README.rst

Supervised MultiModal Integration Tool's Readme

This project aims to be an easy-to-use solution to run a prior benchmark on a dataset and evaluate mono- & multi-view algorithms capacity to classify it correctly.

Getting Started

SuMMIT has been designed and uses continuous integration for Linux platforms (ubuntu 18.04), but we try to keep it as compatible as possible with Mac and Windows.

Platform	Last positive test
Linux
Mac	1st of May, 2020
Windows	1st of May, 2020

Prerequisites

To be able to use this project, you'll need :

• Python 3

And the following python modules will be automatically installed :

• numpy, scipy,
• matplotlib - Used to plot results,
• sklearn - Used for the monoview classifiers,
• joblib - Used to compute on multiple threads,
• h5py - Used to generate HDF5 datasets on hard drive and use them to spare RAM,
• pickle - Used to store some results,
• pandas - Used to manipulate data efficiently,
• six -
• m2r - Used to generate documentation from the readme,
• docutils - Used to generate documentation,
• pyyaml - Used to read the config files,
• plotly - Used to generate interactive HTML visuals,
• tabulate - Used to generated the confusion matrix.

Installing

Once you cloned the project from the gitlab repository, you just have to use :

cd path/to/summit/
pip install -e .

In the summit directory to install SuMMIT and its dependencies.