encoder. It aims at detecting outlier machine instances and its core idea is to learn the normal patterns of multivariate time series and use the reconstruction probability to do outlier judgment. Moreover, for a detected outlier machine instance, DOMI provides interpretation based on reconstruction probability changes of univaraite time series.

Getting Started

Clone the repo

git clone https://github.com/Tsinghuasuya/DOMI_code

Get data from github and unzip

git lfs clone https://github.com/Tsinghuasuya/DOMI_dataset && cd DOMI_dataset && unzip publicDataset.zip  && cd  ../DOMI_code

Install dependencies (with python 3.6)

(virtualenv is recommended)

pip install -r requirements.txt

Run the code

cd code && python domi.py

If you want to change the default configuration, you can edit ExpConfig in config.py or overwrite the config in domi.py using command line args. For example:

python domi.py --noExp=2 --max_epoch=100 --initial_lr=0.0001 

Result

After running the programmings, you can get the output in the file directory that you set in the config. For each instance, you can get the total score and score of each univariate time series. All the results are in the folder {config.result_dir}/, with trained model in {config.result_dir}/DOMI_{noExp}.model, the output and config of DOMI in the folder {config.result_dir}/DOMI_{noExp}/, and the detailed detection results are in the folder DOMI_{noExp}/result_summay/. It's made up by the following parts:

• OutlierScores_metric.txt: score of each univariate time series for instance in the testing dataset.
• OutlierScores.txt: score for each instance in the testing dataset.
• MetricResult.txt: interpretation using the univariate time series of machine instances.
• PRF.txt: summary of the overall statistics, including expected score of each metric and F1-score, recall, precision.