daozhong/anomalybert

AnomalyBERT: Transformer-based Anomaly Detector

This is the code for Self-supervised Transformer for Time Series Anomaly Detection using Data Degradation Scheme.

Installation

Please clone our repository at path/to/repository/ and install the packages in requirements.txt. Before installing the packages, we recommend installing Python 3.8 and Pytorch 1.9 with CUDA.

git clone https://github.com/Jhryu30/AnomalyBERT.git path/to/repository/

conda create --name your_env_name python=3.8
conda activate your_env_name

pip install torch==1.9.0+cu111 -f https://download.pytorch.org/whl/torch_stable.html  # example CUDA setting
pip install -r requirements.txt

We use five public datasets, SMAP, MSL, SMD, SWaT, and WADI. Following the instruction in here, you can download and preprocess the datasets. After preprocessing, you need to edit your dataset directory in utils/config.py.

DATASET_DIR = 'path/to/dataset/processed/'

Demo

We release our trained models on SWaT/WADI/SMAP/MSL. You can download the files from here, and we recommend placing it in logs/best_checkpoints/ folder. Now you can run our demo code in demo.ipynb and see how AnomalyBERT works.

Training

We provide the training code for our model. For example, to train a model of 6-layer Transformer body on SMAP dataset, run:

python3 train.py --dataset=SMAP --n_layer=6

To train a model on MSL dataset with patch size of 2 and customized outlier synthesis probability, run:

python3 train.py --dataset=MSL --patch_size=2 --soft_replacing=0.5 --uniform_replacing=0.1 --peak_noising=0.1 \
--length_adjusting=0.1

You can use the default option for training each dataset, as we did in our paper.

python3 train.py --default_options=SMAP # or any dataset name in MSL/SMD/SWaT/WADI and subset of SMD; SMD0 ~ SMD27

If you want to customize the model and training settings, please check the options in train.py.

Anomaly score estimation and metric computation

To estimate anomaly scores of test data with the trained model, run the estimate.py code. For example, you can estimate anomaly scores of SMAP test set divided by channel with window sliding of 16.

python3 estimate.py --dataset=SMAP --model=logs/YYMMDDhhmmss_SMAP/model.pt --state_dict=logs/YYMMDDhhmmss_SMAP/state_dict.pt \
--window_sliding=16

Now you will obtain results (npy) file that contains the estimated anomaly scores. With the results file, you can compute F1-score with and without the point adjustment by running:

python3 compute_metrics.py --dataset=SMAP --result=logs/YYMMDDhhmmss_SMAP/state_dict_results.npy

If you want to customize the estimation or computation settings, please check the options in estimate.py and compute_metrics.py.