# Turing Change Point Detection Benchmark Welcome to the repository for the Turing Change Point Detection Benchmark, a benchmark evaluation of change point detection algorithms developed at [The Alan Turing Institute](https://turing.ac.uk). This benchmark uses the time series from the [Turing Change Point Dataset](https://github.com/alan-turing-institute/TCPD) (TCPD). **Useful links:** - [Turing Change Point Detection Benchmark](https://github.com/alan-turing-institute/TCPDBench) - [Turing Change Point Dataset](https://github.com/alan-turing-institute/TCPD) - [An Evaluation of Change Point Detection Algorithms](https://arxiv.org/abs/2003.06222) by [Gertjan van den Burg](https://gertjan.dev) and [Chris Williams](https://homepages.inf.ed.ac.uk/ckiw/). ## Introduction Change point detection focuses on accurately detecting moments of abrupt change in the behavior of a time series. While many methods for change point detection exists, past research has paid little attention to the evaluation of existing algorithms on real-world data. This work introduces a benchmark study and a dataset ([TCPD](https://github.com/alan-turing-institute/TCPD)) that are explicitly designed for the evaluation of change point detection algorithms. We hope that our work becomes a proving ground for the evaluation and development of change point detection algorithms that work well in practice. This repository contains the code necessary to evaluate and analyze a significant number of change point detection algorithms on the TCPD, and serves to reproduce the work in [Van den Burg and Williams (2020)](https://arxiv.org/abs/2003.06222). Note that work based on either the dataset or this benchmark should cite that paper: ```bib @article{vandenburg2020evaluation, title={An Evaluation of Change Point Detection Algorithms}, author={{Van den Burg}, G. J. J. and Williams, C. K. I.}, journal={arXiv preprint arXiv:2003.06222}, year={2020} } ``` ## Getting Started This repository contains all the code to generate the results (tables/figures/constants) from the paper, as well as to reproduce the experiments entirely. You can either install the dependencies directly on your machine or use the provided Dockerfile (see below). If you don't use Docker, first clone this repository using: ``` $ git clone --recurse-submodules https://github.com/alan-turing-institute/TCPDBench ``` ### Generating Tables/Figures Generating the tables and figures from the paper is done through the scripts in ``analysis/scripts`` and can be run through the provided ``Makefile``. First make sure you have all requirements: ``` $ pip install -r ./analysis/requirements.txt ``` and then use make: ``` $ make results ``` The results will be placed in ``./analysis/output``. Note that to generate the figures a working LaTeX and ``latexmk`` installation is needed. ### Reproducing the experiments To fully reproduce the experiments, some additional steps are needed. Note that the Docker procedure outlined below automates this process substantially. First, obtain the [Turing Change Point Dataset](https://github.com/alan-turing-institute/TCPD) and follow the instructions provided there. Copy the dataset files to a ``datasets`` directory in this repository. To run all the tasks we use the [abed](https://github.com/GjjvdBurg/abed) command line tool. This allows us to define the experiments in a single configuration file (``abed_conf.py``) and makes it easy to keep track of which tasks still need to be run. Note that this repository contains all the result files, so it is not necessary to redo all the experiments. If you still wish to do so, the instructions are as follows: 1. Move the current result directory out of the way: ``` $ mv abed_results old_abed_results ``` 2. Install [abed](https://github.com/GjjvdBurg/abed). This requires an existing installation of openmpi, but otherwise should be a matter of running: ``` $ pip install abed ``` 3. Tell abed to rediscover all the tasks that need to be done: ``` $ abed reload_tasks ``` This will populate the ``abed_tasks.txt`` file and will automatically commit the updated file to the Git repository. You can show the number of tasks that need to be completed through: ``` $ abed status ``` 4. Initialize the virtual environments for Python and R, which installs all required dependencies: ``` $ make venvs ``` Note that this will also create an R virtual environment (using [RSimpleVenv](https://github.com/GjjvdBurg/RSimpleVenv), which ensures that the exact versions of the packages used in the experiments will be installed. 5. Run abed through ``mpiexec``, as follows: ``` $ mpiexec -np 4 abed local ``` This will run abed using 4 cores, which can of course be increased if desired. Note that a minimum of two cores is needed for abed to operate. Furthermore, you may want to run these experiments in parallel on a large number of cores, as the expected runtime is on the order of 21 days on a single core. ### Running the experiments with Docker If you like to use [Docker](https://www.docker.com/) to manage the environment and dependencies, you can do so easily with the provided Dockerfile. You can build the Docker image using: ``` $ docker build -t alan-turing-institute/tcpdbench github.com/alan-turing-institute/TCPDBench ``` You can then follow the same procedure as above but using the relevant docker commands to run them in the container. ## License The code in this repository is licensed under the MIT license, unless otherwise specified. See the [LICENSE file](LICENSE) for further details. Reuse of the code in this repository is allowed, but should cite [our paper](https://arxiv.org/abs/2003.06222). ## Notes If you find any problems or have a suggestion for improvement of this repository, please let us know as it will help us make this resource better for everyone. You can open an issue on [GitHub](https://github.com/alan-turing-institute/TCPDBench) or send an email to ``gvandenburg at turing dot ac dot uk``.