This is the code we used for all the paper's experiments. The code follows scikit-learn's calibration structure, using the CalibratedClassifierCV class, though we handle the cross-validation ourselves, to ensure that all calibration methods have access to exactly the same calibration set.

There's a main file for each one of the base classifiers:

• Logistic Regression: main_lr.py
• Adaboost: main_boosting.py
• Naive Bayes: main_nb.py

All experiments can be parallelized using scoop, with instructions at the beginning of each main file.

• Numpy - NumPy is the fundamental package for scientific computing with Python.
• Scikit-learn - Machine Learning in Python.
• Scoop - Scalable COncurrent Operations in Python (optional).

MIT