Simple RNN autoencoder example in PyTorch. Can be used as anomaly detection for timeline data.
These instructions will get you a copy of the project up and running on your local machine for development and testing purposes. See deployment for notes on how to deploy the project on a live system.
Use python 3.x and libraries from requirements.txt file.
virtualenv --python /usr/bin/python3 venv
. ./venv/bin/activate
pip install -r requirements.txt
Clone NAB git repo with datasets:
git clone https://github.com/numenta/NAB.git
And then start training:
python -m autoencoder.rnntrainer \
--train_file NAB/data/artificialNoAnomaly/art_daily_small_noise.csv \
--test_file NAB/data/artificialWithAnomaly/art_daily_jumpsup.csv
See rnntrainer.py file for more options and default values.
- Petr Masopust - Initial work - EHP
This project is licensed under the MIT License - see the LICENSE file for details.