End-to-end deep representation learning for time series clustering

This is the code corresponding to the experiments conducted for the work "End-to-end deep representation learning for time series clustering: a comparative study" (Baptiste Lafabregue, Jonathan Weber, Pierre Gançarki & Germain Forestier), in submission

The results obtained for this paper on both archives are reported in the paper_results/ folder

Datasets

The dataset used for the paper are available at : http://www.timeseriesclassification.com/ Both the univariate and the multivariate archive can be used.

Usage

Install packages

You can use your favorite package manager (conda is recommended), create a new environment of python 3.8 or greater and use the packages listed in requirements.txt

pip install -r requirements.txt

For training on GPU

If you wish to train the networks on GPU you must install the tensorflow-gpu package. For example with pip:

pip install tensorflow-gpu

or

conda install tensorflow-gpu

Extract dataset to numpy format

First, to train the networks you need to convert them into .npy files. The CBF dataset is provided as an example. To do so you can use the utils.py script but you need first to change the two last line of the script here. Note that this script is suited to extract data from .sktime files

Train networks

To train networks and obtain the results you can use the following command:

python ucr_experiments.py --dataset <dataset_name> --itr <itreation_number> --architecture <network_architecture> --encoder_loss <enc> --clustering_loss IDEC --archives <archive_name> --nbneg <negative_exmaple_for_tiplet_loss> --hyper default_hyperparameters.json

Here is an example of the DRNN-rec-DEC combination on the CBF dataset

python ucr_experiments.py --dataset CBF --itr 1 --architecture dilated_cnn --encoder_loss reconstruction --clustering_loss DEC --archives UCRArchive_2018 --hyper default_hyperparameters.json

more informations are provided if you type directly python ucr_experiments.py in your prompt

Results

The results are stored in two folders:

• ae_weights/<itreation_number>/<combination_name>/ will contain the logs (performance/loss evolution) and the saved weights of both the pretrained model (without clustering loss) and the final model, for the final model it will also save the representation learned of the train and test test ,the clustering map and the centroids
• stats/<itreation_number> will contain the final statistics used to store the summarized stats used to evaluate methods. It will contain a .error file if an error occurred during the process

Other computations

Baseline can be computed with following script:

python ucr_baseline.py --dataset <dataset_name> --itr <itreation_number> --archives <archive_name>

To combine already computed representations (i.e. for triplet compbined) it can be done with the following script (example with DCNN architecture):

python merge_trainers.py --dataset <dataset_name> --itr <itreation_number> --archives <archive_name> --prefix "dilated_cnn_tripletK" --suffix  "_None_0.0"

To use a reduction method on top of an already computed representation you can use:

python alternate_clustering.py --dataset <dataset_name> --itr <itreation_number> --architecture <network_architecture> --encoder_loss <enc> --clustering_loss IDEC --archives <archive_name> --nbneg <negative_exmaple_for_tiplet_loss>

For this last option the argument should be the semae as the one used to first compute the model. Note that the arguments are used to find load saved .npy files in ae_weights folder, so they should not have been used or renamed.