This repository contains the implementation of the methods proposed in the paper "Why Here and Not There?" -- Diverse Contrasting Explanations of Dimensionality Reduction by André Artelt, Alexander Schulz and Barbara Hammer.
The experiments as described in the paper are implemented in the folder Implementation.
Dimensionality reduction is a popular preprocessing and a widely used tool in data mining. Transparency, which is usually achieved by means of explanations, is nowadays a widely accepted and crucial requirement of machine learning based systems like classifiers and recommender systems. However, transparency of dimensionality reduction and other data mining tools have not been considered much yet, still it is crucial to understand their behavior -- in particular practitioners might want to understand why a specific sample got mapped to a specific location.
In order to (locally) understand the behavior of a given dimensionality reduction method, we introduce the abstract concept of contrasting explanations for dimensionality reduction, and apply a realization of this concept to the specific application of explaining two dimensional data visualization.
The shell script run_experiments.sh runs all experiments.
The implementation of the proposed algorithms for computing diverse counterfactual explanations can be found in the Implementation folder: linear_dr.py, som_dr.py, ae_dr.py and tsne_dr.py.
The baseline approach is implemented in memory_counterfactual.py.
We use the implementation (see Implementation/parametric_tSNE) from https://github.com/jsilter/parametric_tsne
- Python3.6
- Packages as listed in
Implementation/REQUIREMENTS.txt
MIT license - See LICENSE
You can cite the version on arXiv -- paper got accepted at ICPRAM-2023