BARBE

This repository contains the code for "Explaining Decisions of Black-box Models using BARBE" paper [1]. Black-box Assocaition Rule-Based Explanation (BARBE) is a model-independent framework that can explain the decisions of any black-box classifier for tabular datasets with high-precision.

We rely on the rules provided by SigDirect [1'] (the associative classifier we leveraged in BARBE) to explain each decision of a black-box model.

The repository stores local versions of existing packages LIME, VAE-LIME, S-LIME, LORE, and Anchor for the purposes of conducting experiments. The directory barbe contains all code for BARBE including experiments used in faithful evaluations [2]. Alongside a still in-development front-end (BARBiE).

The organization of the barbe directory

• dataset - directory containing each dataset (cleaned and unclean) used in different experiments
• experiments - directory containing experiments for [2] and [3] along with helper code for averaging results. Contains subdirectories: Results (raw result tables) and SummarizedResults (tables of averaged results)
• poster - directory containing all posters presented related to BARBE
• pretrained - directory containing pre-trained models from each article for reproducibility, loaded using the dill package
• styles - directory containing style objects used by implementation of front-end application built using the shiny package
• tests - directory containing code for various tests for BARBE and front-end wrapper functions used for other explainers
• utils - general directory of utilies applied in BARBE and experiments
  • {anchor, fieap, lime, lore}_interface.py - wrappers for explainer models that have various data input formats and model formats, used to homogenize their input and output
  • bbmodel_interface.py - wrapper for black-box model to check compatability of scikit or torch based models ensuring that the data input/output formats from BARBE are consistent with the black-box
  • dummy_interfaces.py - dummy explainer that outputs default baseline explanations to compare to
  • experiment_datasets.py - utility for loading datasets for experiments
  • sigdirect_interface.py - wrapper for sigdirect to transform data into sigdirect's expected formats of input/output and handle broad hyperparameters used in BARBE
  • simulation_dataset.py - utility for producing simulated data for diverse experiments on BARBE and other explainers
  • visualizer_javascript.js - javascript code used by visualizer to handle unique reactive elements
  • visualizer_utility.py - utilities to get required details from data, black-boxes, and the explainer not handled directly by the visualizer
• discretizer.py - defines the object CategoricalEncoder used in many instances to handle encoding categorical values into one-hot or ordinal values for BARBE and wrapped explainers
• explainer.py - defines the object BARBE that takes data and learns local explanations orchistrating each other utility
• perturber.py - defines the objects BarbePerturber and ClassBalancedPerturbed which produce perturbations of an input piece of data output in the pandas DataFrame format
• visualizer.py - contains code for running the visual front-end for BARBE using shiny

Work in this Repository

[1 - Link to BARBE Paper]

[2 - Link to Faithful Evaluations] Designed to provide true performance comparisons by weighing fidelity of a surrogate against the black-box based on the distance (k-Nearest Neighbors and Euclidean distance) of samples from the explained sample. Also included improvements to BARBE to produce perturbations more faithful to the original data to improve training.

[1' - Link to SigDirect paper]