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https://github.com/alan-turing-institute/AnnotateChange

A simple flask application to collect annotations for the Turing Change Point Dataset, a benchmark dataset for change point detection algorithms
https://github.com/alan-turing-institute/AnnotateChange

bootstrap change-detection changepoint changepoint-detection d3-visualization d3js d3v5 flask flask-application flask-login flask-sqlalchemy open-science reproducible-research time-series time-series-analysis

Last synced: 8 days ago
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A simple flask application to collect annotations for the Turing Change Point Dataset, a benchmark dataset for change point detection algorithms

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README 

        
# AnnotateChange



Welcome to the repository of the "AnnotateChange" application. This 

application was created to collect annotations of time series data in order to 

construct the [Turing Change Point 

Dataset](https://github.com/alan-turing-institute/TCPD) (TCPD). The TCPD is a 

dataset of real-world time series used to evaluate change point detection 

algorithms. For the change point detection benchmark that was created using 

this dataset, see the [Turing Change Point Detection 

Benchmark](https://github.com/alan-turing-institute/TCPDBench) repository.



Any work that uses this repository should cite our paper: [**Van den Burg & 

Williams - An Evaluation of Change Point Detection Algorithms 

(2020)**](https://arxiv.org/abs/2003.06222). You can use the following BibTeX 

entry:



```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}

}

```



Here's a screenshot of what the application looks like during the annotation 

process:





screenshot of 
<br />AnnotateChange




Some of the features of AnnotateChange include:



* Admin panel to add/remove datasets, add/remove annotation tasks, add/remove 

  users, and inspect incoming annotations.



* Basic user management: authentication, email confirmation, forgotten 

  password, automatic log out after inactivity, etc. Users are only allowed to 

  register using an email address from an approved domain.



* Task assignment of time series to user is done on the fly, ensuring no user 

  ever annotates the same dataset twice, and prioritising datasets that are 

  close to a desired number of annotations.



* Interactive graph of a time series that supports pan and zoom, support for 

  multidimensional time series.



* Mandatory "demo" to onboard the user to change point annotation.



* Backup of annotations to the admin via email.



* Time series datasets are verified upon upload acccording to a strict schema.



## Getting Started



Below are instructions for setting up the application for local development 

and for running the application with Docker.



### Basic



AnnotateChange can be launched quickly for local development as follows:



1. Clone the repo

   ```

   $ git clone https://github.com/alan-turing-institute/AnnotateChange

   $ cd AnnotateChange

   ```



2. Set up a virtual environment and install dependencies (requires Python 

   3.7+)

   ```

   $ sudo apt-get install -y python3-venv # assuming Ubuntu

   $ pip install wheel

   $ python3 -m venv ./venv

   $ source ./venv/bin/activate

   $ pip install -r requirements.txt

   ```



3. Create local development environment file

   ```

   $ cp .env.example .env.development

   $ sed -i 's/DB_TYPE=mysql/DB_TYPE=sqlite3/g' .env.development

   ```

   With ``DB_TYPE=sqlite3``, we don't have to deal with MySQL locally.



4. Initialize the database (this will be a local ``app.db`` file).

   ```

   $ ./flask.sh db upgrade

   ```



5. Create the admin user account

   ```

   $ ./flask.sh admin add --auto-confirm-email

   ```

   The ``--auto-confirm-email`` flag automatically marks the email address of 

   the admin user as confirmed. This is mostly useful in development 

   environments when you don't have a mail address set up yet.



6. Run the application

   ```

   $ ./flask.sh run

   ```

   This should tell you where its running, probably ``localhost:5000``. You 

   should be able to log in with the admin account you've just created.



7. As admin, upload **ALL** demo datasets (included in [demo_data](./demo_data)) 

   through: Admin Panel -> Add dataset. You should then be able to follow the 

   introduction to the app (available from the landing page).



8. After completing the instruction, you then will be able to access the user 

   interface ("Home") to annotate your own time series.



### Docker



To use AnnotateChange locally using Docker, follow the steps below. For a 

full-fledged installation on a server, see the [deployment 

instructions](./docs/DEPLOYMENT.md).



0. Install [docker](https://docs.docker.com/get-docker/) and 

   [docker-compose](https://docs.docker.com/compose/install/).



