FilterNet: A many-to-many deep learning architecture for time series classification

This repository contains code to reproduce the results and figures in the paper: FilterNet: A many-to-many deep learning architecture for time series classification.

Setup

The easiest way to run this software is via the Anaconda Python distribution.

Install Anaconda
Run conda env create -f environment.yaml
Enable the filternet environment, like, source activate filternet
Install filternet so it is importable, by running pip install -e . in the same directory as setup.py

In the root dir of this repo:

pytest tests

This will be really slow the first time because it has to download and pre-process several large AR datasets.

Subsequent test runs will probably still be slow, but... less slow.

Run the scripts in the scripts/ directory. These are very long-running scripts that reproduce each experimental condition many times. You might want to set, e.g., NUM_REPEATS=1 if you don't need this level of reproducibility.
Run the notebooks to re-produce the figures. You might need to edit a few paths to specific models to match the filenames on your system, especially if you changed the NAME or NUM_REPEATS parameters.

Name		Name	Last commit message	Last commit date
Latest commit History 15 Commits
filternet		filternet
scripts		scripts
tests		tests
.gitignore		.gitignore
Filternet Paper - architectures.ipynb		Filternet Paper - architectures.ipynb
Filternet Paper - ensembles.ipynb		Filternet Paper - ensembles.ipynb
Filternet Paper - event metrics visualization.ipynb		Filternet Paper - event metrics visualization.ipynb
Filternet Paper - heat map examples to compare model results.ipynb		Filternet Paper - heat map examples to compare model results.ipynb
Filternet Paper - inference window length.ipynb		Filternet Paper - inference window length.ipynb
Filternet Paper - multimodal sensor fusion.ipynb		Filternet Paper - multimodal sensor fusion.ipynb
Filternet Paper - overall results summary.ipynb		Filternet Paper - overall results summary.ipynb
Filternet Paper - plot training history example.ipynb		Filternet Paper - plot training history example.ipynb
README.md		README.md
ensemble_effects.png		ensemble_effects.png
environment.yaml		environment.yaml
multimodal_sensor_fusion.png		multimodal_sensor_fusion.png
setup.py		setup.py
stripchart heatmaps.png		stripchart heatmaps.png
training_history.png		training_history.png
win_len_effects.png		win_len_effects.png