Full documentation for the project is available on Read the Docs

calpit is a Python package for diagnosing and recalibrating conditional density estimates. The package is built on top of Pytorch (with other ML backends to be added soon) and provides a simple and flexible interface matching the scikit-learn API.

The following is a basic recipe for using the calpit package:

   from calpit import CalPit #import the CalPit class
   
   calpit_model = CalPit(model=model) #Any Pytorch model CalPit class
   
   trained_model = calpit_model.fit(x_calib,y_calib, cde_cali,y_grid) #Fit the model with a calibration dataset
   
   pp_result = calpit_model.predict(x_test, cov_grid) #Predict the local PIT distribution for a test dataset
   
   new_cde = calpit_model.transform(x_test, cde_test, y_grid) #Recalibrate the conditional density estimate for a test dataset

To install the current release of the package, you can run the following command:

   pip install calpit

To install the latest version of the code from Github, you can run the following command:

  pip install git+https://github.com/lee-group-cmu/Cal-PIT

If you would like to install the package for development purposes, you can clone the repository and install the package in editable mode:

   >> git clone https://github.com/lee-group-cmu/Cal-PIT.git
   >> cd Cal-PIT
   >> pip install -e .