SI-QPOT 0.1.0

An implementation of SI-QPOT: Statistical Inference for Quadratic Programming-based Feature Selection after Optimal Transport-based Domain Adaptation

SI-QPOT: Statistical Inference for Quadratic Programming-based Feature Selection after Optimal Transport-based Domain Adaptation

SI-QPOT is a Python package that implements a selective inference (SI) framework for conducting valid statistical inference after QP-based Feature Selection algorithms in the presence of domain adaptation (DA). The main idea is to leverages the SI framework and employs a divide-and conquer approach to efficiently compute the $p$ -value. Our proposed methods provides valid $p$-value for FS-DA results, by keeping the false positive rate (FPR) under control, while also maximizing the true positive rate (TPR), i.e., lowering the false negative rate (FNR).

Requirements

This package has the following requirements:

cvxpy
mpmath
numpy
POT
scikit-learn
scipy

Installation

Package Installation

This package can be installed using pip:

$ pip install si_qpot

Usage

We provide several Jupyter notebooks demonstrating how to use the stand-da package in action.

• Examples for conducting inference for QP-based Feature Selection after OT-based DA

>> ex1_feature_selection_after_DA.ipynb

• Check the uniformity of the pivot

>> ex2_validity_of_p_value.ipynb