The code provided enable you to run both C-mix and CURE models in high dimension. You may be interested if you face a supervised problem with temporal labels and you want to predict relative risks.
This implementation is used in the paper "C-mix: a high dimensional mixture model for censored durations, with applications to genetic data" published in SMMR journal (Statistical Methods in Medical Research) and available here.
You must have python >= 3.6 In order to install you must have the required Python dependencies:
pip install -r requirements.txt
The library can be tested simply by running
python -m unittest discover -v . "*tests.py"
in terminal. This shall check that everything is working and in order...
To use the package outside the build directory, the build path should be added to the PYTHONPATH environment variable, as such (replace $PWD with the full path to the build directory if necessary):
export PYTHONPATH=$PYTHONPATH:$PWD
For a permanent installation, this should be put in your shell setup script. To do so, you can run this from the tick directory:
echo 'export PYTHONPATH=$PYTHONPATH:'$PWD >> ~/.bashrc
Replace .bashrc with the variant for your shell (e.g. .tcshrc, .zshrc, .cshrc etc.).
You should definitely try the notebook "C-mix tutorial". It gives very useful example of how to use the model based on simulated data. It will be very simple then to adapt it to your own data.