Shogun Machine Learning Toolbox

Mentors: Student: Michele Mazzoni

<p>Shogun implements dozens of machine learning and related algorithms, however many of them are not properly benchmarked and the library suffer from a lack of documentation and examples. The project objective is to benchmark basic machine learning algorithms against other libraries and rewrite their code where needed, extend their documentation and unit test coverage and provide more real world examples.</p>

Mentors: Student: Giovanni De Toni

<p>The Shogun Toolbox is a well-established machine learning project that provides efficient algorithms implementations that can be used in a wide range of applications and with multi-language support (thanks to SWIG magic). Unfortunately, since it was built by many hands for many years, its code has become not easily maintainable or extendable and it does not use many new programming techniques and components that have appeared since the Shogun foundation. The time has come to blow some fresh air (and some new fresh code) into Shogun's depths. This project aims to correct and update the codebase and to complete the integration of many new features that will make it more modular and easy to use. My efforts will be focused on: integrate the new Tags and serialization frameworks, substitute old-style macros with brand new C++11 smart pointers, enable premature stopping of ML algorithm and, last but not least, implement a useful (and beautiful) progress bar to show a visual representation of algorithm’s execution.</p>

Mentors: Student: Olivier Nguyen

<p>For GSoC2017, I intend to use the Shogun library on health data and show the usefulness of machine learning in applications that could save people's lives and benefit society. More specifically, I want to focus on analyzing health data for applications such as clinical decision support and mortality prediction. The dataset I will work with is the MIMIC database, which is comprised of information relating to patients admitted to the ICU at a large hospital. The data mainly includes demographic, administrative, and clinical data from over 45,000 critical care patients. The project will be divided into two parts: In the first part, I plan to perform data cleaning and apply various machine learning algorithms on the MIMIC dataset for mortality prediction, predict the risk of developing certain diseases, determine the effectiveness of certain drugs, and more. In the final part, I will explore more novel methods like LSTMs to exploit the time-series data. Recent research has shown good results of using deep learning on electronic health records.</p>

Mentors: Student: weijie Lin

<p>Every line of code in SHOGUN has a long history and have gone through many brains and hands. This made SHOGUN what it is today: a powerful toolbox with a lot of features. But most of the code has been written by researchers for their studies. Usually the focus is on "getting things done", proving awesome ideas and optimize them "as fast as possible". As a drawback, people didn't care too much about software engineering aspects. In addition, lots of new technologies have shown up since some parts of the code have been written, which allows us to do even cooler things with less code now. I expect to improve the maintainability, stability and beauty of Shogun by using C++1x feature and add more unit tests for it.</p>

Mentors: Student: Sanuj Sharma

<p>Shogun is an amazing machine learning toolbox with good set of algorithms available. Since the project is old, the code base is huge and it's old. A major refactor of code is required to make the development process better and improve the user experience. This can be done by making Shogun support plugins which requires all of the code to be migrated to the plugin framework. I also aim to develop a new parameter framework to improve the syntax of the API which is exposed to the user via SWIG. This would also make installing Shogun much easier (which is an important priority of Shogun community) as base library will be small and most of the features will be enabled by installing plugins for the base library with their own dependencies.</p>