Installation

pip

The latest stable version of BPt can be found and installed through pip the python packaging system.

pip install brain-pred-toolbox

Github

The latest development version of BPt can also optionally be installed from github directly.

git clone https://github.com/sahahn/BPt
cd BPt
pip install .

Python version

This library is only tested on python versions 3.7+ so while 3.6 might work, for the most reliable performance please use higher versions of python!

Extra Libraries

BPt has a number of other optional requirements, then when installed allow using more default options. These are not added as required libraries for a few reasons, either to keep the number of dependencies down, or because sometimes installation of these libraries is non-trivial.

The different extension libraries can be downloaded with

pip install brain-pred-toolbox[extra]

Though note, some may not download properly via pip depending on your operating system.

Different extension libraries are listed below:

bp-neurotools

This is a library by the same maintainers as BPt. It is designed to be less ML specific, but still contains some useful utilites for neuroimaging ML. See sahahn/neurotools.

lightgbm

This is a library designed to perform extreme gradient boosting. It is offered under Models under reserved keys ‘light gbm’ and ‘lgbm’. See https://lightgbm.readthedocs.io/en/latest/Python-Intro.html if having trouble installing through pip.

nilearn

This is a library dedicated to doing ML for neuroimaging, if installed it allows use of BPt.extensions.SingleConnectivityMeasure.

python-docx

This library is required to use the save_file option of BPt.Dataset.summary() and BPt.util.save_docx_table(). It is used for saving tables in docx format.

xgboost

This is another library for performing extreme gradient boosting. It is offered under Models.

mvlearn

There is experimental support through the BPt extensions for using objects like CCA from the multi-vew learn library, mvlearn.

imblearn

There is experimental support for the use of some ensembles methods from library imblearn. These are ensembles that ensure bagging is done in a class-balanced manner.