1. Clone this repository and switch to it:

   ```

   $ git clone https://github.com/alan-turing-institute/AnnotateChange

   $ cd AnnotateChange

   ```



2. Build the docker image:

   ```

   $ docker build -t gjjvdburg/annotatechange .

   ```



3. Create the directory for persistent MySQL database storage:

   ```

   $ mkdir -p persist/{instance,mysql}

   $ sudo chown :1024 persist/instance

   $ chmod 775 persist/instance

   $ chmod g+s persist/instance

   ```



4. Copy the environment variables file:

   ```

   $ cp .env.example .env

   ```

   Some environment variables can be adjusted if needed. For example, 

   when moving to production, you'll need to change the `FLASK_ENV` variable 

   accordingly. Please also make sure to set a proper `SECRET_KEY` and 

   `AC_MYSQL_PASSWORD` (`= MYSQL_PASSWORD`). You'll also need to configure a 

   mail account so the application can send out emails for registration etc. 

   This is what the variables prefixed with ``MAIL_`` are for. The 

   ``ADMIN_EMAIL`` is likely your own email, it is used when the app 

   encounters an error and to send backups of the annotation records. You can 

   limit the email domains users can use with the ``USER_EMAIL_DOMAINS`` 

   variable. See the [config.py](config.py) file for more info on the 

   configuration options.



5. Create a local docker network for communiation between the AnnotateChange 

   app and the MySQL server:

   ```

   $ docker network create web

   ```



6. Launch the services with docker-compose

   ```

   $ docker-compose up

   ```

   You may need to wait 2 minutes here before the database is initialized.

   If all goes well, you should be able to point your browser to 

   ``localhost:7831`` and see the landing page of the application. Stop the 

   service before continuing to the next step (by pressing `Ctrl+C`).



7. Once you have the app running, you'll want to create an admin account so 

   you can upload datasets, manage tasks and users, and download annotation 

   results. This can be done using the following command:

   ```

   $ docker-compose run --entrypoint 'flask admin add --auto-confirm-email' annotatechange

   ```



8. As admin, upload **ALL** demo datasets (included in [demo_data](./demo_data)) 

   through: Admin Panel -> Add dataset. You should then be able to follow the 

   introduction to the app (available from the landing page).



9. After completing the instruction, you then will be able to access the user 

   interface ("Home") to annotate your own time series.



## Notes



This codebase is provided "as is". If you find any problems, please raise an 

issue [on GitHub](https://github.com/alan-turing-institute/annotatechange).



The code is licensed under the [MIT License](./LICENSE).



This code was written by [Gertjan van den Burg](https://gertjan.dev) with 

helpful comments provided by [Chris 

Williams](https://homepages.inf.ed.ac.uk/ckiw/).



## Some implementation details



Below are some thoughts that may help make sense of the codebase.



* AnnotateChange is a web application build on the Flask framework. See [this 

  excellent 

  tutorial](https://blog.miguelgrinberg.com/post/the-flask-mega-tutorial-part-i-hello-world) 

  for an introduction to Flask. The [flask.sh](./flask.sh) shell script loads 

  the appropriate environment variables and runs the application.



* The application handles user management and is centered around the idea of a 

  "task" which links a particular user to a particular time series to 

  annotate.



* An admin role is available, and the admin user can manually assign and 

  delete tasks as well as add/delete users, datasets, etc. The admin user is 

  created using the [cli](./app/cli.py) (see the Getting Started documentation 

  above).



* All datasets must adhere to a specific dataset schema (see 

  [utils/dataset_schema.json](app/utils/dataset_schema.json)). See the files 

  in [demo_data] for examples, as well as those in 

  [TCPD](https://github.com/alan-turing-institute/TCPD).



* Annotations are stored in the database using 0-based indexing. Tasks are 

  assigned on the fly when a user requests a time series to annotate (see 

  [utils/tasks.py](app/utils/tasks.py)).



* Users can only begin annotating when they have successfully passed the 

  introduction.



* Configuration of the app is done through environment variables, see the 

  [.env.example](.env.example) file for an example.



* Docker is used for deployment (see the deployment documentation in 

  [docs](docs)), and [Traefik](https://containo.us/traefik/) is used for SSL, 

  etc.



* The time series graph is plotted using [d3.js](https://d3js.org/).