This repository contains the code we used to test the model pipeline that we proposed in our CISBAT paper (September 2019).
Figure above shows the overview of the pipeline that we use in our paper. We first encode the
time-series into three-channel images where the channels are Markov Transition, Gramian
Angular Summation and Gramian Angular Difference Fields. Next, we train an undercomplete
autoencoder to learn the manifold of the underlying data-generating distribution. Finally,
we use ward-linking agglomerative clustering method to cluster the instances in the
latent space, based on the learned manifold.
We obtained centroids similar to those shown bellow:
We used a modified version of the Grad-CAM algorithm to visualize the
features that contributed the most to the clustering:
Create an Anaconda environment by running:
conda env create -f reqs.yml
Activate the scafe environment by executing:
conda activate scafe.
Finally, install pyts by executing
pip install pyts==0.7.5
Note that the data should be stored in a .csv file. To run the autoencoder
training and clustering with feature extraction (full pipeline) run:
python scafe.py --full --path ./path-to-your-data-csv/.
We strongly recommend to train the model only on machines that have CUDA support.