SHAP (SHapley Additive exPlanations) is a mature Python library for interpreting machine learning model predictions using Shapley values from cooperative game theory. It provides a unified API across model types: tree ensembles (XGBoost, LightGBM, CatBoost, sklearn, PySpark), deep neural networks (TensorFlow, PyTorch), linear models, and arbitrary callables. The architecture is modular: explainers (Tree, Deep, Kernel, Partition, Gradient, Linear, etc.) consume models and maskers (tabular, image, text) to produce Explanation objects, which are visualized via matplotlib or interactive JavaScript force plots. The library includes benchmarks for evaluating explanation quality, extensive test suites with serialization baselines, and C++/CUDA extensions for performance. Although primarily a library (application), it has a strong research character: it originated from academic papers, includes notebooks reproducing experiments, and contains a user study on human interpretability. The codebase is well-engineered with reusable patterns (serialization, OpChain for lazy operations, MaskedModel abstraction), but carries some technical drift from legacy API aliases, dependency version sprawl, and a large minified JavaScript bundle in the repository.
OmniXAI is a comprehensive explainable AI (XAI) library providing a unified interface for generating explanations across multiple data types (tabular, vision, text, time series, ranking, vision-language) and ML models. It implements both model-agnostic (LIME, SHAP, L2X, MACE) and model-specific (Grad-CAM, Integrated Gradients, decision tree) explanation methods. The library features a modular architecture with separate modules for data handling, preprocessing, explanation generation, visualization (Dash-based dashboard), and deployment (BentoML integration). Key design patterns include base classes for explainers and explanations, factory patterns for explainer instantiation, and composable preprocessing pipelines. The system supports end-to-end workflows from data preprocessing to explanation visualization and model serving.
This repository is a curated collection of research papers (PDFs) organized by topic, including AI, LLMs, adversarial machine learning, data streams, explainable AI, and more. It does not contain any executable code, notebooks, or data pipelines. The checkpoint files suggest some Spark streaming was performed, but the primary value is the paper inventory. It serves as a reference library for researchers and practitioners. There is no software to evaluate for craftsmanship or reusability.