DeepHYDRA

This is the official implementation of the DeepHYDRA algorithm presented in the paper "DeepHYDRA: Resource-Efficient Time-Series Anomaly Detection in Dynamically-Configured Systems".

Creation of the conda Environments

The folder envs/ contains the conda environments for the different models. You can create the conda environments with the following command:

conda create --name <env_name> --file <file>  

In the respective conda environments, install the Python packages not installed via conda with

pip install -r <env_name>_python_requirements.txt  

This is a bit messy, and we will probably streamline this in the future.

Downloading/Creating the Datasets

For the machine-1-1 dataset, extract the files in the archive datasets/smd/machine-1-1.tar.gz.

For the HLT datasets, retrieve the original datasets from here and place them in the subfolder datasets/hlt. Afterwards, run the scripts

generate_hlt_datasets.py  
generate_combined_detection_test_set.py  

Use the conda environment contained in envs/dataset_generation.txt for this step.

Running the One-Liners

To run the one-liner baselines, run the script

run_one_liners.sh  

in the subfolder baselines/one-liners.

Running MERLIN

To run the MERLIN scripts, you have to clone the py-merlin repository. Build and install this package inside the environment contained in envs/merlin.txt. Afterwards, you waill be able to run the script

run_merlin.sh  

in the subfolder baselines/merlin.

Running the Models on machine-1-1 and the Reduced HLT Datasets

Use the respectively named conda environments envs/informers.txt and envs/tranad.txt to run the specific models. You can run the models with the parameters used in the paper by executing the scripts

run_smd.sh  
run_hlt.sh  
run_hlt_unaugmented.sh  

Contained in the subfolders transformer_based_detection/informers and transformer_based_detection/tranad.

Running the Combined Detection

After training the models on the reduced HLT data, you can run the combined detection method using the generated checkpoints. To do this, run the scripts

run_informers_combined.sh  
run_tranad_combined.sh  

contained in the subfolders detection_combined/benchmark/informers and detection_combined/benchmark/tranad.

Calculating the Performance Metrics and Generating the Plots

Running all of the scripts described above should have populated the folders evaluation/combined_detection/predictions, evaluation/reduced_detection/predictions, and evaluation/smd/predictions with the necessary files to calculate the performance metrics and generate the comparison plots shown in the paper. You can calculate the metrics by running the scripts

get_results_over_random_seeds.sh  

in the subfolders evaluation/combined_detection, evaluation/reduced_detection, and evaluation/smd/. Note that the results for TranAD on the machine-1-1 dataset are stored directly in the model folder as results_tranad_machine-1-1.csv.

The plots can be generated by running

plot_comparison_plots.py  

in the subfolder evaluation/combined/detection.