This is the implementation of ICDM 2020 paper Meta-AAD: Active Anomaly Detection with Deep Reinforcement Learning. We propose to learn a meta-policy with deep reinforcement learning to optimize the performance of active anomaly detection. Please refer the paper for more deteails.
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Cite this Work
If you find this project helpful, please cite
@inproceedings{DBLP:conf/icdm/ZhaLWH20, author = {Daochen Zha and Kwei{-}Herng Lai and Mingyang Wan and Xia Hu}, editor = {Claudia Plant and Haixun Wang and Alfredo Cuzzocrea and Carlo Zaniolo and Xindong Wu}, title = {Meta-AAD: Active Anomaly Detection with Deep Reinforcement Learning}, booktitle = {20th {IEEE} International Conference on Data Mining, {ICDM} 2020, Sorrento, Italy, November 17-20, 2020}, pages = {771--780}, publisher = {{IEEE}}, year = {2020}, url = {https://doi.org/10.1109/ICDM50108.2020.00086}, doi = {10.1109/ICDM50108.2020.00086}, timestamp = {Wed, 17 Feb 2021 11:24:58 +0100}, biburl = {https://dblp.org/rec/conf/icdm/ZhaLWH20.bib}, bibsource = {dblp computer science bibliography, https://dblp.org} }
Installation
Make sure you have python 3.5+ installed.
git clone https://github.com/daochenzha/Meta-AAD.git
cd Meta-AAD
pip install -r requirments.txt
pip install -e .
Training a Meta-Policy
Train a meta-policy with train.py. The important arguments are as follows.
--train: the datasets used for training, seperated by commas.--test: the datasets used for testing, seperated by commas.--num_timesteps: the number of training steps of reinforcement learning agents--log: where the log and models will be outputted
By default, the reinforcement learning training log will be saved in log/, the anomaly discovery curves will be saved in log/anomaly_curves, and the trained model will be saved in log/.
Evaluating a Trained Model
You may evaluate a trained model with evaluate.py. The important arguments are as follows.
--load: the path tomodel.zipfile.--test: the datasets used for testing, seperated by commas.
Baselines
We provide two baselines in this repo for comparison: a random query strategy and IForest query strategy. They are available in evaluate_baselines.py. For other baselines, please refer to the following repos.