Feature Relevance Explainers in Tabular Anomaly Detection

This repository contains the code for the experiments in the dissertation "Feature Relevance Explainers in Tabular Anomaly Detection".

Data

data contains the smaller data sets used and has downloading instructions for the larger data sets.

Training detectors

Parameter studies can be conducted using erp_param_search.py or cidds_param_search.py.

The best performing models and hyperparameters used in the experiments are available in outputs/models/.

Generating Feature Relevance Explanations

Trained models can be explained using erp_xai.py or cidds_xai.py.

Note: Additional setup is required for running SHAP with optimized reference data. To integrate the optimization procedure directly within kernel-SHAP, this implementation requires to manually override the shap/explainer/_kernel.py script within the SHAP package. For this, either override the contents of shap/explainer/_kernel.py entirely with the backup file provided in xai/backups/shap_kernel_backup.py or add the small segments marked with # NEWCODE within xai/backups/shap_kernel_backup.py in the original library file of shap/explainer/_kernel.py.

Running Explainer Evaluations

Correctness and completeness evaluations are part of erp_xai.py or cidds_xai.py. Consistency evaluations are in erp_rashomon_eval.py or cidds_rashomon_eval.py. Compactness evaluations are in erp_size_eval.py or cidds_size_eval.py. Continuity and contrastivity heatmaps are created through plotting/heatmap_plots_erp.py or plotting/heatmap_plots_cidds.py.