path: root/README.md

# 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:

<p align="center">
<img height="500px" src="./annotatechange_wide.png" alt="screenshot of 
AnnotateChange" />
</p>

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/).