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Commit b10e9ed6 authored by Carlos Muniz's avatar Carlos Muniz
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STAN: Improve code for reproducibility

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README.md
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......@@ -59,14 +59,14 @@ python merlin.py
MERLIN3.1.m uses outdated functions to compute the mean and standard deviation: `nanmean` and `nanstd`. If you are using R2020b or higher, you will need to change these functions to `mean(T, 'omitnan');` and `std(T, 'omitnan');` to run the script. We only tested the script on Windows 11.
### Run `STAN and ALL BASELINES`
## Run `STAN and ALL BASELINES`
-------
To run STAN against the rest of the baselines we have included a configuration file to install the `utsadbench` library released as part of the paper:
> Ferdinand Rewicki, Joachim Denzler, and Julia Niebling. 2023. "Is It Worth It? Comparing Six Deep and Classical Methods for Unsupervised Anomaly Detection in Time Series" Applied Sciences 13, no. 3: 1778. https://doi.org/10.3390/app13031778
#### Install `UTSADBENCH`
### Install `UTSADBENCH`
We use [Anaconda](https://www.anaconda.com/download/) to create an updated environment to install and run `utsadbench`. We suggest following the script provided by us at `install_utsadbench.sh` to install all dependencies
......@@ -82,6 +82,7 @@ For building [MDI]((https://cvjena.github.io/libmaxdiv/)) algorithm, the followi
* [Eigen3](https://eigen.tuxfamily.org), tested with eigen3 version 3.4
* [pkg-config](https://www.freedesktop.org/wiki/Software/pkg-config/), tested with version 0.29.1
* [OpenMP](https://www.openmp.org/), tested with version 4.5
* [CUDA] Version: 12.2
Once the dependencies are installed, run `utsadbench`'s script to install `libmaxdiv` or run:
......@@ -91,11 +92,31 @@ chmod +x ./scripts/install_mdi.sh
./scripts/install_mdi.sh
```
#### Run `UTSADBENCH`
#### Potential Issues
> AttributeError: module 'numpy' has no attribute 'bool'. `np.bool` was a deprecated alias for the builtin `bool`.
To solve this issue, locate the file `is-it-worth-it-benchmark/utsadbench/results/classifier.py`, line 50, and change `np.bool` for `bool`.
### Run `UTSADBENCH`
## License
Please refer to the `LICENSE.md` file for further information about how the content is licensed.
\ No newline at end of file
To run the `utsadbench` benchmark, we follow their instructions. First, start the mlflow server.
```bash
mkdir -p out/mlflow
```
```bash
mlflow server --host 0.0.0.0 -p 5000 --backend-store-uri sqlite:///out/mlflow/mlflow.sqlite --default-artifact-root $PWD/out/mlflow/artifacts/ --gunicorn-opts "--timeout 180"
```
Once running, you can run the other baselines and STAN by running
```bash
python utsadbench/experiments/main.py +experiment=main/<model>
```
where `<model>` is one of `ae,ganf,mdi,merlin,rrcf,rrcf_sequences,tranad`, and also `stan` (our method).
More more details about hyperparameter tunning and additional experiments, check the [README.md](is-it-worth-it-benchmark/README.md)
## License
All our source code is under [Apache 2.0](LICENSE) license.
install_utsadbench.sh
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......@@ -2,14 +2,15 @@
wget https://gitlab.com/dlr-dw/is-it-worth-it-benchmark/-/archive/main/is-it-worth-it-benchmark-main.zip
unzip is-it-worth-it-benchmark-main.zip
mv is-it-worth-it-benchmark-main is-it-worth-it-benchmark
cp -rn ./is-it-worth-it-benchmark-main/* ./is-it-worth-it-benchmark/
rm -rf ./is-it-worth-it-benchmark-main
rm is-it-worth-it-benchmark-main.zip
conda env create -f requirements/utsadbench_requirements.yml # Create the conda environment
conda activate btw2025
pip install merlin@git+https://gitlab.com/dlr-dw/py-merlin.git
cd is-it-worth-it-benchmark-main
cd is-it-worth-it-benchmark
pip install -e . --no-deps # All dependencies are already installed in the conda environment
......
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