Making Parametric Anomaly Detection on Tabular Data Non-Parametric Again

Overview | Installation | Examples

Overview

This repo contains the code to run the experiments in "Making Parametric Anomaly Detection on Tabular Data Non-Parametric Again".

Installation

Set up and activate the Python environment by executing

conda env create -f environment.yml

Make sure to have the latest version of condas.

Datasets

To download all datasets at once, with wget:

bash get_dataset_wget.sh

with curl:

bash get_dataset_curl.sh

Examples

To run the experiments for each dataset, without retrieval, for cpu or mono-gpu:

source ./scripts/cpu/no_retrieval/abalone.sh

where abalone can be replaced by any dataset in the paper.

For distributed training, change the number of GPUs accordingly in ./scripts/distributed/no_retrieval/abalone.sh and run:

--nnodes=$NUMBER_OF_NODE --nproc_per_node=$NUMBER_OF_GPUS_PER_NODE

--mp_nodes $NUMBER_OF_NODE        #number of computing nodes
--mp_gpus $TOTAL_NUMBER_OF_GPUS   #total number of gpus

Similarly, for retrieval-augmented methods, replace no_retrieval in the previous path by the chosen retrieval method in ['knn', 'v-attention', 'attention_bsim', 'attention_bsim_bval']. For abalone and knnretrieval, run the following:

source ./scripts/cpu/knn/abalone.sh

or

source ./scripts/distributed/knn/abalone.sh

Citation

If you use this code for your work, please cite our paper Paper as

@inproceedings{thimonier2024making,
author = {Thimonier, Hugo and Popineau, Fabrice and Rimmel, Arpad and Doan, Bich-Li\^{e}n},
title = {Retrieval Augmented Deep Anomaly Detection for Tabular Data},
year = {2024},
isbn = {9798400704369},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3627673.3679559},
doi = {10.1145/3627673.3679559},
booktitle = {Proceedings of the 33rd ACM International Conference on Information and Knowledge Management},
pages = {2250–2259},
numpages = {10},
keywords = {anomaly detection, deep learning, tabular data},
location = {Boise, ID, USA},
series = {CIKM '24}
}