ViewFuture / seq2tens

Seq2Tens: An efficient representation of sequences by low-rank tensor projections

Seq2Tens: An efficient representation of sequences by low-rank tensor projections

This repository contains supplementary code to the paper https://arxiv.org/abs/2006.07027.

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Disclaimer

The present code is what was used at the time of submission of the paper, therefore at the moment is not intended to be very user-friendly. A cleaned up and streamlined version of the code with many extra features is in the making and soon will be released, so stay tuned!

Installing

To get started, you should first clone the repository using git, e.g. with the command

git clone https://github.com/tgcsaba/seq2tens.git

and then create and activate virtual environment with Python <= 3.7

conda create -n env_name python=3.7
conda activate env_name

Then, install the requirements using pip by

pip install -r requirements.txt

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Download datasets

The tsc directory contains the appropriate scripts used to run the tsc experiments in the paper. The datasets can be downloaded from our dropbox folder using the download_data.sh script in the ./tsc/datasets folder by running

cd tsc
bash ./datasets/download_data.sh

or manually by copy-pasting the dropbox url containd within the aforementioned script.

Support

We encourage the use of this code for applications, and we aim to provide support in as many cases as possible. For further assistance or to tell us about your project, please send an email to

[e-mail] / [e-mail] / [e-mail].