A PyTorch implementation of LSTM-based Encoder-Decoder for Multi-sensor Anomaly Detection
The project structure is based on the following Pytorch Project Template
├── agents
| └── rnn_autoencoder.py # the main training agent for the recurrent NN-based AE
├── graphs
| └── models
| | └── recurrent_autoencoder.py # recurrent NN-based AE model definition
| └── losses
| | └── MAELoss.py # contains the Mean Absolute Error (MAE) loss
| | └── MSELoss.py # contains the Mean Squared Error (MSE) loss
| | └── AUCLoss.py # under development (DO NOT USE!)
├── datasets # contains all dataloaders for the project
| └── ecg5000.py # dataloader for ECG5000 dataset
├── data
| └── ECG5000 # contains all ECG time series
├── utils # utilities folder containing metrics, checkpoints and arg parsing (configs).
| └── assets
| └── checkpoints.py
| └── config.py
| └── metrics.py
| └── create_config.py
| └── data_preparation.py
├── notebooks # Folder where adding your notebook
├── experiments # Folder where saving the results of your experiments
├── main.py
In the encoder each vector of a time-window
of length
is fed into a recurrent unit to perform the following computation:
In the decoder we reconstruct the time series in reverse order:
The ECG5000 dataset contains 5000 ElectroCardioGram (ECG) univariate time series of length . Each sequence corresponds to an heartbeat. Five classes are annotated, corresponding to the following labels: Normal (N), R-on-T Premature Ventricular Contraction (R-on-T PVC), Premature Ventricular Contraction (PVC), Supra-ventricular Premature or Ectopic Beat (SP or EB) and Unclassified Beat (UB). For each class we have the number of instances reported in the following Table:
| Class | #Instance |
|---|---|
| N | 2919 |
| R-on-T PVC | 1767 |
| PVC | 194 |
| SP or EB | 96 |
| UB | 24 |
Since the main task here is anomaly detection rather than classification, all istances which do not belong to class N have been merged in unique class which will be referred to as Anomalous (AN).
You can directly download the ECG5000 dataset from here or by running the script utils/data_preparation.py. This script allows performing data partitioning as well, i.e., splitting your data in training, validation and test set. For more details, run the following: python utils/data_preparation.py -h
Check requirements.txt.
- Before running the project, you need to add your configuration into the folder
configs/as found here. To this aim, you can just modify the scriptutils/create_config.pyand then running the followingpython utils/create_config.py. - Finally to run the project:
python main.py configs/config_rnn_ae.json