autoqild.mi_estimators

The mi_estimators package offers various mutual information (MI) estimators designed for use in information leakage detection.

These estimators provide methods to evaluate MI between features and class labels using different modeling techniques.

Modules

auto_gluon_estimator	AutoGluon-based MI estimator that leverages automated machine learning (AutoML) to estimate MI with optimized hyperparameters.
gmm_mi_estimator	Gaussian Mixture Model-based MI estimator for evaluating mutual information using probabilistic clustering.
mi_base_class	Base class for all Mutual Information estimators.
mi_estimator_classification	Base class for classification-based MI estimators, providing a framework for estimating MI in supervised learning.
mine_estimator	Mutual Information Neural Estimator (MINE) that uses multiple deep learning architectures to estimate MI for classification tasks.
mine_estimator_mse	Modified MINE estimator that minimizes mean squared error (MSE) to provide more robust MI estimates.
neural_networks_torch	Neural Nwtowkr implementations for running the PC-softmax and Mine MI estimator.
pc_softmax_estimator	MI estimator that uses probability-corrected softmax functions to assess the information content in classification scenarios.
pytorch_utils	Utilities for running the PC-softmax and Mine MI estimator, like loss functions and optimizers.
tab_pfn_estimator	MI estimator integrating the TabPFN model, optimized for small tabular datasets with efficient MI estimation.