Using PIP

pip install sidekit

In a Virtual environment

First, be sure to have virtualenv installed.
You can find some documentation on the official website.

Create your virtual environment

virtualenv env

This will create a directory called env in the current directory.
If you want to specify a different python interpreter (for example to test you program with python 3),
you just have to use the -p option:

virtualenv -p /path/to/python3 env

Activate your environment

Each and every time you will want to work on your project, you will have to first activate your virtualenv:

. ./env/bin/activate

Your prompt should change and you should see the name of your virtualenv between (). In our case (env).

Dependencies

SIDEKIT requires the installation of the following tools.
  • Python
    SIDEKIT has been developed under Python >3.3

    • LINUX: python is natively available on most of LINUX distributions

    • OSX: natively available, you can install a different version of python via Homebrew

    • Windows: Python can be installed on Windows through PythonXY, WinPython or anaconda packages

  • To install other required Python packages use one of the following:

    • conda

    • pip

The following packages are required to use SIDEKIT.

  • matplotlib>=3.0.0

  • numpy>=1.15.2

  • pyparsing>=2.2.2

  • scipy>=1.1.0

  • six==1.11.0

  • h5py>=2.8.0

  • pandas>=0.23.4

  • pytorch>=1.0

  • torchvision>=0.2.1

Optional linkage

Those packages might be used by SIDEKIT if installed. To do so, just make sure they are installed on your machine. When importing, SIDEKIT will look for them and link if possible.

  • LibSVM: library dedicated to SVM classifiers. This library can be downloaded from
    the official website and easily compiled on all plat-forms
    Compile the library (libsvm.so.2 on UNIX/Linux and Mac platforms and libsvm.dll on windows)
    and create a link or copy this library in ./sidekit/libsvm/.