Instant Wild data analysis

This is the code that computes and gives badges to the PYBOSSA project Instant Wild.

The code is run when a task is completed. When a task is completed, the webhooks solution is triggered and the statistical analysis computed. By default each task is analyzed by 5 different people. When this happens, the following schema is followed:

1. If the 5 first answers say that there's no animal in the picture, the task is left as completed. Else,
   1. We reset the state of the task to ongoing asking one extra person to contribute to the same picture until there are 10 contributions agreeing that there is an animal, and the animal is the same (i.e. an elephant) or if we got 25 answers with different solutions/options.
   2. If we have 10 people agreeing on a picture, the system gives "badges" and karma to users:
      1. If the user answered correctly, then, the karma is increased in one, else it's decreased by one (karma cannot be lower than zero).
      2. If the identified animal is an Endangered species, then, the iucn_red_list number is increased as well to reflect it.

That's all! The projec has set of tests (check the tests_analysis.py file) for checking all the available options. The next sections explain how the PYBOSSA webhooks solution works.

This project uses the PYBOSSA webhooks analysis tool

This very simple web module shows how you can easily analyze your PyBossa project in real-time.

PyBossa supports webhooks, notifying via an HTTP POST request the task that has been completed by the volunteers or users. The POST sends basically the following data:

{'fired_at':,
 'project_short_name': 'project-slug',
 'project_id': 1,
 'task_id': 1,
 'result_id': 1,
 'event': 'task_completed'} 

The PyBossa server sends all the required information to analyze the results of the contributions of the volunteers for a given task using Enki.

While the main purpose of this project is to do the analysis of the results, you can customize and fork this project to do many more things like:

• Post to Twitter that your project has completed a task.
• Upload the results to your DropBox folder by writing the results in a file.
• etc.

In this specific version, the analysis module only shows how you can easily get the most voted option for an image pattern recognition project.

Installation

To install the project all you need is run the following command (we recommend you to use a virtual environment):

pip install -r requirements.txt

Now, copy the settings.py.template file to: settings.py and fill in the information. Once you are done with this file, you'll be ready to run the server.

NOTE: Be sure to have a PyBossa API-KEY as the analysis will be stored in the PyBossa results table.

NOTE: It requires a PyBossa server >= 1.2.0.

Running the server

Now that you've the required libraries installed, running the server is as simple as this:

python app.py

Configuring background jobs

By default, this project has disabled the creation of queues in your system. If you expect to have lots of contributions in your project, we recommend you to enable them.

To support queues you will need to install in your machine a Redis server. Then, change the flag: enable_background_jobs to True in your settings.py file, and restart the server.

Note: if you are already running a Redis server and queues, you can customize the name of your queue in the settings file. Check out the config variable: queue_name.

Running the background jobs

Now that you have the project running background jobs, you need to process them. This is very simple, in another terminal run the following command:

rqworker mywebhooks

NOTE: If you've changed the name of the queue, please, update the previous command with your new queue name. That's all! Enjoy!!!

LICENSE

See COPYING file.