I'm very happy of announcing the second release of django-ai: Artificial Intelligence for Django.
The main exciting features of this version are Spam Filtering systems and Classification with Support Vector Machines ready to be plugged into any Django application.
This system uses the scikit-learn framework as engine and allows you to incorporate to any Django Model susceptible to spamming a self-updating filter capable of learning from labelled history to discern Spam content so you can act accordingly.
The classifier can be chosen and all the parameters in the process can be fine-tunned conveniently via the admin front-end. See the :ref:`example <example_spam_filtering>` and the :ref:`documentation <spam_filtering>` for more.
Plugging Spam Filters to your project has never been so easy!! :)
A new app is introduced: Supervised Learning, which provides Classification and Regression models ready to be plugged into django-ai systems or to be used stand-alone in your application where deemed necessary.
Support Vector Machines (SVM), one of the most understood and best performing classifier for high-dimensional data is featured in this app.
- Support for Django 2.0
I'm very happy to announce the first release of django-ai: Artificial Intelligence for Django!!
django-ai is a collection of apps for integrating statistical models into your Django project so you can implement machine learning conveniently.
It aims to integrate several libraries and engines providing your Django app with a set of tools so you can leverage your project functionality with the data generated within.
The integration is done through Django models - where most of the data is generated and stored in a Django project - and an API focused on integrating seamlessly within Django projects’ best practices and patterns.
The rationale of django-ai is to provide for each statistical model or technique bundled a front-end for configuration and an API for integrating it into your code.
Excited?
You are welcome to join the community of users and developers :)
- Bayesian Networks: Integrate Bayesian Networks through your models using the BayesPy framework.
- In development mode (
DEBUG = True) the BayesPy Inference Engine may stall during model estimation on certain states of the Pseudo Random Number Generator. You may need to reset the PRNG or deactivate and activate again your Python virtualenv. This does not affect other operations like cluster assigment.
- First release on PyPI.