Code for reproducing the key results of our NIPS 2015 paper on semi-supervised low-rank logistic regression models for large functional neuroimaging datasets.

Bzdok D, Eickenberg M, Grisel O, Thirion B, Varoquaux G. Semi-supervised Factored Logistic Regression for High-Dimensional Neuroimaging Data Advances in Neural Information Processing Systems (NIPS 2015), Montreal. Paper on ResearchGate

Please cite this paper when using the code for your research.

To follow established conventions of scikit-learn estimators, the SSEncoder class exposes the functions fit(), predict(), and score(). This should allow for seamless integration into other scikit-learn-enabled machine-learning pipelines.

For questions and bug reports, please send me an e-mail at danilobzdok[at]gmail.com.

1. Development setup from a directory above this repository's clone, assuming a clean installation in Ubuntu:

   sudo apt install python3-dev
   sudo apt install python3-tk
   sudo apt install virtualenv
   virtualenv nips2015 -p python3
   cd nips2015/
   source bin/activate
   pip install scikit-learn numpy scipy nibabel nilearn theano matplotlib joblib pandas
   python download_data.py
   python nips3mm.py
   

   Now the files are ready to be executed!