Metadata-Version: 2.1
Name: SI-QPOT
Version: 0.1.0
Summary: An implementation of SI-QPOT: Statistical Inference for Quadratic Programming-based Feature Selection after Optimal Transport-based Domain Adaptation
Home-page: https://github.com/DAIR-Group/SI-QPOT
Author: Nguyen Thang Loi
Author-email: 23520872@gm.uit.edu.vn
License: MIT
Classifier: License :: OSI Approved :: MIT License
Requires-Python: >=3.10
Description-Content-Type: text/markdown
License-File: LICENSE

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

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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:
```bash
$ 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
```
