https://github.com/Jhryu30/AnomalyBERT

https://github.com/Jhryu30/AnomalyBERT

Last synced: 7 days ago
JSON representation

• Host: GitHub
• URL: https://github.com/Jhryu30/AnomalyBERT
• Owner: Jhryu30
• Created: 2022-12-14T07:30:15.000Z (almost 2 years ago)
• Default Branch: main
• Last Pushed: 2024-06-12T11:45:44.000Z (5 months ago)
• Last Synced: 2024-08-02T15:32:35.356Z (3 months ago)
• Language: Python
• Size: 3.7 MB
• Stars: 120
• Watchers: 2
• Forks: 18
• Open Issues: 6
• Metadata Files:
  • Readme: README.md

Awesome Lists containing this project

README

        
# 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](utils/DATA_PREPARATION.md), 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](https://drive.google.com/drive/folders/1PhMwdGsSnrQgs16DPgBPngwV6Fvliatd?usp=sharing), 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`.