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Project description
Cal-PIT
Full Documentation
Full documentation for the project is available on Read the Docs
Overview
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.
Basic Usage
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
Installation
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 .
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